A dairy cow breeding system and method based on artificial intelligence

Abstract

The present invention relates to the field of animal husbandry technology, and in particular to a dairy cow breeding system and method based on artificial intelligence, the system comprising: a data acquisition module, a determination module, a judgment module, a screening module, an adjustment module, a calculation module, a prediction module and a correction module. The present invention can comprehensively monitor the physiological state of dairy cows by combining accurate real-time data acquisition with intelligent analysis. The system not only improves the diagnostic accuracy of the health status of dairy cows by integrating multiple parameters such as hair density, glossiness, body temperature and skin moisture, but also can dynamically adjust the feeding plan to optimize the reproduction rate and reduce health problems caused by improper feeding. In addition, the introduction of artificial intelligence models makes the prediction of the reproduction rate more accurate. By correcting the threshold and the reproduction coefficient, the problem of being unable to accurately and comprehensively reflect the reproduction status of dairy cow groups due to the unstable measurement of traditional sensors and the limitations of individual monitoring is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of animal husbandry, and in particular to a dairy cow breeding system and method based on artificial intelligence. Background Art

[0002] In modern agriculture, the health and reproduction management of dairy cows is crucial to improving production efficiency and ensuring animal welfare. With the advancement of science and technology, traditional dairy cow management methods can no longer meet the needs of efficient, accurate and real-time monitoring. Traditional methods usually rely on manual inspections and single physiological data monitoring, lack a comprehensive analysis of the overall condition of the dairy cow group, and are difficult to cope with complex environmental changes and individual differences. In order to improve the scientificity and accuracy of dairy cow health management, the introduction of artificial intelligence technology provides a new solution for the intelligentization of dairy cow reproduction systems.

[0003] The patent document with patent publication number CN118120651A discloses a real-time monitoring integrated system for the health and reproductive status of dairy cows, the system comprising: a cowshed, a retaining wall arranged on the top of the cowshed, a fence fixedly installed on the outside of the retaining wall and located above the cowshed, and a forage area arranged on one side of the retaining wall, characterized in that: a front leg pedal located on one side of the retaining wall and having a pressure sensor at the bottom is placed on the inner side of the top of the cowshed, an ultrasonic sensor located above the forage area is installed on the inner side of the retaining wall, and a signal processing unit located on one side of the ultrasonic sensor and electrically connected to the ultrasonic sensor and the pressure sensor respectively is installed on the inner side of the retaining wall. The real-time monitoring integrated system comprises a smart collar, a wireless signal transceiver I, a control processing unit, a data storage unit and a computer terminal, the smart collar is connected to the signal processing unit, the control processing unit is connected to a light controller and an AI camera, and the computer terminal is connected to a mobile phone terminal via a wireless network.

[0004] It can be seen that the real-time monitoring integrated system for the health and reproductive status of dairy cows to be detected has the following problems: the system uses a pressure sensor installed under the front leg pedal, but the standing posture and movement of the cows cause the measurement results to be unstable or the data cannot be accurately collected, especially when the cows are in frequent dynamic activities; the system tends to monitor individual dairy cows, and the correlation analysis between group health and behavioral trends is not explained, making it difficult to fully reflect the overall condition of the dairy cow group. Summary of the invention

[0005] To this end, the present invention provides an artificial intelligence-based dairy cow breeding system and method, which is used to overcome the problem in the prior art that the reproductive status of the dairy cow group cannot be accurately and comprehensively reflected due to the unstable measurement of traditional sensors and the limitations of individual monitoring through intelligent data analysis and real-time monitoring.

[0006] To achieve the above objectives, the present invention provides, on one hand, a dairy cow breeding system based on artificial intelligence, comprising:

[0007] A data collection module, for collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount;

[0008] A determination module, connected to the data acquisition module, for determining a number of first temporary cows according to the real-time hair density and the real-time hair glossiness;

[0009] a determination module, which is connected to the determination module and the data acquisition module respectively, and is used to determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow;

[0010] a screening module, which is connected to the determination module and the data acquisition module respectively, and is used to screen the second temporary cows according to the real-time hair glossiness within a preset screening time and a preset standard gloss fluctuation threshold to form a number of abnormal cows;

[0011] an adjustment module connected to the screening module, for adjusting the preset feed amount according to the number of the abnormal cows and the number of the first temporary cows;

[0012] A calculation module, connected to the screening module, for calculating the actual reproduction rate according to the number of abnormal cows and a preset reproduction coefficient;

[0013] a prediction module connected to the data acquisition module, for predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level and a preset artificial intelligence model;

[0014] A correction module is connected to the calculation module and the prediction module respectively, and is used to correct the preset standard gloss fluctuation threshold or the preset reproduction coefficient according to the actual reproduction rate and the ideal reproduction rate.

[0015] Furthermore, the calculation module includes:

[0016] A second ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the total number of the cows to be detected, to form a second ratio;

[0017] A reproduction rate calculation unit is connected to the second proportion calculation unit and is used to calculate the product of the second proportion and a preset reproduction coefficient to form an actual reproduction rate.

[0018] Furthermore, the correction module includes:

[0019] A correction calculation unit, used for calculating the relative deviation between the actual reproduction rate and the ideal reproduction rate;

[0020] a correction comparison unit, connected to the correction calculation unit, for comparing the relative deviation with a preset deviation threshold to form a correction comparison result;

[0021] A correction unit is used to correct the preset standard gloss fluctuation threshold or the preset propagation coefficient according to the correction comparison result.

