Production and monitoring system for welfare auxiliary products of cashmere goats

Abstract

The invention relates to the technical field of animal welfare product management, in particular to a production and monitoring system of cashmere goat welfare auxiliary products, which comprises a hormone response partition module, an injection optimization module, a reproduction association analysis module, an estrus trend mapping module and a safety storage module. According to the method, fluctuation intervals are dynamically divided through the concentration of hormone in the down producing goat serum, key tracking of abnormal areas is enhanced, the response speed to reproductive state changes is increased, the data collection rhythm is optimized by regulating and controlling the monitoring frequency, ineffective information interference is reduced, reasonable resource allocation is achieved, correlation between measuring points is established through hormone feature classification, and the accuracy and accuracy of detection are improved. The accuracy of hormone fluctuation trend identification is enhanced, the reliability of multi-fetal pre-judgment and the accuracy of intervention opportunities are improved, storage partitions are delimited according to spatial distribution, information archiving logic is optimized, the integrity of data storage and retrieval efficiency are enhanced, accurate control and risk avoidance of reproduction management are achieved, and the intervention failure rate is reduced.

Description

Technical Field

[0001] This invention relates to the field of animal welfare product management technology, and in particular to a production and monitoring system for welfare-related auxiliary products for cashmere goats. Background Technology

[0002] The field of animal welfare product management technology encompasses the monitoring and management of animal health and growth, particularly improving animal welfare through enhanced production systems and environments. This area focuses on improving the production performance of farmed animals, such as reproductive capacity, meat quality, down quality, and milk quality, as well as improving their quality of life and reducing stress responses. The technology involves the design and use of animal welfare aids, including ewe and lamb management, feed additives, environmental control equipment, and disease prevention measures. Through innovative and optimized management practices, animal production performance and health can be effectively improved, ensuring they receive appropriate care in a healthy environment.

[0003] The traditional production and monitoring system for welfare-related products in cashmere goats refers to improving the reproductive efficiency and health of cashmere goats through a series of biotechnologies and management methods. This system primarily uses ovulation-inducing drugs and hormones to promote superovulation in cashmere goats, ensuring the production of multiple eggs within a specific cycle, thereby increasing reproductive efficiency. It achieves superovulation techniques by combining drugs such as CIDR, FSH, and PG to synchronize estrus and induce ovulation, especially by injecting exogenous FSH to address the instability of traditional methods. Traditional methods rely on the combination of CIDR and PG, but the results are not as expected and are easily affected by environmental and operational conditions. By using different FSH injection methods, the ovulation process can be more precisely controlled, improving estrus rates and multiple birth rates.

[0004] Traditional techniques relying on drug-induced ovulation fail to achieve real-time identification and regional classification of hormone level fluctuations, resulting in an inability to promptly distinguish differences in hormone responses among individuals. In practice, this can easily lead to over- or under-intervention, especially in high-density farming scenarios where monitoring data is too concentrated and there is a lack of monitoring and adjustment mechanisms based on individual differences. This can easily lead to resource waste and misjudgment. For example, some cashmere goats with strong hormone responses may experience excessive ovulation after being treated with the same program, while individuals with stable hormone fluctuations may miss the intervention window due to insufficient monitoring frequency. In addition, data management is mainly based on linear archiving without establishing reasonable storage partitions based on the distribution characteristics of monitoring points, resulting in slow historical data retrieval and unclear correlations between data. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a production and monitoring system for welfare-related auxiliary products for cashmere goats.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a production and monitoring system for welfare auxiliary products for cashmere goats includes: The hormone response zoning module is based on the reproductive hormone concentration data detected from cashmere goat serum samples. It identifies the hormone concentration difference and fluctuation range between differential treatment groups, determines the hormone response range to which the test point belongs, divides the response areas into three categories: low fluctuation, medium fluctuation, and high fluctuation, calculates the spatial distribution density of the test points and filters them to obtain the hormone response zoning results. Based on the hormone response zoning results, the injection optimization module sets the monitoring priority, adjusts the monitoring frequency of the measurement points, sets high-fluctuation areas as high-frequency monitoring, adjusts the monitoring interval of medium-fluctuation areas, and only monitors the key time node data of low-fluctuation areas to obtain the measurement point monitoring frequency dataset. The reproductive association analysis module calls the monitoring frequency dataset of the test points, extracts the reproductive hormone characteristic parameters of the test points, classifies them according to hormone characteristics, filters test points within the same hormone characteristic unit, and obtains the test point association control relationship. The estrus trend mapping module analyzes the synchronous changes of hormone fluctuations and estrus trends at the test points based on the correlation and comparison relationships of the test points, determines the correlation degree of the test point data, sets a data correlation degree threshold, filters related test points, and obtains the estrus state change trend of cashmere goats.

[0007] As a further aspect of the present invention, the hormone response zoning results include low fluctuation regions, medium fluctuation regions, and high fluctuation regions; the monitoring frequency dataset includes high-frequency monitoring regions, frequency adjustment regions, and key time node monitoring regions; the monitoring point correlation and control relationships include monitoring point units with similar hormone amplitudes, monitoring point units with consistent hormone fluctuation cycles, and monitoring point units with similar hormone change rates; and the estrus state change trend of cashmere goats includes monitoring point hormone fluctuation trends, monitoring point abnormality trends, data correlation thresholds, and associated monitoring points.

