Cow consumption health index analysis method based on artificial intelligence

Abstract

The invention relates to the field of consumption cattle health analysis, and discloses a consumption cattle health index analysis method based on artificial intelligence, and the method comprises the steps: firstly collecting the medical record information of consumption cattle, classifying the consumption cattle, and binding a label to establish a consumption cattle illness condition portrait; the method comprises the following steps: performing regional monitoring on a consumption cattle herd, tracking and recording the motion trail of the consumption cattle, and establishing a consumption cattle herd-motion state active curve with different labels; the method comprises the following steps: monitoring a consumption cattle herd in different periods, and establishing a consumption cattle physical state score curve; monitoring the sleep condition of the consumption cattle to obtain rest indexes of consumption cattle herds with different labels; identifying the stress state and the early symptom of the disease of the cattle herd, and generating a disease pre-estimation map of the cattle herd; integrating the data to obtain a consumption cattle health index analysis model, and performing real-time analysis on the health state of the consumption cattle; the problems that an existing livestock health supervision technology cannot be suitable for the cattle herd, and an existing health analysis technology does not consider cattle characteristics, so that health analysis is inaccurate and analysis is not systematic are solved.

Description

Technical Field

[0001] The present invention relates to the field of yak health analysis, and in particular to an artificial intelligence-based yak health index analysis method. Background Art

[0002] In yak breeding practice, since yaks are free-range animals, the monitoring of the yaks' health status mostly relies on the farmers' experience and regular manual inspections. This method is not only labor-intensive but also inefficient. It is difficult to achieve real-time monitoring of the yaks' health status and more professional analysis, especially in large-scale yak herds. It can easily lead to delayed treatment of diseases and even the spread of diseases among the yak herds.

[0003] Although there are some animal health monitoring products on the market, such as smart collars or ear tags, which can monitor activity levels, feeding behavior and other information to a certain extent, most existing solutions cannot provide comprehensive health assessments. In addition, due to the particularity of yaks, existing animal health monitoring products and methods are not suitable for yak herds. They lack adaptability and it is difficult to adjust the assessment criteria according to the specific conditions of the yak herds. It is impossible to achieve both high accuracy and high versatility for yak herds. Therefore, there is still much room for improvement in universality and accuracy in practical applications. Summary of the invention

[0004] The present invention aims to provide an artificial intelligence-based yak health index analysis method to solve the problem that existing livestock health supervision technology cannot be applied to yak herds, and existing health analysis technology does not take into account the characteristics of yaks, resulting in inaccurate and unsystematic health analysis.

[0005] In order to achieve the above object, the present invention provides the following method: The present invention provides a yak health index analysis method based on artificial intelligence: S1: Collect medical records of yaks, classify yak groups, and bind labels to create yak disease profiles; S2: Divide the yak herd into regional monitoring areas, track and record the movement trajectories of the yaks, and establish the activity curve of the movement status of yak herds with different labels based on the regional monitoring status analysis; S3: Monitor the yak herd in different time periods, collect the body conditions of the yak herd in different time periods according to the life characteristics of the yak herd, and establish a body condition score curve of the yak; S4: monitoring the sleep status of the yaks, collecting the total sleep duration of the yak groups with different labels through artificial intelligence, and obtaining the rest index of the yak groups with different labels through the total sleep duration of the yak groups with different labels; S5: Use composite event processing technology to identify the stress state and early signs of disease of yak herds with different labels, and generate a disease prediction map of yak herds; S6: Integrate the activity curve of the yak groups with different labels-exercise status, the yak body condition score curve, the rest index of the yak groups with different labels and the yak group disease prediction map to obtain a yak health index analysis model, and perform real-time analysis on the health status of the yaks.

