Intelligent neck ring for monitoring estrus of hu sheep and monitoring method and system thereof

Abstract

The embodiment of the specification discloses a lake sheep estrus monitoring intelligent neck ring and a monitoring method and system thereof, wherein the estrus monitoring method comprises the following steps: acquiring heart rate data, body temperature data and motion data of a target lake sheep in real time; judging a behavior state trajectory of the target lake sheep based on the motion data, wherein the behavior state comprises lying, rumination, eating, standing and walking; quantifying the heart rate data, the body temperature data and the behavior state trajectory based on historical reference information to obtain an estrus judgment index; and judging an estrus stage of the target lake sheep based on the estrus judgment index. Through the collection of multiple types of data and the analysis by combining a multi-feature fusion algorithm, the technical problem of low efficiency, poor accuracy and insufficient real-time performance in the estrus judgment of lake sheep breeding by relying on manual work and existing simple equipment is solved. The all-weather continuous monitoring is realized, and the precise and automatic estrus judgment is realized.

Description

Technical Field

[0001] Several embodiments in this specification relate to the field of intelligent aquaculture technology, specifically to the optimization of the accuracy of estrus monitoring in Hu sheep. Background Technology

[0002] In the large-scale sheep farming industry, accurate and timely monitoring of ewes' estrus cycle is crucial for improving reproductive efficiency and achieving precise mating. Ewes have a short estrus cycle with an optimal mating window; missing this window directly leads to non-pregnancy or returning to estrus, affecting lambing intervals and economic benefits. Traditionally, this process relies heavily on the experience of farmers, who make subjective judgments by visually observing the sheep's reproductive tract and behavior. This method is not only labor-intensive but also inherently flawed, suffering from observation delays, inconsistent judgment standards, and susceptibility to individual experience, making it difficult to meet the demands of modern, intensive farming for high efficiency and accuracy.

[0003] Currently, technical monitoring solutions can be mainly divided into two categories: one is based on visual image analysis, which involves deploying cameras to collect videos of sheep activities and using image processing or deep learning algorithms to identify specific behavioral characteristics to indirectly determine estrus; the other is based on the detection of physiological and biochemical samples, such as collecting vaginal mucus, blood, or urine samples from ewes and using test strips, pH detection, or hormone level (such as luteinizing hormone LH) analysis to make a judgment. This method has high accuracy under laboratory conditions.

[0004] However, in practical scenarios of monitoring the estrus cycle of Hu sheep, all of the above methods have limitations. Image-based methods are heavily influenced by environmental lighting conditions, sheep density, obstructions, and the camera's field of view, resulting in blind spots and a tendency to misinterpret similar behaviors outside of estrus as estrus signals, leading to false positives or false negatives. While chemical methods based on sample testing are accurate, they are cumbersome, cannot provide continuous monitoring, have extremely poor real-time performance, require manual sampling, have low automation, and are difficult to implement for daily, routine monitoring of large flocks of sheep. Summary of the Invention

[0005] This specification provides an intelligent neck collar for monitoring the estrus cycle of Hu sheep, along with its monitoring method and system. By collecting multiple types of data and analyzing them using a multi-feature fusion algorithm, it solves the technical problems of low efficiency, poor accuracy, and insufficient real-time performance when relying on manual labor and existing simple equipment for estrus detection in Hu sheep farming. It enables continuous monitoring around the clock, allowing for accurate and automated estrus detection.

[0006] The technical solution is as follows:

[0007] Firstly, the embodiments of this specification provide a method for monitoring the estrus period of Hu sheep, including the following steps:

[0008] Real-time acquisition of heart rate, body temperature, and movement data of the target sheep;

[0009] The behavioral state trajectory of the target sheep is determined based on motion data, including lying down, ruminating, eating, standing, and walking.

[0010] Based on historical baseline information, heart rate data, body temperature data, and behavioral state trajectories are quantified to obtain an estrus judgment index;

[0011] The estrus stage of the target Hu sheep is determined based on the estrus judgment index.

[0012] As a preferred option, it also includes: defining a social distance range with the target sheep as the center and a preset distance as the radius, and obtaining the distance information between the target sheep and the opposite-sex sheep within the social distance range in real time;

[0013] The method of determining the estrus stage of the target sheep based on the estrus judgment index includes:

[0014] When the estrus judgment index is greater than the preset estrus judgment threshold, the number of opposite-sex sheep entering the social distance range within the preset time length and their respective stay duration are obtained based on distance information, and the opposite-sex attraction index is calculated.

[0015] The estrus stage of the target sheep is determined based on the estrus judgment index and the opposite sex attraction index.

[0016] As a preferred option, it also includes: real-time acquisition of the location information between the target Hu sheep and the opposite-sex Hu sheep within the social distancing range;

[0017] The method of determining the estrus stage of the target sheep based on the estrus judgment index and the opposite sex attraction index includes:

[0018] Based on the distance and orientation information of the target sheep and the opposite-sex sheep that stayed for the longest time within a preset time period, the relative distance stability and movement direction correlation are obtained respectively, and the pairing following index is calculated.

[0019] The estrus stage of the target Hu sheep is determined based on the estrus judgment index, the opposite sex attraction index, and the pairing and following index.

[0020] As a preferred embodiment, the motion data includes triaxial acceleration data and triaxial angular velocity data of the neck;

[0021] The method of determining the behavioral state trajectory of the target sheep based on motion data includes:

[0022] Calculate the resultant acceleration, neck pitch angle, and resultant acceleration signal amplitude and resultant acceleration variance of all sampling points within a preset statistical range based on triaxial acceleration data.

[0023] Based on triaxial angular velocity data, the resultant acceleration of sampling points, neck pitch angle, and the amplitude and variance of the resultant acceleration signal of all sampling points within a preset statistical range, combined with preset duration conditions, the behavioral state trajectory of the target sheep is determined.

[0024] As a preferred embodiment, the step of quantifying heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index includes:

[0025] The heart rate index is calculated based on heart rate data and the historical baseline heart rate index of the target Hu sheep.

[0026] The body temperature index is calculated based on body temperature data and the historical baseline body temperature index of the target Hu sheep.

[0027] The movement index is calculated based on the behavioral state trajectory and the historical benchmark behavioral indicators of the target sheep.