[0008] As a further aspect of the present invention, the hormone response partitioning module includes: The hormone difference calculation submodule measures and records the hormone concentration value at each measuring point based on the reproductive hormone concentration data detected from cashmere goat serum samples, calculates the hormone concentration difference and fluctuation range between measuring points, analyzes the hormone response gradient based on the hormone concentration difference and fluctuation range, and obtains the hormone response value at the measuring point. The response classification submodule calls the hormone response value of the measurement point, identifies the response gradient based on the hormone response change trend of adjacent measurement points, determines the response range to which the measurement point belongs, sets the response classification standard, divides the measurement point into three types of regions: low fluctuation, medium fluctuation, and high fluctuation, analyzes the distribution of measurement points in the region, and generates hormone response zoning results. The density filtering submodule calls the hormone response partitioning results, calculates the spatial density feature values ​​of the measurement points in the response area, filters the density areas, and obtains the hormone response partitioning results.

[0009] As a further aspect of the present invention, the injection optimization module includes: The monitoring priority setting submodule calls the hormone response partitioning results, sets the monitoring priority based on the response area of ​​the measurement point, sorts them according to the degree of response change, and obtains the monitoring priority data of the measurement point. The monitoring frequency adjustment submodule calls the monitoring priority data of the measurement points, adjusts the monitoring frequency according to the priority, sets high-frequency monitoring for high-fluctuation areas, adjusts the monitoring interval for medium-fluctuation areas, and filters key time nodes for monitoring for low-fluctuation areas. It analyzes the changes in monitoring frequency, adjusts the size of the monitoring dataset, and obtains the monitoring frequency dataset of the measurement points.

[0010] As a further aspect of the present invention, the reproductive association analysis module includes: The hormone parameter extraction submodule analyzes the stability of the frequency data based on the monitoring frequency dataset of the measurement points, identifies and removes abnormal data, calculates the hormone amplitude, fluctuation period, and rate of change value for each measurement point, and obtains the hormone parameter set of the measurement points. The measurement point classification submodule calls the hormone parameter set of the measurement point, analyzes the differences in hormone characteristics based on hormone amplitude, fluctuation period and change rate, filters measurement points with similar characteristic values, determines the category to which the measurement point belongs, divides the same hormone feature units, optimizes the classification boundary, and obtains hormone feature unit distribution data. The correlation control generation submodule calls the hormone feature unit distribution data, identifies the correlation between test points within the unit, establishes a correlation model for the test points, identifies the connection between test points through correlation analysis, and obtains the correlation control relationship between test points.

[0011] As a further aspect of the present invention, the estrus trend mapping module includes: The measuring point trend analysis submodule calls the correlation and control relationship of the measuring points, analyzes the hormone fluctuations and estrus trends of the measuring points, extracts time series data, and obtains the trend change characteristics of the measuring points; The data correlation judgment submodule calls the trend change characteristics of the measuring points, analyzes the synchronicity between hormone fluctuations and estrus changes at the measuring points, calculates the data correlation of the measuring points, sets a data correlation threshold, filters the data correlation of the measuring points, removes measuring points with low correlation, optimizes the data matching relationship, and obtains the data correlation filtering results. The estrus state trend filtering submodule calls the measurement point association filtering results, filters the trend change data of the associated measurement points, identifies the magnitude of the estrus state change in cashmere goats, and obtains the estrus state change trend of cashmere goats.

[0012] As a further aspect of the present invention, the system also includes a secure storage module: Based on the estrus state change trend of the cashmere goats, the secure storage module extracts the spatial distribution information of the monitoring points, identifies the corresponding positions between the monitoring points, divides the storage area according to the distribution characteristics, adjusts the storage partition layout, and obtains a secure storage solution for auxiliary product production and monitoring data. The auxiliary product production and monitoring data security storage scheme includes storage area division, storage partition layout adjustment, extraction of spatial distribution information of measurement points, and identification of corresponding locations of measurement points.

[0013] As a further aspect of the present invention, the secure storage module includes: The spatial distribution extraction submodule calls the estrus state change trend of the cashmere goats, extracts the spatial distribution data of the monitoring points, identifies the corresponding location of the measuring points, and obtains the spatial distribution information of the measuring points; The storage area partitioning submodule calls the spatial distribution information of the measurement points, identifies the distribution characteristics based on the corresponding locations of the measurement points, filters dense areas of measurement points, partitions storage areas, sets differentiated storage area boundaries, adjusts the balance of measurement point distribution, and obtains the storage area partitioning results. The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes the measurement point storage data mapping, and obtains a secure storage solution for auxiliary product production and monitoring data.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, different fluctuation ranges are dynamically divided based on serum hormone concentration data of cashmere goats, strengthening the key tracking of areas with abnormal hormone levels and improving the timeliness of response to changes in reproductive status. By adjusting the monitoring frequency and optimizing the data collection rhythm, invalid information interference is reduced, and the monitoring resources are rationally allocated. By establishing correlations between monitoring points through the classification and comparison of hormone characteristics, the accuracy of identifying hormone change trends is enhanced. Furthermore, by combining data from highly correlated monitoring points to analyze estrus synchronization changes, the reliability of multiple birth prediction and the accuracy of ovulation intervention timing are improved. Storage partitions are defined based on spatial distribution, and the information archiving logic in the auxiliary product production process is optimized, enhancing the integrity and location efficiency of secure data storage. Overall, precise control and risk avoidance of the reproductive management process are achieved, reducing the intervention failure rate caused by misjudgment of hormone fluctuations. Attached Figure Description