[0006] Preferably, the step of collecting the medical record information of yaks, classifying the yak groups, and binding labels to establish the disease portrait of the yak includes: collecting the medical record information of the yaks, dividing the yak groups into a first yak group, i.e., a yak group with a case history, a second yak group, i.e., a yak group with no disease history, and a third yak group, i.e., a yak group with a diseased state, and binding corresponding yak species labels, i.e., a first yak group label, a second yak group label, and a third yak group label; entering the yak information bound to the yak group labels into an artificial intelligence computer database for storage, and generating a yak disease portrait of each yak.

[0007] Preferably, the yak information bound to the yak group tag includes: the yak's body shape, average hair length, yak's age, yak's movement status, and yak's sleep duration.

[0008] Preferably, the step of entering the yak information bound to the yak group label into the artificial intelligence computer database for storage, and generating a yak disease portrait of each yam, includes: establishing a virtual yak state model according to the body shape of the yak, the average hair length and the age of the yak; the virtual yak state model generates the yak physical strength value according to the artificial intelligence big data and the yak age; the virtual yak state model generates the yak disease rate according to the body shape of the yak and the average hair length, combined with the historical yak medical history; establishing a yak susceptibility time curve according to the yak disease rate, and generating the yak disease value according to the yak susceptibility time curve.

[0009] Preferably, the steps of dividing the yak herd into areas for monitoring, tracking and recording the movement trajectories of the yaks, and establishing movement status activity curves of yak herds with different labels based on regional monitoring status analysis include: dividing the yak herd into areas for monitoring, monitoring the movement trajectories of the yak herd through a high-definition camera, and color-coding the yaks with different yak herd labels; respectively recording the total movement trajectories of the first yak herd label, the second yak herd label, and the third yak herd label; entering the total movement trajectories of the first yak herd label, the second yak herd label, and the third yak herd label into an artificial intelligence computer to calculate the total distance of the yak movement trajectories and the total duration of the yak movement trajectories with different yak herd labels; and establishing movement status activity curves of yak herds with different labels based on the yak physical strength value and the yak disease value combined with the total distance of the yak movement trajectories and the total duration of the yak movement trajectories.

[0010] Preferably, the steps of monitoring the yak herd in different time periods, collecting the physical conditions of the yak herd in different time periods according to the life characteristics of the yak herd, and establishing a yak physical condition score curve include: monitoring the eating status, resting status, and exercise status of the yak herd in different time periods; evaluating the yak physical condition scores of yaks with different labels according to the life characteristics of the yak herd; and constructing a yak physical condition score curve based on the monitored yak physical condition scores of the yak herd in different time periods of eating status, resting status, and exercise status.

[0011] Preferably, the steps of monitoring the sleep condition of yaks, collecting the total sleep duration of yak groups with different labels through artificial intelligence, and obtaining the rest index of yak groups with different labels through the total sleep duration of the yak groups with different labels include: collecting the respiratory condition and movement delay duration of yak groups with different labels through artificial intelligence; judging the sleeping and waking states of the yaks according to the respiratory condition and movement delay duration of the yak groups; collecting the total sleep duration of yak groups with different labels through the sleeping and waking states of the yaks; and scoring the yak groups with different labels according to the total sleep duration to obtain the rest index of the yak groups with different labels.

[0012] Preferably, the step of identifying the stress state and early signs of disease of yak herds with different labels by composite event processing technology to generate a yak herd disease prediction map includes: identifying the stress state and early signs of disease of yak herds with different labels by composite event processing technology, and transmitting the stress state and early sign of disease data of the yak herds with different labels into artificial intelligence, performing calculation analysis and estimation, and obtaining yak disease prediction data; performing high-dimensional data analysis of group dynamic changes through big data analysis of yaks' movement patterns and social interaction data, and obtaining a yak movement health map; integrating the yak disease prediction data and the yak movement health map to obtain a yak disease prediction map.