[0028] The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index and their respective weighting coefficients.

[0029] As a preferred embodiment, the estrus judgment index is calculated based on heart rate index, body temperature index, exercise index, and their respective weighting coefficients, including:

[0030] Obtain the date of the target sheep's last estrus cycle;

[0031] The rhythm index is calculated based on the date of the last estrus and the current date;

[0032] The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index, rhythm index and their respective weighting coefficients.

[0033] As a preferred option, the following also include:

[0034] The weighting coefficients for each index are dynamically set based on the age and season of the target sheep.

[0035] As a preferred embodiment, the step of quantifying heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index also includes:

[0036] Determine whether heart rate, body temperature, and exercise data exceed preset health threshold ranges;

[0037] If so, the target sheep will be flagged as having abnormal health, and estrus cycle determination will be discontinued.

[0038] Secondly, this specification provides a system for monitoring the estrus cycle of Hu sheep, including a data acquisition module, a behavior recognition module, a processing and analysis module, and a decision-making module.

[0039] The data acquisition module acquires the target sheep's heart rate, body temperature, and movement data in real time.

[0040] The behavior recognition module determines the behavioral state trajectory of the target sheep based on motion data. The behavioral state includes lying down, ruminating, eating, standing, and walking.

[0041] The processing and analysis module quantifies heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index.

[0042] The decision-making module determines the estrus stage of the target sheep based on the estrus judgment index.

[0043] Thirdly, a smart neck collar for monitoring the estrus period of Hu sheep includes a neck collar body, a power supply unit, a communication unit, a data acquisition unit disposed in the neck collar body, and a Hu sheep estrus monitoring system as described in the second aspect of the above embodiments. The data acquisition unit includes a heart rate sensor, a body temperature sensor, an accelerometer, and a gyroscope.

[0044] The power supply unit is used to supply power to the smart neckband;

[0045] The estrus monitoring system for Hu sheep is electrically connected to the data acquisition unit and the communication unit, respectively. It acquires data from the data acquisition unit to monitor the estrus period and exchanges the monitoring results with an external terminal through the communication unit.

[0046] Fourthly, embodiments of this specification provide an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the steps described in the first aspect of the above embodiments.

[0047] Fifthly, embodiments of this specification provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps described in the first aspect of the above embodiments.

[0048] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0049] 1. This solution automatically and in real-time collects heart rate, body temperature, and movement data by equipping Hu sheep with smart neck collars integrating multiple sensors. This replaces the inefficient traditional method of relying on manual observation and monitoring, and overcomes the shortcomings of chemical detection methods, which require manual sampling and cannot be performed continuously. It frees up manpower and enables continuous tracking of individual sheep conditions. It achieves all-weather, automated, continuous monitoring, significantly improving monitoring efficiency.

[0050] 2. This method comprehensively assesses heart rate, body temperature, and specific behaviors identified through acceleration and angular velocity data. By establishing individual historical benchmarks and conducting quantitative comparisons, the assessment model becomes personalized and dynamic, effectively reducing the risk of false positives and false negatives caused by individual differences, environmental interference, or behavioral misinterpretations, thus improving the accuracy and reliability of estrus detection.

[0051] 3. This scheme introduces cross-validation based on group social behavior data. It not only analyzes the physiological signals of the target sheep itself, but also calculates the "opposite attraction index" and "pairing follow-up index" by obtaining information on the distance and location of the target sheep to surrounding opposite-sex sheep. The three-layered validation mechanism of individual, group, and pairing further enhances the robustness and confidence of the judgment.

[0052] 4. This solution includes a health abnormality assessment step before the estrus detection process. This avoids misjudging physiological abnormalities caused by diseases or other health problems as estrus, ensuring that the estrus detection results are only valid under the premise that the sheep are basically healthy. This increases the credibility of the output and improves its practicality and reliability.

[0053] 5. The intelligent neck collar for monitoring the estrus cycle of Hu sheep integrates the necessary sensors, processing system, power supply, and communication units, forming an end-to-end dedicated solution. Designed specifically for Hu sheep farming, this device overcomes the problems of insufficient sensor configuration and mismatched algorithm models in existing general-purpose or wearable devices used for other animals, providing a feasible physical platform for deployment in large-scale sheep flocks and the realization of digital farming management. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating a method for monitoring the estrus period of Hu sheep provided in Example 1 of this specification.

[0056] Figure 2This is a flowchart illustrating a method for monitoring the estrus period of Hu sheep provided in Example 2 of this specification.

[0057] Figure 3 This is a flowchart illustrating a method for monitoring the estrus period of Hu sheep provided in Example 3 of this specification.

[0058] Figure 4 This is a schematic diagram of the structure of a Hu sheep estrus monitoring system provided in Example 4 of this specification.

[0059] Figure 5 This is a schematic diagram of the overall structure of a smart neck collar for monitoring the estrus cycle of Hu sheep, provided in Example 5 of this specification.

[0060] Figure 6 This is a schematic diagram of the internal structure of a smart neck ring for monitoring the estrus cycle of Hu sheep, provided in Example 5 of this specification.

[0061] Figure 7 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0062] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0063] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0064] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0065] Ovulation in female Hu sheep typically occurs in the late estrus phase, and the egg can only survive in the oviduct for 4-8 hours, while sperm's fertilization capacity lasts for approximately 24 hours. Accurately determining the estrus period allows for mating 18-24 hours after the onset of estrus, maximizing the probability of sperm-egg fertilization and significantly improving conception rates. This also avoids missed mating windows leading to empty pregnancies or returning to estrus, shortens lambing intervals, and enables frequent breeding. Relying on manual observation of the sheep's reproductive tract physiology and behavioral characteristics to determine the estrus period is labor-intensive and subject to subjectivity and delays, potentially leading to inaccurate judgments or lower-than-expected mating rates.

[0066] Therefore, this application is submitted.