[0015] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the hormone response partitioning module in this invention; Figure 3 This is a flowchart of the injection optimization module in this invention; Figure 4This is a flowchart of the reproductive association analysis module in this invention; Figure 5 This is a flowchart of the estrus trend mapping module in this invention; Figure 6 This is a flowchart of the secure storage module in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 A production and monitoring system for cashmere goat welfare supplements includes: The hormone response zoning module is based on the reproductive hormone concentration data detected from cashmere goat serum samples. It identifies the hormone concentration difference and fluctuation range between differential treatment groups, determines the hormone response range to which the test point belongs, divides the response areas into three categories: low fluctuation, medium fluctuation, and high fluctuation, calculates the spatial distribution density of the test points and filters them to obtain the hormone response zoning results. The injection optimization module sets monitoring priorities based on hormone response zoning results, adjusts the monitoring frequency of measurement points, sets high-fluctuation areas as high-frequency monitoring, adjusts the monitoring interval of medium-fluctuation areas, and only monitors key time node data of low-fluctuation areas to obtain measurement point monitoring frequency dataset. The reproductive association analysis module calls the monitoring frequency dataset of the test points, extracts the reproductive hormone characteristic parameters of the test points, classifies them according to hormone characteristics, filters test points within the same hormone characteristic unit, and obtains the test point association control relationship. The estrus trend mapping module analyzes the synchronous changes of hormone fluctuations and estrus trends at the measurement points based on the correlation and control relationship of the measurement points, judges the correlation degree of the measurement point data, sets the data correlation degree threshold, filters the related measurement points, and obtains the estrus status change trend of cashmere goats. The secure storage module extracts spatial distribution information of monitoring points based on the estrus state change trend of cashmere goats, identifies the corresponding positions between monitoring points, divides storage areas according to distribution characteristics, adjusts the storage partition layout, and obtains a secure storage solution for auxiliary product production and monitoring data.

[0019] The hormone response zoning results include low-fluctuation, medium-fluctuation, and high-fluctuation regions. The monitoring frequency dataset includes high-frequency monitoring regions, regions with adjusted monitoring frequencies, and key time node monitoring regions. The correlation and comparison relationships of monitoring points include monitoring point units with similar hormone amplitudes, monitoring point units with consistent hormone fluctuation cycles, and monitoring point units with similar hormone change rates. The estrus status change trend of cashmere goats includes the hormone fluctuation trend of monitoring points, the trend of abnormal points of monitoring points, the data correlation threshold, and the associated monitoring points. The data security storage scheme for auxiliary product production and monitoring includes storage area division, storage partition layout adjustment, extraction of spatial distribution information of monitoring points, and identification of corresponding locations of monitoring points.

[0020] Please see Figure 2 The hormone response partitioning module includes: The hormone difference calculation submodule measures and records the hormone concentration value at each measuring point based on the reproductive hormone concentration data detected from cashmere goat serum samples, calculates the hormone concentration difference and fluctuation range between measuring points, analyzes the hormone response gradient based on the hormone concentration difference and fluctuation range, and obtains the hormone response value at the measuring point. Based on reproductive hormone concentration data from cashmere goat serum samples, 100 adult cashmere goats at different physiological stages were randomly selected from the farm. Venous serum samples were collected periodically, and enzyme-linked immunosorbent assay (ELISA) kits were used to quantitatively detect four reproductive hormones: progesterone, estradiol, luteinizing hormone (LH), and follicle-stimulating hormone (FSH). Hormone concentration values ​​at each measurement point were recorded. For example, at a specific time point T1, the progesterone concentration of cashmere goat individual A (measurement point P1) was measured at 5.2 ng / mL; at time point T2, the same cashmere goat individual A (measurement point P1) was measured again, and the value was 6.8 ng / mL. For each measurement point, hormone concentration measurements were performed every 2 hours for 24 consecutive hours. The difference and fluctuation range of hormone concentration between measurement points were calculated. The difference in progesterone concentration at measurement point P1 between T1 and T2 was [value missing]. ng / mL, fluctuation range is Hormone concentrations were measured in ng / mL. The hormone response gradient was analyzed based on the difference in hormone concentration and the range of fluctuation. A low response gradient was defined as a difference in progesterone concentration less than 1.0 ng / mL and a range of fluctuation less than 1.5 ng / mL; a medium response gradient was defined as a difference in progesterone concentration between 1.0 ng / mL and 3.0 ng / mL and a range of fluctuation between 1.5 ng / mL and 4.0 ng / mL; and a high response gradient was defined as a difference in progesterone concentration greater than 3.0 ng / mL or a range of fluctuation greater than 4.0 ng / mL. The hormone response values ​​at the measurement points were then obtained.