[0022] Furthermore, the correction unit includes:

[0023] a difference calculation subunit, for calculating the difference between the relative deviation and the preset deviation threshold value to form a correction difference value when the correction comparison result is that the relative deviation is greater than the preset deviation threshold value;

[0024] A first correction subunit, connected to the difference calculation subunit, for correcting the preset standard gloss fluctuation threshold according to the relative deviation and a preset correction coefficient when the correction difference is greater than a preset standard deviation;

[0025] The second correction subunit is connected to the difference calculation subunit, and is used to correct the preset reproduction coefficient according to the relative deviation and the preset correction coefficient when the correction difference is less than or equal to the preset standard deviation.

[0026] Furthermore, the determining module includes:

[0027] A density comparison unit, used to compare the real-time hair density with a preset standard density to form a density comparison result;

[0028] A glossiness comparison unit, used to compare the real-time hair glossiness with a preset standard glossiness to form a glossiness comparison result;

[0029] A determination unit is connected to the density comparison unit and the gloss comparison unit respectively, and is used to determine a number of first temporary cows according to the density comparison result and the gloss comparison result.

[0030] Furthermore, the determining unit includes:

[0031] A first determination subunit is used to determine that the cow to be detected is a first temporary cow when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is less than the preset standard glossiness;

[0032] The second determination subunit is used to determine that the cow to be detected is the first temporary cow according to the comparison result of the number of the density comparison results and the number of preset standard results within a preset determination time period when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is greater than or equal to the preset standard glossiness.

[0033] Furthermore, the determination module includes:

[0034] a drawing unit, used for drawing a moisture change curve according to the real-time skin moisture level, and drawing a body temperature change curve according to the real-time body temperature;

[0035] a consistency calculation unit connected to the drawing unit, for calculating a correlation coefficient between the water content change curve and the body temperature change curve to form a change consistency;

[0036] An abnormality determination unit is connected to the consistency calculation unit and is used to determine that the first temporary cow is a second temporary cow when the change consistency is less than a preset standard consistency, so as to form a plurality of second temporary cows.

[0037] Furthermore, the screening module includes:

[0038] A gloss fluctuation calculation unit, used to calculate the standard deviation of the real-time hair glossiness to form a gloss fluctuation value;

[0039] A screening unit is connected to the gloss fluctuation calculation unit and is used to screen the second temporary cows whose gloss fluctuation values ​​are greater than a preset gloss fluctuation threshold value to form a number of abnormal cows.

[0040] Furthermore, the adjustment module includes:

[0041] A first ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the number of the first temporary cows to form a first ratio;

[0042] An adjustment unit is connected to the proportion calculation unit and is used to adjust the preset feed amount according to a relative deviation between the first proportion and a preset first proportion threshold value and a preset adjustment coefficient.

[0043] On the other hand, the present invention also provides a dairy cow breeding method based on artificial intelligence, comprising:

[0044] Collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount;

[0045] Determine a number of first temporary cows according to the real-time hair density and the real-time hair glossiness;

[0046] Determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow;

[0047] Determine a gloss fluctuation value according to the real-time hair gloss within a preset screening time, and screen the second temporary cows according to the gloss fluctuation value and a preset gloss fluctuation threshold to form a number of abnormal cows;

[0048] Calculating a first proportion according to the number of the abnormal cows and the number of the first temporary cows, and adjusting the preset feed amount according to a comparison result of the abnormal proportion and the standard proportion threshold until the first proportion is less than a preset first proportion threshold;

[0049] Calculating the actual reproduction rate according to the number of abnormal dairy cows and a preset reproduction coefficient;

[0050] predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level, and a preset artificial intelligence model;

[0051] The preset gloss fluctuation threshold or the preset reproduction coefficient is corrected according to the actual reproduction rate and the ideal reproduction rate.

[0052] Compared with the prior art, the beneficial effect of the present invention is that, through the combination of precise real-time data collection and intelligent analysis, the physiological state of dairy cows can be fully monitored, and reproductive management strategies can be timely identified and adjusted. The system not only improves the diagnostic accuracy of the health status of dairy cows by integrating multiple parameters such as hair density, glossiness, body temperature and skin moisture, but also can dynamically adjust the feeding plan to optimize the reproduction rate and reduce health problems caused by improper feeding. In addition, the introduction of artificial intelligence models makes the prediction of reproduction rate more accurate. By correcting the threshold and reproduction coefficient, the adaptability and accuracy of the system are continuously improved, effectively solving the problem of the inability to accurately and comprehensively reflect the reproductive status of dairy cow groups due to the unstable measurement of traditional sensors and the limitations of individual monitoring.

[0053] Furthermore, by combining the proportion of abnormal cows with the preset reproduction coefficient, the module can dynamically reflect the actual reproduction status of the cow herd and avoid misjudgment caused by a single parameter. In addition, this calculation method is flexible and can adjust the reproduction rate calculation results according to the size and characteristics of different cow herds, thereby improving the applicability of the monitoring system.

[0054] Furthermore, the correction module can dynamically adjust key parameters to ensure consistency between the system's preset standards and actual performance. Through real-time correction, the accuracy of reproductive prediction can be further improved, avoiding long-term deviation accumulation, thereby optimizing dairy cow health management and reproductive rate.