[0067] Example 1

[0068] A method for monitoring the estrus period of Hu sheep

[0069] Reference Figure 1 As shown, Figure 1 A flowchart illustrating a method for monitoring the estrus period of Hu sheep, provided as an embodiment of this specification, may include at least the following steps:

[0070] Step 101: Acquire the target sheep's heart rate, body temperature, and movement data in real time;

[0071] Step 104: Determine the behavioral state trajectory of the target sheep based on motion data, wherein the behavioral state includes lying down, ruminating, eating, standing, and walking;

[0072] Step 106: Quantify heart rate data, body temperature data, and behavioral trajectory based on historical benchmark information to obtain the estrus judgment index;

[0073] Step 108: Determine the estrus stage of the target sheep based on the estrus judgment index.

[0074] The method in this embodiment achieves automated and accurate judgment of the estrus period of Hu sheep through the fusion and quantitative analysis of multi-dimensional data.

[0075] Explanatoryly, data collection can first be accomplished using a smart monitoring device worn on the sheep (preferably around the neck). Heart rate data is collected by an optical heart rate sensor integrated into the smart monitoring device, which obtains heart rate information by detecting changes in blood flow in subcutaneous capillaries; body temperature data is collected by a temperature sensor (such as a high-precision thermistor) in close contact with the sheep's skin to reflect its surface or core body temperature; motion data is collected by an inertial measurement unit (such as a three-axis accelerometer and a three-axis gyroscope). The accelerometer measures the linear acceleration of the sheep's neck in various directions, and the gyroscope measures the angular velocity of neck rotation, together forming raw information reflecting the sheep's body activity and posture.

[0076] Then, by processing and analyzing continuous acceleration and angular velocity data, and based on preset logic or models, behavioral categories with clear biological significance are extracted from the raw motion data. The activities of the Hu sheep over a continuous time period can be categorized into several typical behavioral states such as "lying down," "ruminating," "feeding," "standing," and "walking." This yields a "behavioral state trajectory" that changes over time and consists of specific behavioral labels, thus converting sensor signals into behavioral information that can be used for quantitative analysis.

[0077] Next, the various data types are quantified. "Historical baseline information" refers to the individualized normal range or average value established through monitoring over a period of time (e.g., a 7-day baseline learning period) under the normal physiological state of non-estrus sheep. This includes, for example, the target sheep's baseline resting heart rate, baseline normal body temperature, baseline daily feeding time, baseline daily rumination time, and baseline daily total steps. Quantification is achieved by comparing and calculating the various data acquired in real-time with the corresponding historical baseline information. These quantified results, reflecting deviations from normal levels in various physiological and behavioral dimensions, are then fused using summation or other mathematical models to calculate an estrus prediction index that characterizes the probability of estrus.

[0078] Finally, a final judgment is made based on the calculated index value. For example, corresponding numerical ranges can be preset for different estrus stages of the Hu sheep. For instance, the estrus stages can be divided into "non-estrus," "pre-estrus," "proestrus," "peak estrus," and "end estrus," with a threshold range set for each stage. During the judgment, the calculated estrus judgment index is matched against these preset numerical ranges. To improve the stability of the judgment and avoid misjudgments caused by instantaneous fluctuations, a persistence condition can be introduced. For example, if the estrus judgment index remains continuously within the numerical range corresponding to "peak estrus" for a preset duration (e.g., 60 minutes), then the target Hu sheep is ultimately determined to have entered "peak estrus." The corresponding stage judgment result can then be output accordingly.

[0079] This embodiment presents a more reliable and efficient solution for monitoring the estrus cycle of Hu sheep compared to existing technologies. Addressing the monitoring blind spots and high false positive rates caused by limitations in lighting, occlusion, and field of view inherent in image analysis methods, this embodiment directly and continuously collects physiological and movement data of Hu sheep using wearable devices. This achieves personalized, real-time monitoring unaffected by environmental factors, and integrates multi-dimensional information such as heart rate, body temperature, and behavior for quantitative analysis, improving judgment specificity and reducing false positives and false negatives. Addressing the pain points of cumbersome, discontinuous, and unreal-time chemical testing methods, this embodiment achieves automated and real-time data acquisition and processing, enabling timely early warning. Furthermore, leveraging the wearable device form factor, it provides a feasible path for the routine and digital monitoring of large-scale populations.

[0080] In one embodiment of this specification, the motion data includes triaxial acceleration data and triaxial angular velocity data of the neck;

[0081] The method of determining the behavioral state trajectory of the target sheep based on motion data includes:

[0082] Calculate the resultant acceleration, neck pitch angle, and resultant acceleration signal amplitude and resultant acceleration variance of all sampling points within a preset statistical range based on triaxial acceleration data.

[0083] Based on triaxial angular velocity data, the resultant acceleration of sampling points, neck pitch angle, and the amplitude and variance of the resultant acceleration signal of all sampling points within a preset statistical range, combined with preset duration conditions, the behavioral state trajectory of the target sheep is determined.

[0084] This embodiment specifically discloses the types of motion data, the key feature parameters extracted from them, and the methods for using these parameters to make behavioral logic judgments.

[0085] Explaining the data, firstly, the motion data specifically includes triaxial acceleration and triaxial angular velocity data of the neck, collected by sensors worn around the neck of the Hu sheep. The triaxial acceleration data (usually from an accelerometer) measures the linear acceleration experienced by the collar in three mutually perpendicular directions in space (X, Y, and Z axes). The triaxial angular velocity data (usually from a gyroscope) measures the angular velocity of rotation around the Hu sheep's neck along three axes. Together, these two types of data provide comprehensive information about the Hu sheep's neck motion.

[0086] Secondly, feature values ​​with clear physical meaning and behavioral discrimination are extracted from the raw motion data. Specifically, the resultant acceleration A and neck pitch angle are calculated from the triaxial acceleration values ​​at a single sampling moment, using the following formulas:

[0087] ;

[0088] ;

[0089] A X The accelerometer outputs acceleration on the X-axis (unit: A Y The accelerometer outputs the acceleration on the Y-axis (unit: A Z The accelerometer outputs the acceleration along the Z-axis (unit: The resultant acceleration A quantifies the overall movement intensity of the Hu sheep. When the sheep is at rest, this value primarily reflects gravity, approaching 1g; when the sheep is moving, this value increases significantly, thus serving as a baseline indicator for distinguishing between static and dynamic states. The neck pitch angle, measured in degrees, directly identifies the head posture of the Hu sheep. It directly reflects whether the sheep is looking up at eye level or looking down to forage.