[0021] The response classification submodule calls the hormone response values ​​of the measurement points, identifies the response gradient based on the changing trend of hormone response of adjacent measurement points, determines the response range to which the measurement point belongs, sets the response classification criteria, divides the measurement points into three categories of regions: low fluctuation, medium fluctuation, and high fluctuation, analyzes the distribution of measurement points in the region, and generates hormone response zoning results. The hormone response values ​​of the measurement points are retrieved. For example, for measurement point P1, its progesterone hormone response value is a medium response gradient, and for measurement point P2, its progesterone hormone response value is a high response gradient. The response gradient is identified based on the trend of hormone response changes of adjacent measurement points. For example, if the adjacent measurement points P3 (high response) and P4 (high response) of measurement point P1 (medium response) both show high responses, then P1 is identified as being in the transition region from low to high response. The response range of the measurement point is determined. Using the sliding window method, the window size is set to 3 measurement points. In the cashmere goat breeding area, the hormone response values ​​of 3 consecutive measurement points (e.g., P1, P2, P3) are analyzed. If 2 or more of the 3 measurement points show a high response, then the window is determined. The region is defined as a high-fluctuation response range. Response classification criteria are established to divide the test points into three categories: low-fluctuation, medium-fluctuation, and high-fluctuation. Specifically: when the hormone response values ​​at all test points are less than the set upper limit for low fluctuation (e.g., progesterone response value less than 1.5 ng / mL), it is classified as a low-fluctuation region; when the hormone response values ​​at test points are between the upper limit for low fluctuation and the lower limit for high fluctuation (e.g., progesterone response value between 1.5 ng / mL and 4.0 ng / mL), it is classified as a medium-fluctuation region; and when the hormone response values ​​at test points are greater than the set lower limit for high fluctuation (e.g., progesterone response value greater than 4.0 ng / mL), it is classified as a high-fluctuation region. The distribution of test points within each region is analyzed to generate hormone response zoning results.

[0022] The density screening submodule calls the hormone response partitioning results, using the following formula: ; Calculate the spatial density characteristic values ​​of the measurement points in the response region, filter the density regions, and obtain the hormone response zoning results; in, This represents the spatial density characteristic value of the measurement points in the response area. This represents the total number of measuring points within the response area. Representing the Spatial coordinates of each measuring point Represents the average spatial location of all measuring points. Representing the Hormone response intensity at each measurement point Representing the The relative measurement error of each measuring point; The spatial density characteristic value of the measurement points in the response area represents the spatial distribution density and coverage of the measurement points in a certain response area. It is an important indicator for measuring the uniformity of measurement point layout and spatial representativeness. The higher the density characteristic value, the denser the measurement point coverage in the area, and the more comprehensive the local structural response information obtained; conversely, there are monitoring blind spots. To retrieve hormone response partitioning results, for example, if a hormone response partitioning result shows that the high-fluctuation region includes measurement points P10, P11, P12, P13, and P14, the formula is: Calculate the spatial density characteristic values ​​of the measurement points in the response region; in, The spatial density characteristic value of the measurement points in the response area reflects the degree of aggregation of the measurement points and the comprehensive density of hormone response characteristics in the area. The total number of measurement points within the response region represents the number of discrete measurement points contained within the region where the density feature value is currently calculated. Representing the The spatial coordinates of each measuring point are represented by two-dimensional coordinates, indicating the specific location of the measuring point within the farm. Representing the The hormone response intensity at each measuring point indicates the degree of drastic change in hormone concentration at that measuring point. It is a comprehensive indicator of the normalized difference or fluctuation range of hormone concentration. Representing the The relative measurement error of each measuring point reflects the reliability of the hormone detection data at that measuring point, and is a percentage error determined by the accuracy of the testing equipment and the standardization of operation. The advantage of this formula lies in its comprehensive assessment of the spatial clustering of measuring points within the response region, the intensity of hormone activity, and data reliability by incorporating the spatial location of the measuring points, the intensity of hormone response, and relative measurement error. This allows the density characteristic values ​​to more fully reflect the actual distribution of the measuring points. For example, the total number of measuring points in a high-fluctuation region... The spatial coordinates of the measuring points (unit: meters) are as follows: , , , , ; The average value of the spatial locations of all measuring points Calculation: ; Hormone response intensity at each measuring point (For example, normalized hormone fluctuation values, ranging from 0 to 10): , , , , The relative measurement error of each measuring point (For example, ELISA detection error percentage, ranging from 0.01 to 0.05): , , , , ; Calculate the spatial density characteristic value of the measuring point : ; calculate Spatial distance: ; ; ; ; ; calculate : , , , , ; Substitute into the formula to calculate : ; Filter density regions, set a density threshold of 20, and if the calculated density is... If the value is greater than 20, the region is determined to be a high-density region, and the hormone response partitioning results are obtained. For example, the calculation results... The result indicates that the spatial density characteristic value of the measurement points in the high-fluctuation area is relatively high. This result shows that the measurement points in the high-fluctuation area are densely distributed and the hormone response is intense. The data is highly reliable and provides an important reference for setting the monitoring priority in the future.

[0023] Please see Figure 3 The injection optimization module includes: The monitoring priority setting submodule calls the hormone response partitioning results, sets the monitoring priority based on the response area of ​​the measurement point, sorts them according to the degree of response change, and obtains the monitoring priority data of the measurement point. Using the hormone response zoning results, the cashmere goat farming area was divided into low-fluctuation, medium-fluctuation, and high-fluctuation zones. Monitoring priorities were set based on the measurement point's response zone: measurement points in high-fluctuation zones were assigned Level 1 monitoring priority; those in medium-fluctuation zones were assigned Level 2 priority; and those in low-fluctuation zones were assigned Level 3 priority. Priority was determined by the degree of response change, with measurement points in high-fluctuation zones (e.g., P10, P11, P12, P13, P14) having higher priority than those in medium-fluctuation zones, and vice versa. Within the same priority level, priority was further determined based on the intensity of the hormone response at the measurement point. The values ​​are sorted from high to low to generate monitoring priority data for the measuring points as shown in Table 1. Table 1: Monitoring Priority Table for Measurement Points As shown in Table 1, measurement point P13 was set as the first-level monitoring priority because it is located in a high-fluctuation area and has the highest hormone response intensity.