[0055] Furthermore, the correction unit can flexibly adjust key parameters according to the actual deviation, ensuring that the system can accurately control the conditions and standards for dairy cow reproduction. Through targeted adjustments to different correction conditions, the gloss fluctuation threshold can be optimized when the deviation is large, and the reproduction coefficient can be adjusted when the deviation is small, thereby maintaining the efficient operation of the system and improving the accuracy of dairy cow reproduction efficiency and health management, ensuring that the reproduction rate is always optimized under changing environmental conditions.

[0056] Furthermore, the determination module can accurately screen dairy cows through multi-dimensional hair health indicators (hair density and glossiness), thereby effectively judging the health status of dairy cows and providing a basis for subsequent breeding management.

[0057] Furthermore, by comprehensively comparing the real-time hair density and hair glossiness, the system can more accurately judge the health of the cow. Hair density and glossiness are important indicators of cow health. Low density and dull hair usually indicate malnutrition, disease or other health problems.

[0058] Furthermore, by comparing the consistency of water content changes and body temperature changes, cows that may have health abnormalities can be identified, avoiding misjudgment caused by relying solely on a single indicator, thereby improving the accuracy and timeliness of health monitoring.

[0059] Furthermore, the gloss fluctuation value, as an indicator reflecting the health status of dairy cows, can effectively determine whether there are abnormal changes in dairy cows, thereby helping managers to adjust feeding and care measures in a timely manner.

[0060] Furthermore, the adjustment module can automatically adjust the feed amount according to the change in the proportion of abnormal cows to ensure that the nutritional needs of the dairy cow group are properly met. By combining the proportion deviation and the preset threshold for adjustment, feed waste or unbalanced feeding can be effectively avoided.

[0061] Furthermore, through precise data collection and artificial intelligence analysis, the health status of dairy cows can be dynamically monitored, and feeding management can be adjusted based on real-time data to improve reproductive efficiency. By monitoring indicators such as hair gloss and density, health abnormalities can be discovered and adjusted in a timely manner, thereby optimizing the reproductive performance of dairy cows, improving the production efficiency of the entire dairy cow group, reducing resource waste and improving the sustainability of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the artificial intelligence-based dairy cow breeding system of this embodiment;

[0063] Figure 2 A decision logic diagram for forming a modified difference for the difference calculation subunit of this embodiment;

[0064] Figure 3A decision logic diagram for determining a first temporary cow by a first determination subunit in this embodiment;

[0065] Figure 4 This is a flow chart of the artificial intelligence-based dairy cow breeding method of this embodiment. DETAILED DESCRIPTION

[0066] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0068] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0069] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] See also Figure 1 As shown, it is a schematic diagram of the artificial intelligence-based dairy cow breeding system of this embodiment;

[0071] This embodiment provides a dairy cow breeding system based on artificial intelligence, including:

[0072] A data collection module, for collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount;

[0073] A determination module, connected to the data acquisition module, for determining a number of first temporary cows according to the real-time hair density and the real-time hair glossiness;

[0074] a determination module, which is connected to the determination module and the data acquisition module respectively, and is used to determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow;

[0075] a screening module, which is connected to the determination module and the data acquisition module respectively, and is used to screen the second temporary cows according to the real-time hair glossiness within a preset screening time and a preset standard gloss fluctuation threshold to form a number of abnormal cows;

[0076] an adjustment module connected to the screening module, for adjusting the preset feed amount according to the number of the abnormal cows and the number of the first temporary cows;

[0077] A calculation module, connected to the screening module, for calculating the actual reproduction rate according to the number of abnormal cows and a preset reproduction coefficient;

[0078] a prediction module connected to the data acquisition module, for predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level and a preset artificial intelligence model;

[0079] A correction module is connected to the calculation module and the prediction module respectively, and is used to correct the preset standard gloss fluctuation threshold or the preset reproduction coefficient according to the actual reproduction rate and the ideal reproduction rate.

[0080] The hair density, hair gloss, body temperature and skin moisture level of dairy cows are closely related to their reproductive rate, because these physiological indicators reflect the health and nutritional status of dairy cows, which directly affect their reproductive capacity. Hair density and gloss are usually related to the nutritional intake and metabolic level of dairy cows. Malnutrition or disease may cause sparse and dull hair, thus affecting reproductive function. Body temperature is an important indicator of dairy cow health. Too high or too low body temperature may indicate that the dairy cow is experiencing disease or stress response, which will inhibit the normal secretion of reproductive hormones. Skin moisture level reflects the hydration status and blood circulation of dairy cows. Too low skin moisture may indicate dehydration or endocrine problems, which will also have a negative impact on reproductive efficiency. Therefore, by monitoring these parameters, the health status of dairy cows can be comprehensively evaluated and reproductive management can be optimized. In addition, accurate reproductive rate data can help farms predict the production capacity and scale of future dairy cow groups, arrange resources reasonably, and optimize production plans.