[0090] Both the resultant acceleration signal amplitude (SA) and the resultant acceleration variance (VARA) are statistical characteristics calculated within a preset statistical time range (i.e., composed of a certain number of continuous sampling points n; for example, when the sensor sampling rate is 200 Hz, the value of n is selected as 30-300). SA is the sum of the absolute values ​​of the resultant acceleration differences of all adjacent sampling points within the window, and the formula is:

[0091] ;

[0092] A i Let be the sum acceleration at the i-th sampling point, which is sensitive to continuous, subtle periodic oscillations (such as chewing, rumination, and peristalsis).

[0093] VARA is the sum of all acceleration values ​​within a window relative to its average value. The measure of dispersion is given by the following formula:

[0094] ;

[0095] VARA reflects the volatility or intensity of a movement pattern. Small variance indicates stable movement, while large variance indicates intense or irregular movement.

[0096] Finally, the calculated feature parameters (resultant acceleration A, pitch angle, angular velocity GYRO, signal amplitude SA, and variance VARA) are compared with pre-set empirical threshold ranges for each behavior state. Furthermore, the state meeting the conditions must persist for a certain duration before being finally identified as that behavior. This multi-feature threshold combination and duration verification logic effectively improves the accuracy and anti-interference capability of behavior recognition.

[0097] For example, to determine a behavioral state as recumbent, it is required that A < 1.05g (almost no movement overall), VARA < 0.002 (extremely smooth movement), GYRO < 2.0° / s (almost no rotation), and this state lasts for more than 1 minute; to determine a behavioral state as eating, it is required that pitch < -30° (head significantly lowered), GYRO within [3° / s, 12° / s] (head swaying regularly), VARA within [0.015, 0.06] (moderate shaking), SA > 0.4 (significant accumulation of micro-movements), and this state lasts for more than 7 seconds.

[0098] In summary, this embodiment clarifies the composition of motion data and discloses in detail the specific calculation logic and judgment rules from data to features and from features to behavior labels, providing a clear and implementable technical path for behavior state trajectory judgment.

[0099] In one embodiment of this specification, the step of quantifying heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index includes:

[0100] The heart rate index is calculated based on heart rate data and the historical baseline heart rate index of the target Hu sheep.

[0101] The body temperature index is calculated based on body temperature data and the historical baseline body temperature index of the target Hu sheep.

[0102] The movement index is calculated based on the behavioral state trajectory and the historical benchmark behavioral indicators of the target sheep.

[0103] The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index and their respective weighting coefficients.

[0104] This embodiment discloses a method for quantitative decomposition and fusion calculation of multi-source heterogeneous data into a single comprehensive evaluation index.

[0105] Specifically, a strategy of first quantifying by dimension and then weighting and integrating is adopted to transform the three different types of monitoring data—heart rate, body temperature, and behavior—into dimensionless indices that reflect their degree of change relative to the individual's normal level, and then integrate them into an overall estrus judgment index.

[0106] First, a heart rate index is calculated to quantify the deviation of heart rate from an individual's normal range. To do this, a historical baseline heart rate index needs to be established for the target sheep. For example, during a 7-day baseline learning period, heart rate data is collected when the sheep are in a "resting" behavioral state, and the average of these data is calculated to obtain the individual's historical baseline heart rate index H. base Next, calculate the heart rate index; for example, calculate the average heart rate over the past 6 hours based on heart rate data. Then calculate the heart rate index:

[0107] .

[0108] Furthermore, a short-term trend amplification factor can be introduced when calculating the heart rate index H. For example, the average heart rate over the past 24 hours can also be calculated based on the heart rate data. The short-term trend amplification factor can be defined as: This amplifies the difference between short-term (6h) and medium-term (24h) data. If the average heart rate is higher in the short term than in the medium term, the short-term trend amplification factor is greater than 1, indicating that the short-term heart rate is trending upwards, and vice versa. Then calculate the heart rate index:

[0109] ;

[0110] Secondly, the body temperature index (T) is calculated to quantify abnormal changes in body temperature, with a calculation logic similar to the heart rate index (H). Based on historical data, body temperature values ​​within the normal physiological range (e.g., 38.5℃ to 39.8℃) during a 7-day baseline learning period are selected, and their average value is calculated to obtain the individual's historical baseline body temperature index (T). base Next, calculate the body temperature index; for example, calculate the average body temperature over the past 6 hours based on the body temperature data. Then calculate the body temperature index:

[0111] .

[0112] Next, calculate the change in activity levels related to estrus by quantifying the movement index. Quantitative indicators of key behaviors in the recent period (e.g., within the last 24 hours) can be derived from behavioral patterns, such as total feeding time within 24 hours. Total rumination time within 24 hours Total steps in 24 hours Based on historical data, the baseline total eating time over a 7-day baseline learning period was calculated on an average of 24 hours. The baseline total rumination time within an average of 24 hours over 7 days The baseline total number of steps within an average of 24 hours over 7 days It's easy to understand that each time the resultant acceleration A reaches a maximum or minimum value, one step is counted, accumulating until the walking state stops, and the total number of steps is counted. Finally, the motion index B is calculated based on the combination of these components.

[0113] ;

[0114] The values ​​of 0.4, 0.3, and 0.3 represent the weights for walking, eating, and rumination, respectively, and can be set according to the actual impact of changes in different behaviors on the calculation of the movement index. Furthermore, since Hu sheep typically exhibit restlessness, increased activity, and reduced eating and rumination time during estrus, the calculations for eating and rumination are performed by dividing the baseline value by the recent value, and the calculations for walking are performed by dividing the recent value by the baseline value.

[0115] After obtaining the heart rate index, body temperature index, and activity index, preset weighting coefficients are assigned to these three indices, and the estrus judgment index X is calculated by fusion using mathematical models such as weighted summation. The higher the value of X, the greater the probability that the target sheep has entered the estrus period.

[0116] In one embodiment of this specification, the calculation of the estrus judgment index based on the heart rate index, body temperature index, exercise index, and their respective weighting coefficients includes:

[0117] Obtain the date of the target sheep's last estrus cycle;

[0118] The rhythm index is calculated based on the date of the last estrus and the current date;

[0119] The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index, rhythm index and their respective weighting coefficients.