[0024] The monitoring frequency adjustment submodule calls the monitoring priority data of the measurement points, adjusts the monitoring frequency according to the priority, sets high-frequency monitoring for high-fluctuation areas, adjusts the monitoring interval for medium-fluctuation areas, and filters key time nodes for monitoring for low-fluctuation areas. It analyzes the changes in monitoring frequency, adjusts the size of the monitoring dataset, and obtains the monitoring frequency dataset of the measurement points. Based on the monitoring priority data of the measurement points shown in Table 1, measurement point P13 is a first-level monitoring priority, measurement point P21 is a second-level monitoring priority, and measurement point P31 is a third-level monitoring priority. The monitoring frequency is adjusted according to the priority. High-frequency monitoring is set for high-fluctuation areas; for example, for a first-level monitoring priority measurement point (such as P13), monitoring is set to once every 2 hours. The monitoring interval is adjusted for medium-fluctuation areas; for example, for a second-level monitoring priority measurement point (such as P21), monitoring is set to once every 6 hours. For low-fluctuation areas, key time nodes are selected for monitoring; for example, for a third-level monitoring priority measurement point (such as P31), monitoring is set to once a day. The changes in monitoring frequency are analyzed. For example, by observing the adjustment of the monitoring frequency over a week, it is found that the monitoring frequency in high-fluctuation areas increased from 4 times per day to 12 times per day. The size of the monitoring dataset is adjusted accordingly. For example, based on the adjustment of the monitoring frequency, if the original monitoring dataset contained 1000 data points (100 measurement points monitored once a day), and the monitoring frequency in high-fluctuation areas increases by 3 times, the size of the monitoring dataset will increase by at least 2000 data points. The measurement point monitoring frequency dataset is then obtained.

[0025] Please see Figure 4 The reproductive association analysis module includes: The hormone parameter extraction submodule analyzes the stability of the frequency data based on the monitoring frequency dataset of the measurement points, identifies and removes abnormal data, calculates the hormone amplitude, fluctuation period, and rate of change for each measurement point, and obtains the hormone parameter set of the measurement points. Based on a monitoring frequency dataset, for example, for progesterone concentration data monitored every 2 hours at monitoring point P10 within a week, the stability of the frequency data is analyzed. A moving average method is used to smooth the progesterone concentration data for 7 consecutive days, and its standard deviation is calculated. Outliers are identified and removed. If a data point is outside twice the standard deviation range, it is considered an outlier and removed. For example, if the progesterone concentration suddenly spikes to 20 ng / mL on a certain day, far exceeding the normal range (5-10 ng / mL), it is identified as an outlier and removed. The standard deviation is calculated for each... The hormone amplitude, fluctuation period, and rate of change values ​​for each measurement point are obtained. For example, for measurement point P10, the main frequency of the progesterone concentration data after removing outliers is extracted as the fluctuation period (e.g., the period is 24 hours) by performing Fourier transform on the data. The peak-to-trough difference is calculated as the hormone amplitude (e.g., the amplitude is 3.5 ng / mL). The slope of the concentration change at adjacent time points is calculated by linear regression as the rate of change (e.g., the rate of change is 0.2 ng / mL / hour). The hormone parameter set for each measurement point is obtained, which includes the hormone amplitude, fluctuation period, and rate of change for each measurement point.

[0026] The measurement point classification submodule calls the hormone parameter set of the measurement point, analyzes the differences in hormone characteristics based on hormone amplitude, fluctuation period and change rate, filters measurement points with similar characteristic values, determines the category to which the measurement point belongs, divides the same hormone feature units, optimizes the classification boundary, and obtains the distribution data of hormone feature units. The system calls upon a set of hormone parameters for each measurement point. For example, measurement point P10 has a progesterone amplitude of 3.5 ng / mL, a period of 24 hours, and a rate of change of 0.2 ng / mL / hour; measurement point P11 has a progesterone amplitude of 3.8 ng / mL, a period of 24 hours, and a rate of change of 0.25 ng / mL / hour. Based on the hormone amplitude, fluctuation period, and rate of change, the system analyzes the differences in hormone characteristics. It quantifies the differences in hormone characteristics between measurement points by calculating the Euclidean distance of each parameter, and filters measurement points with similar characteristic values. A threshold for similar characteristic values ​​is set; for example, if the Euclidean distance between two measurement points is less than 0.5, their characteristic values ​​are considered similar, and the measurement points are assigned to a category. If the characteristic values ​​of multiple measurement points are close to a preset class center... The test points are then grouped into the same category and divided into the same hormone characteristic units. For example, all test points with progesterone amplitude between 3.0-4.0 ng / mL, cycle between 23-25 ​​hours, and change rate between 0.15-0.25 ng / mL / hour are divided into a "normal estrous cycle characteristic unit". The classification boundary is optimized by adjusting the class center and classification boundary through iterative clustering algorithms (such as K-means) to maximize the similarity of test points within each category and minimize the similarity of test points between different categories. The distribution data of hormone characteristic units is obtained. For example, three main hormone characteristic units are finally obtained: normal estrous cycle unit, proestrus unit, and mesestrus unit, and the unit to which each test point belongs is recorded.