[0081] The data acquisition module in the present invention realizes real-time acquisition of the physiological parameters of dairy cows by integrating a variety of high-precision sensors. Among them, the detection of hair density adopts an optical image sensor, and the hair distribution is determined by high-resolution image acquisition and algorithm analysis; the hair glossiness is calculated by a spectral sensor using the intensity and characteristics of the reflection spectrum; the body temperature adopts a non-contact infrared temperature sensor, which can accurately detect the epidermal temperature when the dairy cow is in dynamic activities; the skin moisture level is detected by a capacitive humidity sensor to detect the moisture changes on the surface of the dairy cow's skin. These sensors are connected to the data acquisition module through a wireless communication module to ensure the efficiency and real-time nature of data transmission, and provide accurate and reliable basic data for subsequent analysis and decision-making.

[0082] The preset screening time is the observation period of the system when determining the abnormal state of the cow. It depends on the daily behavior cycle of the cow and the stability of the changes in physiological parameters. It is usually set between 10 minutes and 30 minutes. In this embodiment, it is set to 20 minutes to ensure that sufficient data changes can be captured, while reducing redundant information and improving screening efficiency.

[0083] The preset standard gloss fluctuation threshold is used to determine the abnormal variation range of the glossiness of the cow's hair, which depends on the normal fluctuation range of the glossiness of healthy cows and the influence of environmental factors, and is usually set between ±5% and ±10%. In this embodiment, it is set to ±7% to more accurately screen out cows with potential health problems and reduce the incidence of misjudgment.

[0084] The preset feed amount is the feed amount determined based on the average nutritional needs and daily activity level of the dairy cows, and is usually set between 20 kg and 65 kg per cow per day; in this embodiment, it is set to 35 kg to ensure adequate nutritional supply while avoiding feed waste and optimizing reproduction and health status.

[0085] The preset reproduction coefficient is a reproduction efficiency coefficient determined by the system based on the reproduction target, the average reproduction capacity of the dairy cow population and external environmental factors, and is usually set between 0.7 and 0.9; in this embodiment, it is set to 0.85 to achieve an optimal balance in the reproduction rate while improving the scientificity and effectiveness of reproduction management.

[0086] The preset artificial intelligence model used in this system is a multimodal neural network model based on deep learning, which specifically includes these structures and applications: Convolutional neural network (CNN) is used to process real-time image data of hair density and hair gloss, extract the spatial characteristics of gloss changes, and conduct comparative analysis in combination with historical data; Recurrent neural network (RNN) is used to analyze the time series data of real-time body temperature and skin moisture level, capture the dynamic change trend of physiological parameters, especially in high-frequency sampling, to ensure the accuracy of prediction; the fusion layer fuses the features of image data and time series data, and generates a high-dimensional feature representation of overall health and reproductive capacity through the fully connected layer; the output layer combines the reproductive goals, outputs the ideal reproductive rate, and compares it with the actual reproductive rate to provide data support for subsequent correction modules.

[0087] The data acquisition module monitors the cows' hair density, glossiness, body temperature, and skin moisture in real time. The determination module selects the first temporary cow based on the hair density and glossiness information, and the judgment module further determines the second temporary cow based on the body temperature and skin moisture. The screening module screens according to the changes in hair glossiness and the standard threshold to identify abnormal cows. The adjustment module adjusts the feed amount according to the number of abnormal cows, the calculation module calculates the actual reproduction rate based on the screening results, and predicts the ideal reproduction rate through the prediction module combined with the artificial intelligence model. The correction module adjusts the standard gloss fluctuation threshold or reproduction coefficient according to the deviation between the actual and ideal reproduction rates to optimize the overall reproduction effect.

[0088] By combining precise real-time data collection with intelligent analysis, the physiological state of dairy cows can be fully monitored, and reproductive management strategies can be identified and adjusted in a timely manner. The system not only improves the diagnostic accuracy of dairy cow health by integrating multiple parameters such as hair density, glossiness, body temperature and skin moisture, but also dynamically adjusts feeding plans to optimize reproductive rates and reduce health problems caused by improper feeding. In addition, the introduction of artificial intelligence models makes the prediction of reproductive rates more accurate. By correcting thresholds and reproductive coefficients, the adaptability and accuracy of the system are continuously improved, effectively solving the problem of being unable to accurately and comprehensively reflect the reproductive status of dairy cow groups due to the unstable measurement of traditional sensors and the limitations of individual monitoring.

[0089] Specifically, the calculation module includes:

[0090] A second ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the total number of the cows to be detected, to form a second ratio;

[0091] A reproduction rate calculation unit is connected to the second proportion calculation unit and is used to calculate the product of the second proportion and a preset reproduction coefficient to form an actual reproduction rate.

[0092] The second proportion calculation unit calculates and generates the ratio of abnormal cows to the total number of cows, i.e., the second proportion, by counting the number of abnormal cows and the total number of cows. The reproduction rate calculation unit multiplies the second proportion by the preset reproduction coefficient to calculate the actual reproduction rate, providing key reference data for subsequent adjustment and prediction of the system.

[0093] By combining the proportion of abnormal cows with the preset reproduction coefficient, the module can dynamically reflect the actual reproduction status of the cow herd and avoid misjudgment caused by a single parameter. In addition, this calculation method is flexible and can adjust the reproduction rate calculation results according to the size and characteristics of different cow herds, thereby improving the applicability of the monitoring system.

[0094] Specifically, the correction module includes:

[0095] A correction calculation unit, used for calculating the relative deviation between the actual reproduction rate and the ideal reproduction rate;

[0096] a correction comparison unit, connected to the correction calculation unit, for comparing the relative deviation with a preset deviation threshold to form a correction comparison result;

[0097] A correction unit is used to correct the preset standard gloss fluctuation threshold or the preset propagation coefficient according to the correction comparison result.