[0120] This embodiment further broadens the quantitative dimensions of the estrus judgment index by introducing the estrus cycle rhythm, so that the final estrus judgment index can reflect the inherent and periodic physiological laws of the Hu sheep, thereby improving the foresight and accuracy of the judgment.

[0121] Interpretively, estrus in Hu sheep is a repetitive physiological event with a near-fixed cycle, and the date of the last estrus provides crucial prior information for predicting the possible time window of the next estrus. Therefore, based on the established evaluation system based on heart rate index, body temperature index, and exercise index, correction factors based on time series and biological rhythms are added.

[0122] First, the last estrus date of the target Hu sheep can be obtained from historical records or manually entered by the breeding manager and denoted as Dlast. Then, the time information is converted into a quantifiable value related to the probability of estrus. For example, trigonometric functions are used to simulate the cyclical fluctuations, and the rhythm index C is calculated:

[0123] ;

[0124] Where Dnow is the current date. This represents the number of days since the last estrus. The constant 17 represents the typical estrus cycle length of the Hu sheep (approximately 17 days). The output range of the cosine function cosine fluctuates between [-1, 1], and after adding 1, the rhythm exponent C ranges between [0, 2] and changes periodically. When the C value is close to 0 (just after estrus) or a multiple of a complete cycle, it is close to 2, indicating a high probability of estrus; when the number of days in the past is close to half a cycle (e.g., 8.5 days), the C value is close to 0, indicating a low probability of estrus.

[0125] Finally, appropriate weighting coefficients are assigned to the heart rate index, body temperature index, exercise index, and rhythm index, respectively. The four index dimensions are then fused using weighted summation or other fusion models to generate the final estrus judgment index X. The rhythm index changes smoothly over time and is independent of specific physical signs on that day, making estrus determination more intelligent and closer to the natural physiological rhythms of animals, thus helping to reduce false positives.

[0126] In one embodiment of this specification, it further includes:

[0127] The weighting coefficients for each index are dynamically set based on the age and season of the target sheep.

[0128] It is illustrative that the physiological and behavioral manifestations of Hu sheep with different physiological characteristics during estrus may vary, and the importance of various indicators may also differ for the same Hu sheep under different external environments. Therefore, the weights assigned to the heart rate index, body temperature index, activity index, and rhythm index in this embodiment are not fixed values, but rather a set of weight coefficients most suitable for the current monitoring scenario is automatically selected or calculated based on the individual attributes (sex, age) of the target Hu sheep and the current environmental time information (season), thereby achieving more accurate personalized judgment.

[0129] Specifically, the intensity and characteristics of estrus in Hu sheep may vary at different physiological stages (such as puberty in youth, peak estrus in adulthood, and old age). For example, the estrus symptoms of young ewes may be less typical than those of adult ewes, requiring a higher weighting for their behavioral index and a relatively lower weighting for their physiological index. Conversely, for multiparous adult ewes, the system may rely more on their stable physiological cycle patterns, thus assigning a higher weight to the rhythm index. The system can retrieve preset weighting parameter tables for different age groups based on the sheep's age or parity recorded in the database. Similarly, environmental seasons significantly affect the basal metabolism, activity level, and estrus cycle of Hu sheep. For example, in the hot summer, the basal heart rate of Hu sheep may generally be higher, resulting in a greater thermoregulatory load. In this case, a small increase in the heart rate index alone may have a lower indicative significance for estrus, and its weight can be reduced accordingly. At the same time, seasonal changes in the photoperiod are a key factor affecting the reproductive endocrine system of sheep, thus influencing the prominence of their estrus behavior. Therefore, the season can be determined based on the current date, and the weights of various indices can be dynamically adjusted to compensate for interference from environmental factors, so that the calculation model can maintain the stability of its judgment in different seasons.

[0130] This weighted differentiation strategy in this embodiment enables the final estrus judgment index X to more sensitively and specifically reflect the true estrus state of a specific individual under specific conditions, thereby improving the adaptability, individualization level and overall monitoring accuracy of the method.

[0131] In one embodiment of this specification, the step of quantifying heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index further includes:

[0132] Determine whether heart rate, body temperature, and exercise data exceed preset health threshold ranges;

[0133] If so, the target sheep will be flagged as having abnormal health, and estrus cycle determination will be discontinued.

[0134] This embodiment adds a health anomaly screening step to the entire estrus determination process, modifying the original direct estrus index calculation process into a two-branch process that includes conditional judgment. This ensures that estrus determination is based on the premise that the sheep are in a basically healthy state, thereby effectively avoiding abnormal physiological and behavioral data caused by disease, stress, or other health problems, which could seriously interfere with estrus determination and produce false positive or false negative results.

[0135] Specifically, the monitored heart rate, body temperature, and activity data are compared with preset health threshold ranges representing the normal range of vital signs in Hu sheep to quickly identify dangerous states in target Hu sheep that significantly deviate from their healthy state. For example, a heart rate below 38 beats / min indicates bradycardia; a resting heart rate above 130 beats / min indicates tachycardia. A body temperature below 38.2°C or above 40.8°C indicates abnormal body temperature. A total step count below 200 steps in 24 hours indicates insufficient steps, and the animal may be extremely depressed and unable to move normally.

[0136] If any abnormalities are detected, an alert will be issued to management personnel regarding the abnormal health status of the target sheep, and estrus detection will be discontinued. Continuing to use potentially distorted data to determine estrus when a clear health abnormality is present will yield unreliable and meaningless results; therefore, detection should be stopped to avoid issuing incorrect estrus alerts and misleading breeding management.

[0137] If no abnormalities are found, proceed to step 106 to calculate the estrus judgment index.

[0138] This embodiment ensures that the estrus detection model operates only when the data is reliable, thereby improving the accuracy and reliability of the final judgment result, and providing basic health abnormality alerts.