[0027] The correlation control generation submodule calls the hormone feature unit distribution data, identifies the correlation between test points within the unit, establishes a correlation model for the test points, identifies the connection between test points through correlation analysis, and obtains the correlation control relationship between test points. By utilizing hormone characteristic unit distribution data, for example, data showing that test points P10 and P11 belong to the "normal estrous cycle unit," while test point P15 belongs to the "proestrous unit," the correlation between test points within each unit is identified. For instance, within the "normal estrous cycle unit," the synchronicity of progesterone concentration changes over time in P10 and P11 is analyzed using the Pearson correlation coefficient. The calculated correlation coefficient is 0.92, indicating a strong correlation between the two. A correlation model for the test points is established, and a Bayesian network model is used to construct the causal and dependency relationships between the test points. The connections between test points are identified through correlation analysis. For example, analysis of the correlation model reveals that an increase in progesterone concentration in P10 is accompanied by an increase in estradiol concentration in P11, indicating a positive correlation between the two. Correlation control relationships are obtained, for example, a "progesterone-estradiol positive correlation" correlation control relationship is found between P10 and P11.

[0028] Please see Figure 5 The estrus trend mapping module includes: The measuring point trend analysis submodule calls the measuring point correlation and control relationship, analyzes the hormone fluctuation and estrus trend of the measuring point, extracts time series data, and obtains the trend change characteristics of the measuring point; By invoking correlation and control relationships at the monitoring points—for example, the correlation and control relationships show a strong positive correlation between progesterone fluctuations in P10 and estradiol fluctuations in P11—the relationship between hormone fluctuations at the monitoring points and estrus trends can be analyzed. Using time series analysis methods, such as the ARIMA model, historical progesterone concentration data for P10 can be predicted. This is combined with clinical data of cashmere goats (such as vulvar redness and swelling, acceptance of mounting, etc.) to simultaneously analyze their estrus trends. Time series data can be extracted; for example, the average daily progesterone concentration and daily estrus behavior score for P10 over the past 30 days can be extracted to obtain the trend change characteristics of the monitoring points. For instance, the progesterone concentration of P10 showed a continuous upward trend over the past week, and the estrus behavior score also increased synchronously from 1 to 5, indicating that it is entering estrus.

[0029] The data correlation judgment submodule calls the trend change characteristics of the measuring points to analyze the synchronicity between hormone fluctuations and estrus changes at the measuring points, using the formula: ; Calculate the correlation degree of measurement point data, set a data correlation degree threshold, filter the correlation degree of measurement point data, remove measurement points with low correlation degree, optimize the data matching relationship, and obtain the measurement point correlation filtering results; in, Represents the total number of data points. This represents the amplitude of hormone fluctuation at the i-th measurement point. This represents the amplitude of estrus changes at the i-th measuring point. This represents the error term for the i-th measurement point. The correlation degree of the measurement point data; The correlation of measurement point data reflects the similarity and correlation between various measurement points in dynamic response or signal characteristics. A higher correlation indicates that there is a certain redundancy between measurement points, and the collected information is repetitive or similar. A lower correlation indicates that the response differences between measurement points are large, and richer structural state information can be provided. In measurement point optimization, reasonable control of data correlation helps to remove redundant information and improve data utilization efficiency. The trend change characteristics of the measuring points are invoked. For example, the progesterone fluctuation amplitude at measuring point P10 is 3.5 ng / mL, and the amplitude of estrus behavior changes is 4 points (from 1 to 5 points). The formula is as follows: Calculate the correlation degree of the measurement point data; in, The total number of data points represents the number of time-series data points used to calculate the correlation. Representing the The amplitude of hormone fluctuations at each measurement point represents the value at the 1st measurement point. The range of hormone concentration changes measured at each time point Representing the The amplitude of estrus changes at the ____ measurement point indicates the ____ value at the ____ point. The degree of change in estrus behavior observed at each time point is a quantified numerical value. Representing the The error term for each measurement point includes the measurement error and the impact of unconsidered random factors on the data; The advantage of this formula lies in its ability to quantify the synchronicity and correlation between hormone fluctuation amplitudes and estrus change amplitudes by calculating the relative difference and taking into account measurement errors. This provides a quantitative basis for eliminating measurement points with low correlation. A data correlation threshold can be set, for example, to 0.2, to filter the correlation of measurement points and eliminate those with low correlation. If the value is greater than the threshold, the data for that measurement point is retained; if... If the value is less than or equal to the threshold, the data for that measurement point is removed to optimize the data matching relationship. For example, by removing measurement points with low correlation, only data that are highly synchronized with hormone fluctuations and estrus trends are retained, improving the accuracy of subsequent analysis and obtaining the measurement point correlation screening results. This is done to calculate the correlation degree of the measurement point data. The hormone fluctuation amplitude of P10 over 5 consecutive days was selected. And the amplitude of estrus changes Data, and corresponding error terms The specific data is shown in Table 2: Table 2: Data on P10 hormone levels and estrus. As shown in Table 2, the amplitude of hormone fluctuations This represents the daily peak-to-trough difference in progesterone concentration, and the amplitude of estrus variation. This represents the daily change in estrus behavior scores (e.g., the difference between the score from the previous day and the score on the current day), with an error term. It is derived from a comprehensive assessment of laboratory measurement errors and clinical observation errors, calculating the correlation between measurement point data. : , ; ; ; ; ; ; Calculation results The result is compared with a preset threshold of 0.2. The results indicate that the correlation between hormone fluctuations and estrus changes at measuring point P10 is low. This suggests that the data correlation at measuring point P10 did not reach the set threshold, indicating significant noise or weak synchronicity between the two. Therefore, P10 needs to be removed to avoid negatively impacting the accuracy of subsequent estrus trend prediction.