[0098] The correction calculation unit calculates the relative deviation between the actual reproduction rate and the ideal reproduction rate. Then, the correction comparison unit compares the deviation with the preset deviation threshold to generate a correction comparison result. Finally, the correction unit adjusts the preset standard gloss fluctuation threshold or the preset reproduction coefficient according to the correction comparison result, thereby optimizing the reproduction prediction and control effect of the system.

[0099] The correction module can dynamically adjust key parameters to ensure consistency between the system's preset standards and actual performance. Through real-time correction, the accuracy of reproductive prediction can be further improved, avoiding long-term deviation accumulation, thereby optimizing dairy cow health management and reproductive rate.

[0100] Please continue reading Figure 2 As shown, it is a decision logic diagram of the difference calculation subunit forming a correction difference in this embodiment;

[0101] Specifically, the correction unit includes:

[0102] A difference calculation subunit, configured to calculate the difference between the relative deviation and the preset deviation threshold value to form a correction difference value when the correction comparison result is that the relative deviation is greater than the preset deviation threshold value;

[0103] A first correction subunit, connected to the difference calculation subunit, for correcting the preset standard gloss fluctuation threshold according to the relative deviation and a preset correction coefficient when the correction difference is greater than a preset standard deviation, wherein the preset standard gloss fluctuation threshold is positively correlated with the relative deviation;

[0104] The second correction subunit is connected to the difference calculation subunit, and is used to correct the preset reproduction coefficient according to the relative deviation and the preset correction coefficient when the correction difference is less than or equal to the preset standard deviation, and the reproduction coefficient and the relative deviation are positively correlated.

[0105] The preset deviation threshold is a critical value used to determine whether the relative deviation exceeds the acceptable range. It depends on the tolerance of the system and the accuracy requirements of dairy cow health management. It is usually set between 1% and 5%. In this embodiment, it is set to 3%, which can tolerate a certain error while avoiding over-correction to ensure stable operation of the system.

[0106] The preset correction coefficient is used to adjust the system parameters when the actual reproduction rate deviates from the ideal value. It is usually set based on historical data and experience to ensure the optimization of reproduction efficiency. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2, which can moderately correct the parameters and avoid excessive adjustment leading to system fluctuations.

[0107] The preset standard deviation value is used to determine whether the correction difference reaches the degree that needs adjustment, which depends on the data fluctuation range and the sensitivity requirements of the system. It is usually set between 0.1 and 0.3, and is set to 0.2 in this embodiment, which can ensure that the system can make timely adjustments when the fluctuation is large, but will not cause overreaction due to overcorrection.

[0108] The correction unit first calculates the difference between the relative deviation and the preset deviation threshold through the difference calculation subunit to form a correction difference. When the correction comparison result shows that the relative deviation is greater than the preset deviation threshold, the first correction subunit corrects the preset standard gloss fluctuation threshold according to the correction difference, the condition greater than the preset standard deviation value, the relative deviation and the preset correction coefficient; if the correction difference is less than or equal to the preset standard deviation value, the second correction subunit adjusts the preset reproduction coefficient according to the relative deviation and the preset correction coefficient.

[0109] The correction unit can flexibly adjust key parameters according to the actual deviation to ensure that the system can accurately control the conditions and standards of dairy cow reproduction. Through targeted adjustments to different correction conditions, the gloss fluctuation threshold can be optimized when the deviation is large, and the reproduction coefficient can be adjusted when the deviation is small, thereby maintaining the efficient operation of the system and improving the accuracy of dairy cow reproduction efficiency and health management, ensuring that the reproduction rate is always optimized under changing environmental conditions.

[0110] Specifically, the determination module includes:

[0111] A density comparison unit, used to compare the real-time hair density with a preset standard density to form a density comparison result;

[0112] A glossiness comparison unit, used to compare the real-time hair glossiness with a preset standard glossiness to form a glossiness comparison result;

[0113] A determination unit is connected to the density comparison unit and the gloss comparison unit respectively, and is used to determine a number of first temporary cows according to the density comparison result and the gloss comparison result.

[0114] The preset standard density is a reference value for comparing the real-time hair density of dairy cows, and usually depends on the breed, age and health of the dairy cow. Generally speaking, it is set within the normal range of a particular breed of dairy cow, usually 500-600 hairs / cm². In this embodiment, the preset standard density is set to 550 hairs / cm², ensuring that the difference between healthy dairy cows and potential problem dairy cows can be accurately identified, and the hair health status of dairy cows can be reflected in real time, and any potential health problems can be discovered in time.

[0115] The preset standard glossiness is a reference value for comparing the glossiness of the cow's hair, which depends on the breed of the cow, the season and environmental factors. It is usually set in the range of 30 glossiness to 50 glossiness to ensure that the normal brightness and health status of the cow's hair are reflected. In this embodiment, the preset standard glossiness is set to 40 glossiness, which can not only reflect the hair condition of a normal and healthy cow, but also help find cows whose hair has lost gloss, thereby identifying health problems.