[0139] Example 2

[0140] A method for monitoring the estrus period of Hu sheep

[0141] Reference Figure 2 As shown, Figure 2 This is a flowchart illustrating a method for monitoring the estrus period of Hu sheep according to one embodiment of this specification. The difference between this embodiment and Embodiment 1 is that the estrus monitoring method further includes:

[0142] Step 102: Determine the social distance range with the target sheep as the center and a preset distance as the radius, and obtain the distance information between the target sheep and the opposite-sex sheep within the social distance range in real time;

[0143] Step 108, determining the estrus stage of the target sheep based on the estrus judgment index, includes:

[0144] Step 1081: When the estrus judgment index is greater than the preset estrus judgment threshold, obtain the number of opposite-sex sheep entering the social distance range within the preset time length and their respective stay duration based on the distance information, and calculate the opposite-sex attraction index.

[0145] Step 1082: Determine the estrus stage of the target sheep based on the estrus judgment index and the opposite sex attraction index.

[0146] Explanatoryly, this embodiment introduces a cross-validation mechanism based on group social behavior data. It utilizes the biological behavior of Hu sheep releasing pheromones and attracting mates during estrus as an objective external validation signal to assist in the judgment and confidence of the estrus judgment index calculated based on individual physiological and behavioral data, thereby enhancing the robustness and accuracy of judgment in complex real-world farming environments.

[0147] First, continuous monitoring of group spatial relationships is conducted. A social distance range is defined with any target sheep as the center and a preset radius (e.g., 5 meters). This range is considered an area where estrus-related social interactions may occur. To achieve this, each sheep is equipped with a near-field ranging device (e.g., a UWB module). This module measures and reports the instantaneous distance between the sheep and all other sheep in the enclosure at a certain frequency (e.g., once per second). Simultaneously, based on pre-stored individual identity and attribute data for each sheep, especially gender information, and using real-time distance data and gender information, all opposite-sex sheep within the social distance range of any target sheep are continuously filtered out, and their corresponding IDs and real-time distances are recorded. This creates a dynamic list of nearby opposite-sex sheep and their distance information for each sheep.

[0148] Secondly, when the calculated estrus judgment index, reflecting the physiological and behavioral state of the target sheep, exceeds a preset estrus judgment threshold (e.g., a threshold of 3.85 indicating the entry into the "pre-estrus period"), the sheep is determined to have shown signs of estrus, triggering this cross-validation process. Historical distance records within a preset time period (e.g., 6 hours) are reviewed. For each female sheep that entered the target sheep's social distance range during this period, their cumulative stay time is calculated. Then, considering the number of female sheep entering the social range within this preset time period and their respective stay times, a female attraction index is calculated.

[0149] For example, a weighted combination and normalization process is performed on parameters such as "different numbers of opposite sexes" and "total stay time" or "average stay time" to generate an attraction index. The higher this value, the more obvious the effect of attracting attention and approach from the opposite sex when the target sheep shows physiological signs of estrus.

[0150] The final determination of estrus can be based on a tiered decision-making rule. For example, when the "estrus judgment index" is in the "pre-estrus" range and the "sex attraction index" is also simultaneously above a certain threshold, the confidence level can be raised to "high confidence - pre-estrus." Conversely, if the "estrus judgment index" is high but the "sex attraction index" is extremely low, it may indicate that the physiological abnormality stems from disease or other factors, giving a "low confidence" warning or suggesting a health check, thereby effectively reducing false positives. When the "estrus judgment index" has entered the "proestrus" or "peak estrus" phase, a high "sex attraction index" can serve as a strong confirmatory signal, increasing the confidence level of a true positive result and guiding management to immediately take mating measures.

[0151] This embodiment collects and utilizes natural spatial social interaction data within a Hu sheep herd to construct a dual-judgment model that corroborates individual internal physiological signals and group external behavioral responses. This effectively enhances the monitoring's resistance to individual accidental errors (such as temporary sensor malfunctions or single severe stress events) and environmental interference, achieving high-precision and high-reliability automated monitoring of the Hu sheep estrus cycle.

[0152] Example 3

[0153] A method for monitoring the estrus period of Hu sheep

[0154] Reference Figure 3 As shown, Figure 3 This is a flowchart illustrating a method for monitoring the estrus period of Hu sheep, provided as an embodiment of this specification. The difference between this embodiment and Embodiment 2 is that the estrus monitoring method further includes:

[0155] Step 103: Obtain real-time location information between the target sheep and the opposite-sex sheep within the social distancing range;

[0156] Step 1082, determining the estrus stage of the target sheep based on the estrus judgment index and the opposite sex attraction index, includes:

[0157] Step 10821: Based on the distance and orientation information of the target sheep and the opposite-sex sheep that stayed for the longest time within the preset time period, obtain the relative distance stability and movement direction correlation, and calculate the pairing following index.

[0158] Step 10822: Determine the estrus stage of the target sheep based on the estrus judgment index, the opposite sex attraction index, and the pairing and following index.

[0159] This embodiment further introduces a pairing follower index to identify a more specific estrus behavior pattern—stable pairing intention—from the general attention represented by the heterosexual attraction index. This constructs a three-layer verification framework of individual physiological signals, group attraction effect, and stable pairing interaction, effectively filtering out interference and achieving ultra-high accuracy in estrus detection.

[0160] Specifically, based on the group attraction assessment using the number of opposite-sex sheep and their dwell time, a refined analysis of the behavior of the core interaction targets is conducted. First, not only is distance information between sheep obtained, but also the directional information between the target sheep and each opposite-sex sheep within the social distance range is simultaneously acquired (e.g., distance and angle can be measured simultaneously using positioning technologies such as UWB). After calculating the opposite-sex attraction index, the sheep with the longest dwell time among all opposite-sex sheep that have appeared within a preset time period is identified and designated as the core opposite-sex sheep for interaction with the target sheep during that time period, serving as the object of subsequent refined analysis.

[0161] Secondly, the pairing and following index is fused from two key sub-features. Relative distance stability quantifies the fluctuation in the spatial distance between the target sheep and its mate. The instantaneous distance sequence between the two can be extracted, and the standard deviation of this sequence can be calculated. The smaller the standard deviation, the more stable the distance. It's easy to understand that to obtain an index ranging from 0 to 1 that is positively correlated with stability, an exponential decay function can be used for mapping. The closer to 1, the more stable the close proximity, indicating a strong mutual attraction and following tendency. The correlation of movement direction quantifies the synchronicity of the two sheep's movement trajectories. Based on the acquired directional information, the displacement sequences of the target sheep and its mate can be reconstructed, and the Pearson correlation coefficients of their displacement sequences in the east-west (X-axis) and north-south (Y-axis) directions can be calculated. The closer this value is to 1, the more synchronized their movement directions are, which is a significant characteristic of mating behavior during estrus.