[0030] The estrus state trend filtering submodule calls the measurement point association filtering results, filters the trend change data of the associated measurement points, identifies the magnitude of the estrus state change in cashmere goats, and obtains the estrus state change trend of cashmere goats; The system retrieves the correlation and filtering results of the measurement points. For example, after judging the correlation degree, the correlation degree of measurement point P11 reaches the threshold and is retained, while P10 is removed. The system filters the trend change data of the associated measurement points. For example, it retains only the time series data of progesterone fluctuation and estrus trend of P11. The system identifies the magnitude of changes in the estrus state of cashmere goats. By performing curve fitting on the progesterone concentration and estrus behavior score of P11, the system identifies its peak, trough, and the slope of rise and fall, and judges the magnitude of the estrus state. For example, if the progesterone concentration rises from 3.0 ng / mL to 8.0 ng / mL in 24 hours, and the estrus behavior score rises from 2 points to 8 points, the magnitude of the change in the estrus state is judged to be "high intensity rise". The system obtains the trend of changes in the estrus state of cashmere goats. For example, the system finally obtains the trend that cashmere goat individual A (the individual where measurement point P11 is located) is in the "rapid progress of proestrus".

[0031] Please see Figure 6 The secure storage module includes: The spatial distribution extraction submodule calls the trend of estrus status changes in cashmere goats, extracts spatial distribution data of monitoring points, identifies the corresponding location of the measuring points, and obtains spatial distribution information of the measuring points; The system retrieves the estrus state change trend of cashmere goats. For example, if the estrus state change trend of cashmere goat individual A is "rapid progress in proestrus", and the spatial coordinates of the measuring point P11 where this individual is located are (12, 22), the system extracts the spatial distribution data of the monitoring points. It extracts the precise GPS coordinates of all measuring points from the farm's GIS map. For example, in addition to P11, the coordinates of measuring points such as P12 (11, 21) and P13 (15, 25) are also extracted. The system identifies the corresponding positions of the measuring points and matches the extracted GPS coordinates with the actual cashmere goat individual IDs. For example, it confirms that P11 corresponds to cashmere goat individual A and P12 corresponds to cashmere goat individual B. The system obtains the spatial distribution information of the measuring points and generates a list containing all measuring point IDs and their corresponding spatial coordinates.

[0032] The storage area partitioning submodule calls the spatial distribution information of the measurement points, identifies the distribution characteristics based on the corresponding locations of the measurement points, filters dense areas of measurement points, partitions storage areas, sets differentiated storage area boundaries, adjusts the balance of measurement point distribution, and obtains the storage area partitioning results. The system retrieves spatial distribution information of measurement points, such as P11(12, 22), P12(11, 21), P13(15, 25), P14(16, 24), and P10(10, 20). Based on the corresponding locations of the measurement points, it identifies distribution characteristics and, by calculating the Euclidean distance between adjacent measurement points, identifies areas where measurement points cluster. It then filters densely populated areas, setting the criterion for dense areas as: containing at least 3 measurement points within a radius of 5 meters. Areas meeting this criterion are selected as densely populated areas and then divided into storage regions. For example... The farm is divided into multiple grids, each corresponding to a storage area. Based on the division results of densely populated areas of measuring points, the grids with a large amount of measuring point data are designated as high storage areas. Differentiated storage area boundaries are set. For example, the boundary of the high storage area is set to be 20 meters long and 20 meters wide, the medium storage area is 50 meters long and 50 meters wide, and the low storage area is 100 meters long and 100 meters wide. The balance of measuring point distribution is adjusted. If the number of measuring points in a certain storage area is too small, it is adjusted by merging adjacent areas or expanding the boundary to obtain the storage area division results.

[0033] The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes the mapping of measurement point storage data, and obtains a secure storage solution for auxiliary product production and monitoring data. The storage area partitioning results are retrieved. For example, the storage area partitioning results show that the farm is divided into high storage area A (measurement points P11, P12), medium storage area B (measurement points P10, P13), and low storage area C (the remaining measurement points). The storage partition layout is adjusted. For example, high storage area A is mapped to a high-performance server cluster, medium storage area B is mapped to a medium-performance server, and low storage area C is mapped to a low-cost storage device. The measurement point storage data mapping is optimized to ensure that high-frequency access and high-priority data are stored on storage media with faster access speeds and higher reliability, thereby obtaining a secure storage solution for auxiliary product production and monitoring data.

[0034] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A production and monitoring system for welfare auxiliary products for cashmere goats, characterized in that, The system includes: The hormone response zoning module is based on the reproductive hormone concentration data detected from cashmere goat serum samples. It identifies the hormone concentration difference and fluctuation range between differential treatment groups, determines the hormone response range to which the test point belongs, divides the response areas into three categories: low fluctuation, medium fluctuation, and high fluctuation, calculates the spatial distribution density of the test points and filters them to obtain the hormone response zoning results. Based on the hormone response zoning results, the injection optimization module sets the monitoring priority, adjusts the monitoring frequency of the measurement points, sets high-fluctuation areas as high-frequency monitoring, adjusts the monitoring interval of medium-fluctuation areas, and only monitors the key time node data of low-fluctuation areas to obtain the measurement point monitoring frequency dataset. The reproductive association analysis module calls the monitoring frequency dataset of the test points, extracts the reproductive hormone characteristic parameters of the test points, classifies them according to hormone characteristics, filters test points within the same hormone characteristic unit, and obtains the test point association control relationship. The estrus trend mapping module analyzes the synchronous changes of hormone fluctuations and estrus trends at the test points based on the correlation and comparison relationships of the test points, determines the correlation degree of the test point data, sets a data correlation degree threshold, filters related test points, and obtains the estrus state change trend of cashmere goats.

2. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 1, characterized in that, The hormone response zoning results include low fluctuation regions, medium fluctuation regions, and high fluctuation regions. The monitoring frequency dataset for measuring points includes high-frequency monitoring regions, regions with adjusted monitoring frequencies, and monitoring regions at key time nodes. The correlation and control relationships of measuring points include measuring point units with similar hormone amplitudes, measuring point units with consistent hormone fluctuation cycles, and measuring point units with similar hormone change rates. The estrus status change trend of cashmere goats includes the hormone fluctuation trend of measuring points, the trend of abnormal points of measuring points, the data correlation threshold, and related measuring points.

3. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 1, characterized in that, The hormone response partitioning module includes: The hormone difference calculation submodule measures and records the hormone concentration value at each measuring point based on the reproductive hormone concentration data detected from cashmere goat serum samples, calculates the hormone concentration difference and fluctuation range between measuring points, analyzes the hormone response gradient based on the hormone concentration difference and fluctuation range, and obtains the hormone response value at the measuring point. The response classification submodule calls the hormone response value of the measurement point, identifies the response gradient based on the hormone response change trend of adjacent measurement points, determines the response range to which the measurement point belongs, sets the response classification standard, divides the measurement point into three types of regions: low fluctuation, medium fluctuation, and high fluctuation, analyzes the distribution of measurement points in the region, and generates hormone response zoning results. The density filtering submodule calls the hormone response partitioning results, calculates the spatial density feature values ​​of the measurement points in the response area, filters the density areas, and obtains the hormone response partitioning results.

4. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 3, characterized in that, The injection optimization module includes: The monitoring priority setting submodule calls the hormone response partitioning results, sets the monitoring priority based on the response area of ​​the measurement point, sorts them according to the degree of response change, and obtains the monitoring priority data of the measurement point. The monitoring frequency adjustment submodule calls the monitoring priority data of the measurement points, adjusts the monitoring frequency according to the priority, sets high-frequency monitoring for high-fluctuation areas, adjusts the monitoring interval for medium-fluctuation areas, and filters key time nodes for monitoring for low-fluctuation areas. It analyzes the changes in monitoring frequency, adjusts the size of the monitoring dataset, and obtains the monitoring frequency dataset of the measurement points.

5. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 4, characterized in that, The reproductive association analysis module includes: The hormone parameter extraction submodule analyzes the stability of the frequency data based on the monitoring frequency dataset of the measurement points, identifies and removes abnormal data, calculates the hormone amplitude, fluctuation period, and rate of change value for each measurement point, and obtains the hormone parameter set of the measurement points. The measurement point classification submodule calls the hormone parameter set of the measurement point, analyzes the differences in hormone characteristics based on hormone amplitude, fluctuation period and change rate, filters measurement points with similar characteristic values, determines the category to which the measurement point belongs, divides the same hormone feature units, optimizes the classification boundary, and obtains hormone feature unit distribution data. The correlation control generation submodule calls the hormone feature unit distribution data, identifies the correlation between test points within the unit, establishes a correlation model for the test points, identifies the connection between test points through correlation analysis, and obtains the correlation control relationship between test points.

6. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 5, characterized in that, The estrus trend mapping module includes: The measuring point trend analysis submodule calls the correlation and control relationship of the measuring points, analyzes the hormone fluctuations and estrus trends of the measuring points, extracts time series data, and obtains the trend change characteristics of the measuring points; The data correlation judgment submodule calls the trend change characteristics of the measuring points, analyzes the synchronicity between hormone fluctuations and estrus changes at the measuring points, calculates the data correlation of the measuring points, sets a data correlation threshold, filters the data correlation of the measuring points, removes measuring points with low correlation, optimizes the data matching relationship, and obtains the data correlation filtering results. The estrus state trend filtering submodule calls the measurement point association filtering results, filters the trend change data of the associated measurement points, identifies the magnitude of the estrus state change in cashmere goats, and obtains the estrus state change trend of cashmere goats.

7. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 1, characterized in that, The system also includes a secure storage module: Based on the estrus state change trend of the cashmere goats, the secure storage module extracts the spatial distribution information of the monitoring points, identifies the corresponding positions between the monitoring points, divides the storage area according to the distribution characteristics, adjusts the storage partition layout, and obtains a secure storage solution for auxiliary product production and monitoring data. The auxiliary product production and monitoring data security storage scheme includes storage area division, storage partition layout adjustment, extraction of spatial distribution information of measurement points, and identification of corresponding locations of measurement points.

8. The production and monitoring system for cashmere goat welfare auxiliary products according to claim 7, characterized in that, The secure storage module includes: The spatial distribution extraction submodule calls the estrus state change trend of the cashmere goats, extracts the spatial distribution data of the monitoring points, identifies the corresponding location of the measuring points, and obtains the spatial distribution information of the measuring points; The storage area partitioning submodule calls the spatial distribution information of the measurement points, identifies the distribution characteristics based on the corresponding locations of the measurement points, filters dense areas of measurement points, partitions storage areas, sets differentiated storage area boundaries, adjusts the balance of measurement point distribution, and obtains the storage area partitioning results. The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes the measurement point storage data mapping, and obtains a secure storage solution for auxiliary product production and monitoring data.

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