[0116] In the determination module, the density comparison unit compares the real-time hair density with the preset standard density, and determines the health of the cow's hair by the difference; the glossiness comparison unit compares the real-time hair glossiness with the preset standard glossiness to evaluate the cow's hair condition. Based on the comparison results of density and glossiness, the determination unit combines these two data to identify several first temporary cows, whose health status and reproductive potential will be further analyzed and evaluated.

[0117] The determination module can accurately screen dairy cows through multi-dimensional hair health indicators (hair density and glossiness), thereby effectively judging the health status of dairy cows and providing a basis for subsequent breeding management.

[0118] Please continue reading Figure 3 As shown, it is a decision logic diagram of the first determination subunit determining the first temporary cow in this embodiment;

[0119] Specifically, the determining unit includes:

[0120] A first determination subunit is used to determine that the cow to be detected is a first temporary cow when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is less than the preset standard glossiness;

[0121] The second determination subunit is used to determine that the cow to be detected is the first temporary cow when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is greater than or equal to the preset standard glossiness, and when the number of the density comparison results within a preset determination time period is greater than the number of preset standard results.

[0122] The preset determination time period is a time period used to evaluate whether the changes in the density and glossiness of the cow's hair continue to exceed the standard threshold. It depends on the speed of changes in the cow's health status and the impact of environmental changes on the cow's hair. It is usually set between 3 days and 7 days. In this embodiment, it is set to 4 days to fully consider the volatility of the cow's health status, while avoiding the interference of too frequent data fluctuations on the judgment, ensuring that the cow's health problems can be accurately identified and the effectiveness and stability of monitoring are improved.

[0123] The preset number of standard results refers to the number of standard results or the threshold value of quantity that needs to be reached when performing density comparison within a preset time period, which depends on the data stability and fluctuation characteristics in the actual production environment. It is usually set between 3 and 5 times, and is set to 4 times in this embodiment, which can balance the sensitivity and stability to health fluctuations, and can detect health changes in a timely manner, and avoid misjudgment due to accidental instantaneous changes or environmental factors, thereby improving the accuracy and efficiency of monitoring.

[0124] In the determination module, the first determination subunit compares the cow's real-time hair density with the preset standard density, and the hair glossiness with the preset standard glossiness. If both are lower than the standard value, the cow is determined to be the first temporary cow; the second determination subunit determines whether the cow's hair density continues to be lower than the standard density within the preset determination time, and further confirms whether the cow is classified as the first temporary cow by combining the hair glossiness comparison result. This process ensures that the health status of the cow is accurately monitored and evaluated through multi-dimensional standard judgment.

[0125] By comprehensively comparing the real-time hair density and hair glossiness, the system can more accurately judge the health of the cow. Hair density and glossiness are important indicators of cow health. Low density and dull hair usually indicate malnutrition, disease or other health problems.

[0126] Specifically, the determination module includes:

[0127] a drawing unit, used for drawing a moisture change curve according to the real-time skin moisture level, and drawing a body temperature change curve according to the real-time body temperature;

[0128] a consistency calculation unit connected to the drawing unit, for calculating a correlation coefficient between the water content change curve and the body temperature change curve to form a change consistency;

[0129] An abnormality determination unit is connected to the consistency calculation unit and is used to determine that the first temporary cow is a second temporary cow when the change consistency is less than a preset standard consistency, so as to form a plurality of second temporary cows.

[0130] The preset standard consistency is a standard value determined based on the typical relationship between skin moisture and body temperature changes under normal physiological conditions of dairy cows. It depends on the physiological characteristics and health standards of dairy cows, and its purpose is to measure the consistency between the moisture change curve and the body temperature change curve. The usual setting range is between 0.7 and 0.9. In this embodiment, it is set to 0.8, which can ensure a high accuracy in monitoring the health of dairy cows, while avoiding misjudgments caused by overly strict or overly loose standards.

[0131] In the determination module, the drawing unit draws the moisture change curve and the temperature change curve according to the real-time skin moisture level and body temperature data. Then, the consistency calculation unit calculates the correlation coefficient of the two curves to obtain the change consistency. Finally, the abnormality determination unit determines the cow as the second temporary cow according to the preset standard consistency threshold if the change consistency is lower than the threshold, and further screens out abnormal cows.

[0132] By comparing the consistency of water content changes and body temperature changes, cows that may have health abnormalities can be identified, avoiding misjudgment caused by relying solely on a single indicator, thereby improving the accuracy and timeliness of health monitoring.

[0133] Specifically, the screening module includes:

[0134] A gloss fluctuation calculation unit, used to calculate the standard deviation of the real-time hair glossiness to form a gloss fluctuation value;

[0135] A screening unit is connected to the gloss fluctuation calculation unit and is used to screen the second temporary cows whose gloss fluctuation values ​​are greater than a preset gloss fluctuation threshold value to form a number of abnormal cows.

[0136] The preset gloss fluctuation threshold is a standard value used to determine whether the gloss change of cow hair exceeds the normal fluctuation range. It depends on the natural fluctuation range of cow hair health and the impact of environmental factors on hair gloss. It is usually set between 0.5% and 3%. In this embodiment, it is set to 2%, which can effectively balance the normal fluctuation range of cow hair gloss and potential health problems, thereby ensuring that the system is moderately sensitive to changes in hair gloss, can detect abnormalities in a timely manner, and will not overreact to short-term fluctuations, thereby improving screening accuracy and stability.