[0162] Finally, in the final judgment, the estrus period is determined by considering three dimensions: the estrus judgment index reflecting the sheep's physiological state, the opposite sex attraction index reflecting the group's attention, and the pairing and following index reflecting the quality of deep interaction. A decision-making rule integrating these three factors can also be established. Only when all three factors increase simultaneously is the sheep considered to have entered a specific estrus stage with the highest confidence level.

[0163] This three-layer verification mechanism can effectively distinguish between true and false estrus signals, completely differentiating false positive clusters caused by accidental factors, group stress, or resource competition from genuine estrus behavior aimed at mating, thereby minimizing the overall false alarm rate.

[0164] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0165] Example 4

[0166] A monitoring system for the estrus cycle of Hu sheep

[0167] Please refer to the following. Figure 4 , Figure 4 A schematic diagram of a monitoring system for the estrus period of Hu sheep provided in an embodiment of this specification is shown.

[0168] The Hu sheep estrus monitoring system 400 includes a data acquisition module 401, a behavior recognition module 402, a processing and analysis module 403, and a decision-making module 404.

[0169] The data acquisition module 401 acquires the heart rate data, body temperature data, and movement data of the target sheep in real time.

[0170] The behavior recognition module 402 determines the behavioral state trajectory of the target sheep based on motion data. The behavioral state includes lying down, ruminating, eating, standing, and walking.

[0171] The processing and analysis module 403 quantifies heart rate data, body temperature data, and behavioral state trajectory based on historical benchmark information to obtain an estrus judgment index.

[0172] The decision-making module 404 determines the estrus stage of the target sheep based on the estrus judgment index.

[0173] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the estrus monitoring system embodiment is basically similar to the estrus monitoring method embodiment, so the description is relatively simple; relevant parts can be referred to the description of the estrus monitoring method embodiment.

[0174] Example 5

[0175] A smart neck collar for monitoring the estrus cycle of Hu sheep

[0176] Please refer to the following. Figure 5 , Figure 6 , Figure 5 , Figure 6The overall structural diagram and internal structural diagram of a smart neck collar for monitoring the estrus period of Hu sheep provided in the embodiments of this specification are shown respectively.

[0177] A smart neck collar for monitoring the estrus cycle of Hu sheep includes a neck collar body 1, a power supply unit 2, a communication unit 3, a data acquisition unit disposed within the neck collar body, and the aforementioned Hu sheep estrus cycle monitoring system 400. The data acquisition unit includes a heart rate sensor 4, a body temperature sensor 5, an accelerometer, and a gyroscope 6.

[0178] The power supply unit 2 is used to supply power to the smart neckband;

[0179] The Hu sheep estrus monitoring system 400 is electrically connected to the data acquisition unit and the communication unit 3, respectively, to acquire data from the data acquisition unit for estrus monitoring, and to exchange the monitoring results with an external terminal through the communication unit 3.

[0180] To illustrate, most existing wearable devices have limited functions and insufficient sensor configuration to comprehensively acquire estrus-related indicators, thus failing to achieve accurate identification and judgment in this scenario.

[0181] The smart neckband proposed in this implementation is a wearable electronic device integrating sensing, power supply, computing, and communication functions. It is specifically designed for monitoring the estrus cycle of Hu sheep and is a physical device capable of comprehensively collecting key data and performing professional analysis. The specific technical components of this neckband include the following:

[0182] The neck ring body 1 is the physical shell and structural basis of the device, and its shape and size are designed to be worn safely and securely on the neck of the Hu sheep.

[0183] Power supply unit 2 supplies power to the smart neckband and typically includes a rechargeable battery and corresponding power management circuitry. It ensures that all electrical components, such as the heart rate sensor, processor, and communication module, can operate continuously for extended periods to meet the needs of continuous monitoring.

[0184] The data acquisition unit is the sensing front end for accurate monitoring, comprehensively acquiring physiological and behavioral indicators related to estrus. It includes a heart rate sensor 4, a body temperature sensor 5, an accelerometer, and a gyroscope 6. The heart rate sensor 4 is used for non-invasive detection of the real-time heart rate of the sheep using optical or electrical methods; the body temperature sensor 5 is used for contact measurement of the sheep's real-time surface or near-core body temperature; the accelerometer and gyroscope 6 together constitute an inertial measurement unit, used for high-frequency acquisition of the sheep's triaxial linear acceleration and triaxial angular velocity at the neck.

[0185] The Hu sheep estrus monitoring system 400 is the core processing unit of the smart neckband and can be integrated into the processor and memory of this smart neckband. This system is electrically connected to the data acquisition unit and can acquire and process data from heart rate, body temperature, accelerometer, and gyroscope sensors, executing monitoring algorithms.

[0186] Communication unit 3 enables wireless data interaction with external terminals (such as the central server of the farm or the administrator's mobile device) (e.g., via Wi-Fi, Bluetooth, or LoRa technology). The monitoring results obtained by the Hu sheep estrus monitoring system 400 are sent out through communication unit 3, and configuration commands from external terminals can also be sent to the neck ring through this unit.

[0187] The smart neckband may also include a USB port for charging and data transfer.

[0188] This embodiment integrates a multi-sensor data acquisition unit specifically adapted to the physiological characteristics of Hu sheep, an algorithm processing system specially designed for Hu sheep estrus monitoring, and a power supply and communication unit to maintain its operation into a wearable neck collar, forming a complete end-to-end dedicated monitoring device. This device can meet the needs of large-scale, accurate, and automated Hu sheep estrus identification in livestock farms.

[0189] Example 6

[0190] An electronic device

[0191] Please see Figure 7 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this specification.

[0192] like Figure 7 As shown, the electronic device 700 may include at least one processor 701, at least one network interface 704, a user interface 703, a memory 705, and at least one communication bus 702.

[0193] The communication bus 702 can be used to realize the connection and communication of the above components.

[0194] The user interface 703 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0195] The network interface 704 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0196] The processor 701 may include one or more processing cores. The processor 701 connects to various parts within the electronic device 700 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 705, and by calling data stored in the memory 705. Optionally, the processor 701 may be implemented using at least one hardware form selected from DSP, FPGA, and PLC. The processor 701 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 701 and may be implemented as a separate chip.

[0197] The memory 705 may include RAM or ROM. Optionally, the memory 705 may include a non-transitory computer-readable medium. The memory 705 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 705 may also be at least one storage device located remotely from the aforementioned processor 701. As a computer storage medium, the memory 705 may include an operating system, a network communication module, a user interface module, and an estrus monitoring application. The processor 701 may be used to call the estrus monitoring application stored in the memory 705 and execute the steps of the estrus monitoring method mentioned in the foregoing embodiments.

[0198] Example 7

[0199] A computer-readable storage medium

[0200] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above-described estrus monitoring method embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0201] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0202] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0203] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A method for monitoring the estrus period of Hu sheep, characterized in that, Includes the following steps: Real-time acquisition of heart rate, body temperature, and movement data of the target sheep; The behavioral state trajectory of the target sheep is determined based on motion data, including lying down, ruminating, eating, standing and walking. Based on historical baseline information, heart rate data, body temperature data, and behavioral state trajectories are quantified to obtain an estrus judgment index; The estrus stage of the target Hu sheep is determined based on the estrus judgment index.

2. The method for monitoring the estrus period of Hu sheep according to claim 1, characterized in that, Also includes: The social distance range is defined with the target sheep as the center and a preset distance as the radius, and the distance information between the target sheep and the opposite-sex sheep within the social distance range is obtained in real time. The method of determining the estrus stage of the target Hu sheep based on the estrus judgment index includes: When the estrus judgment index is greater than the preset estrus judgment threshold, the number of opposite-sex sheep entering the social distance range within the preset time length and their respective stay duration are obtained based on distance information, and the opposite-sex attraction index is calculated. The estrus stage of the target sheep is determined based on the estrus judgment index and the opposite sex attraction index.

3. The method for monitoring the estrus period of Hu sheep according to claim 2, characterized in that, Also includes: Real-time location information of target Hu sheep and Hu sheep of the opposite sex within the social distancing range; The method of determining the estrus stage of the target sheep based on the estrus judgment index and the opposite sex attraction index includes: Based on the distance and orientation information of the target sheep and the opposite-sex sheep that stayed for the longest time within a preset time period, the relative distance stability and movement direction correlation are obtained respectively, and the pairing following index is calculated. The estrus stage of the target Hu sheep is determined based on the estrus judgment index, the opposite sex attraction index, and the pairing and following index.

4. The method for monitoring the estrus period of Hu sheep according to claim 1, characterized in that, The motion data includes triaxial acceleration data and triaxial angular velocity data of the neck; The method of determining the behavioral state trajectory of the target sheep based on motion data includes: The resultant acceleration, neck pitch angle, and resultant acceleration signal amplitude and variance of all sampling points within a preset statistical range are calculated based on triaxial acceleration data. Based on triaxial angular velocity data, the resultant acceleration of sampling points, neck pitch angle, and the amplitude and variance of the resultant acceleration signal of all sampling points within a preset statistical range, combined with preset duration conditions, the behavioral state trajectory of the target sheep is determined.

5. The method for monitoring the estrus period of Hu sheep according to claim 1, characterized in that, The process of quantifying heart rate data, body temperature data, and behavioral trajectory based on historical benchmark information to obtain an estrus judgment index includes: The heart rate index is calculated based on heart rate data and the historical benchmark heart rate of the target Hu sheep. The body temperature index is calculated based on body temperature data and the historical baseline body temperature index of the target Hu sheep. The movement index is calculated based on the behavioral state trajectory and the historical benchmark behavioral indicators of the target sheep. The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index and their respective weighting coefficients.

6. The method for monitoring the estrus period of Hu sheep according to claim 5, characterized in that, The estrus judgment index is calculated based on heart rate index, body temperature index, exercise index, and their respective weighting coefficients, including: Obtain the date of the target sheep's last estrus cycle; The rhythm index is calculated based on the date of the last estrus and the current date; The estrus judgment index is calculated based on the heart rate index, body temperature index, exercise index, rhythm index and their respective weighting coefficients.

7. A method for monitoring the estrus period of Hu sheep according to any one of claims 5 or 6, characterized in that, Also includes: The weighting coefficients for each index are dynamically set based on the age and season of the target sheep.

8. The method for monitoring the estrus period of Hu sheep according to claim 1, characterized in that, The process of quantifying heart rate data, body temperature data, and behavioral trajectory based on historical benchmark information to obtain an estrus judgment index also includes: Determine whether heart rate, body temperature, and exercise data exceed preset health threshold ranges; If so, the target sheep will be flagged as having abnormal health, and estrus cycle determination will be discontinued.

9. A system for monitoring the estrus cycle of Hu sheep, characterized in that, It includes a data acquisition module, a behavior recognition module, a processing and analysis module, and a decision-making module. The data acquisition module acquires the target sheep's heart rate, body temperature, and movement data in real time. The behavior recognition module determines the behavioral state trajectory of the target sheep based on motion data. The behavioral state includes lying down, ruminating, eating, standing, and walking. The processing and analysis module quantifies heart rate data, body temperature data, and behavioral state trajectories based on historical benchmark information to obtain an estrus judgment index. The decision-making module determines the estrus stage of the target sheep based on the estrus judgment index.

10. A smart neck collar for monitoring the estrus cycle of Hu sheep, characterized in that, The system includes a neck collar body, a power supply unit, a communication unit, a data acquisition unit disposed within the neck collar body, and a monitoring system for the estrus period of a Hu sheep as described in claim 9. The data acquisition unit includes a heart rate sensor, a body temperature sensor, an accelerometer, and a gyroscope. The power supply unit is used to supply power to the smart neckband; The estrus monitoring system for Hu sheep is electrically connected to the data acquisition unit and the communication unit, respectively. It acquires data from the data acquisition unit to monitor the estrus period and exchanges the monitoring results with an external terminal through the communication unit.

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