[0137] The screening module calculates the standard deviation of the real-time hair gloss through the gloss fluctuation calculation unit to obtain the gloss fluctuation value. Then, the screening unit compares the calculated gloss fluctuation value with the preset gloss fluctuation threshold. If the gloss fluctuation value is greater than the preset threshold, the cow is determined to be an abnormal cow, thereby screening out several abnormal cows.

[0138] The gloss fluctuation value, as an indicator of the health status of dairy cows, can effectively determine whether there are abnormal changes in dairy cows, and thus help managers adjust feeding and care measures in a timely manner.

[0139] Specifically, the adjustment module includes:

[0140] A first ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the number of the first temporary cows to form a first ratio;

[0141] an adjustment unit connected to the proportion calculation unit, for adjusting the preset feed amount according to a relative deviation between the first proportion and a preset first proportion threshold value and a preset adjustment coefficient, wherein the adjusted preset feed amount is positively correlated with the relative deviation between the first proportion and the preset first proportion threshold value;

[0142] The preset first ratio threshold refers to the standard of the ratio of abnormal cows to the first temporary cows in the adjustment module. It determines when to start the feed adjustment, which depends on the health goals of the dairy cow group, production efficiency and the monitoring requirements of the health status of dairy cows. It is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2. When the proportion of abnormal dairy cows reaches a certain level, the feed amount can be adjusted in time, thereby effectively controlling the health status of the dairy cow group, preventing resource waste, and ensuring the reproduction and production efficiency of dairy cows.

[0143] The adjustment module first calculates the ratio of the number of abnormal cows to the first temporary number of cows through the first proportion calculation unit to form a first proportion. Then, the adjustment unit adjusts the preset feed amount according to the relative deviation between the first proportion and the preset first proportion threshold value and the preset adjustment coefficient to achieve reasonable cow feeding management.

[0144] The adjustment module can automatically adjust the feed amount according to the changes in the proportion of abnormal cows to ensure that the nutritional needs of the dairy cow group are properly met. By combining the proportion deviation and the preset threshold for adjustment, feed waste or unbalanced feeding can be effectively avoided.

[0145] Please continue reading Figure 4 As shown, it is a flow chart of the cow breeding method based on artificial intelligence in this embodiment;

[0146] This embodiment also provides a dairy cow breeding method based on artificial intelligence, comprising:

[0147] Collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount;

[0148] Determine a number of first temporary cows according to the real-time hair density and the real-time hair glossiness;

[0149] Determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow;

[0150] Determine a gloss fluctuation value according to the real-time hair gloss within a preset screening time, and screen the second temporary cows according to the gloss fluctuation value and a preset gloss fluctuation threshold to form a number of abnormal cows;

[0151] Calculating a first proportion according to the number of the abnormal cows and the number of the first temporary cows, and adjusting the preset feed amount according to a comparison result of the abnormal proportion and the standard proportion threshold until the first proportion is less than a preset first proportion threshold;

[0152] Calculating the actual reproduction rate according to the number of abnormal dairy cows and a preset reproduction coefficient;

[0153] predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level, and a preset artificial intelligence model;

[0154] The preset gloss fluctuation threshold or the preset reproduction coefficient is corrected according to the actual reproduction rate and the ideal reproduction rate.

[0155] By collecting real-time data of dairy cows (such as hair density, hair gloss, body temperature and skin moisture level), the health status of dairy cows is determined based on these data, and abnormal dairy cows are gradually screened out. First, the first temporary dairy cow and the second temporary dairy cow are determined, and the abnormal dairy cows are screened by the fluctuation of real-time hair gloss. Then, the proportion of abnormal dairy cows is calculated, and the preset feed amount is adjusted according to this proportion to optimize the feeding effect of dairy cows. Next, the actual reproductive rate is compared with the ideal reproductive rate, and the artificial intelligence model is used to correct the reproductive coefficient or gloss fluctuation threshold to improve the reproductive efficiency of dairy cows.

[0156] Through accurate data collection and artificial intelligence analysis, the health status of dairy cows can be dynamically monitored, and feeding management can be adjusted according to real-time data to improve reproductive efficiency. By monitoring indicators such as hair gloss and density, health abnormalities can be discovered and adjusted in a timely manner, thereby optimizing the reproductive performance of dairy cows, improving the production efficiency of the entire dairy cow group, reducing resource waste and improving the sustainability of agricultural production.

[0157] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0158] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dairy cow breeding system based on artificial intelligence, characterized in that: include: A data collection module, for collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount; A determination module, connected to the data acquisition module, for determining a number of first temporary cows according to the real-time hair density and the real-time hair glossiness; a determination module, which is connected to the determination module and the data acquisition module respectively, and is used to determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow; a screening module, which is connected to the determination module and the data acquisition module respectively, and is used to screen the second temporary cows according to the real-time hair glossiness within a preset screening time and a preset standard gloss fluctuation threshold to form a number of abnormal cows; an adjustment module connected to the screening module, for adjusting the preset feed amount according to the number of the abnormal cows and the number of the first temporary cows; A calculation module, connected to the screening module, for calculating the actual reproduction rate according to the number of abnormal cows and a preset reproduction coefficient; a prediction module connected to the data acquisition module, for predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level and a preset artificial intelligence model; A correction module is connected to the calculation module and the prediction module respectively, and is used to correct the preset standard gloss fluctuation threshold or the preset reproduction coefficient according to the actual reproduction rate and the ideal reproduction rate.

2. The artificial intelligence-based dairy cow breeding system according to claim 1, characterized in that: The calculation module comprises: A second ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the total number of the cows to be detected, to form a second ratio; A reproduction rate calculation unit is connected to the second proportion calculation unit and is used to calculate the product of the second proportion and a preset reproduction coefficient to form an actual reproduction rate.

3. The artificial intelligence-based dairy cow breeding system according to claim 2, characterized in that: The correction module comprises: A correction calculation unit, used for calculating the relative deviation between the actual reproduction rate and the ideal reproduction rate; a correction comparison unit, connected to the correction calculation unit, for comparing the relative deviation with a preset deviation threshold to form a correction comparison result; A correction unit is used to correct the preset standard gloss fluctuation threshold or the preset propagation coefficient according to the correction comparison result.

4. The artificial intelligence-based dairy cow breeding system according to claim 3, characterized in that: The correction unit comprises: A difference calculation subunit, configured to calculate the difference between the relative deviation and the preset deviation threshold value to form a correction difference value when the correction comparison result is that the relative deviation is greater than the preset deviation threshold value; A first correction subunit, connected to the difference calculation subunit, for correcting the preset standard gloss fluctuation threshold according to the relative deviation and a preset correction coefficient when the correction difference is greater than a preset standard deviation; The second correction subunit is connected to the difference calculation subunit, and is used to correct the preset reproduction coefficient according to the relative deviation and the preset correction coefficient when the correction difference is less than or equal to the preset standard deviation.

5. The artificial intelligence-based dairy cow breeding system according to claim 4, characterized in that: The determination module comprises: A density comparison unit, used to compare the real-time hair density with a preset standard density to form a density comparison result; A glossiness comparison unit, used to compare the real-time hair glossiness with a preset standard glossiness to form a glossiness comparison result; A determination unit is connected to the density comparison unit and the gloss comparison unit respectively, and is used to determine a number of first temporary cows according to the density comparison result and the gloss comparison result.

6. The artificial intelligence-based dairy cow breeding system according to claim 5, characterized in that: The determining unit comprises: A first determination subunit is used to determine that the cow to be detected is a first temporary cow when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is less than the preset standard glossiness; The second determination subunit is used to determine that the cow to be detected is the first temporary cow according to the comparison result of the number of the density comparison results and the number of preset standard results within a preset determination time period when the density comparison result is that the real-time hair density is less than the preset standard density, and the glossiness comparison result is that the real-time hair glossiness is greater than or equal to the preset standard glossiness.

7. The artificial intelligence-based dairy cow breeding system according to claim 6, characterized in that: The determination module comprises: a drawing unit, used for drawing a moisture change curve according to the real-time skin moisture level, and drawing a body temperature change curve according to the real-time body temperature; a consistency calculation unit connected to the drawing unit, for calculating a correlation coefficient between the water content change curve and the body temperature change curve to form a change consistency; An abnormality determination unit is connected to the consistency calculation unit and is used to determine that the first temporary cow is a second temporary cow when the change consistency is less than a preset standard consistency, so as to form a plurality of second temporary cows.

8. The artificial intelligence-based dairy cow breeding system according to claim 7, characterized in that: The screening module comprises: A gloss fluctuation calculation unit, used to calculate the standard deviation of the real-time hair glossiness to form a gloss fluctuation value; A screening unit is connected to the gloss fluctuation calculation unit and is used to screen the second temporary cows whose gloss fluctuation values ​​are greater than a preset gloss fluctuation threshold value to form a number of abnormal cows.

9. The artificial intelligence-based dairy cow breeding system according to claim 8, characterized in that: The adjustment module comprises: A first ratio calculation unit, used for calculating the ratio of the number of the abnormal cows to the number of the first temporary cows to form a first ratio; An adjustment unit is connected to the proportion calculation unit and is used to adjust the preset feed amount according to a relative deviation between the first proportion and a preset first proportion threshold value and a preset adjustment coefficient.

10. A dairy cow breeding method based on artificial intelligence, based on the dairy cow breeding system based on artificial intelligence according to any one of claims 1 to 9, characterized in that: include: Collecting the real-time hair density, real-time hair glossiness, real-time body temperature and real-time skin moisture level of each cow to be tested that is fed with a preset feed amount; Determine a number of first temporary cows according to the real-time hair density and the real-time hair glossiness; Determine a number of second temporary cows according to the real-time skin moisture level and the real-time body temperature of the first temporary cow; Determine a gloss fluctuation value according to the real-time hair gloss within a preset screening time, and screen the second temporary cows according to the gloss fluctuation value and a preset gloss fluctuation threshold to form a number of abnormal cows; Calculating a first proportion according to the number of the abnormal cows and the number of the first temporary cows, and adjusting the preset feed amount according to a comparison result of the abnormal proportion and the standard proportion threshold until the first proportion is less than a preset first proportion threshold; Calculating the actual reproduction rate according to the number of abnormal dairy cows and a preset reproduction coefficient; predicting an ideal reproduction rate according to the real-time hair density, the real-time hair glossiness, the real-time body temperature, the real-time skin moisture level, and a preset artificial intelligence model; The preset gloss fluctuation threshold or the preset reproduction coefficient is corrected according to the actual reproduction rate and the ideal reproduction rate.

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