[0013] Preferably, the step of integrating the yak group-exercise state activity curve with different labels, the yak body condition score curve, the rest index of the yak group with different labels and the yak group disease prediction map to obtain a yak health index analysis model, and performing real-time analysis on the health status of the yak comprises: integrating the data of the yak group-exercise state activity curve with different labels and the yak body condition score curve, and marking the intersection of the yak group-exercise state activity curve with different labels and the yak body condition score curve to obtain a first The curve intersection point; adding the yak group disease prediction map into the different label yak group-exercise status active curve and the yak body condition score curve to obtain the second curve intersection point and the yak state-exercise curve diagram area; adding the rest index of the yak group with different labels into the different label yak group-exercise status active curve, the yak body condition score curve and the yak group disease prediction map, connecting the rest index of the yak group with different labels and the first curve intersection point and the second curve intersection point in sequence to obtain the yak health index curve.

[0014] Preferably, after obtaining the yak health index curve, the method also includes: using the yak state-movement curve diagram area divided by the yak health index curve as the yak health index fluctuation range to obtain a yak health index analysis model; collecting yak body condition data and combining with the yak health index analysis model to simulate the yak life status data to predict the yak's disease index; adding the yak living environment influencing factors to the yak's disease index to estimate the yak's potential disease risk value.

[0015] The beneficial effects of the present invention are as follows: the present invention collects medical records of yaks, classifies yak groups, establishes curve graphs according to the movement trajectories of the yaks, establishes curve graphs according to the physical conditions of the yaks at different time periods, obtains the rest index of the yak group by monitoring the sleep status of the yaks, identifies the stress status and early signs of diseases of yak groups with different labels through composite event processing technology, generates a yak group disease prediction map, and finally combines and analyzes a variety of data to establish a yak health index analysis model, which can analyze the health status of the yaks more systematically, and incorporates the characteristics of the yaks to make the health analysis more professional, predicts the disease status of the yaks, adds potential disease risk values, and can predict the disease status of the yaks in advance, preventing the disease from spreading in the yak group after the yaks are sick, resulting in excessive losses in the yak group. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0017] Figure 1 It is a flow chart of a yak health index analysis method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the technical personnel in the technical field better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] Although there are some animal health monitoring products on the market, such as smart collars or ear tags, which can monitor activity levels, feeding behavior and other information to a certain extent, most existing solutions cannot provide comprehensive health assessments. In addition, due to the particularity of yaks, existing animal health monitoring products and methods are not suitable for yak herds. They lack adaptability and it is difficult to adjust the assessment criteria according to the specific conditions of the yak herds. It is impossible to achieve both high accuracy and high versatility for yak herds. Therefore, there is still much room for improvement in universality and accuracy in practical applications.

[0022] The present invention aims to provide an artificial intelligence-based yak health index analysis method to solve the problem that existing livestock health supervision technology cannot be applied to yak herds, and existing health analysis technology does not take into account the characteristics of yaks, resulting in inaccurate and unsystematic health analysis.

[0023] The specific embodiment of the present invention provides a method for analyzing the health index of cattle based on artificial intelligence, the method is as follows Figure 1 As shown, the following steps are included: S1: Collect medical records of yaks, classify yak groups, and bind labels to create yak disease profiles.

[0024] In an embodiment of the present invention, the medical history information of yaks is collected, and the yak group is divided into a first yak group, i.e., a yak group with a case history, a second yak group, i.e., a yak group with no disease history, and a third yak group, i.e., a yak group with a diseased state, and the corresponding yak species labels, i.e., the first yak group label, the second yak group label, and the third yak group label, are bound; the yak information bound to the yak group labels is entered into an artificial intelligence computer database for storage, and a yak disease portrait of each yak is generated; the yak information bound to the yak group labels includes: the body shape of the yak , average hair length, yak age, yak exercise status, and yak sleep time; a virtual yak status model is established based on the yak's body shape, average hair length, and yak age; the virtual yak status model generates the yak's physical strength value based on artificial intelligence big data and the yak's age; the virtual yak status model generates the yak disease rate based on the yak's body shape and average hair length, combined with the historical yak medical records; a yak susceptibility time curve is established based on the yak disease rate, and the yak disease value is generated based on the yak susceptibility time curve.

[0025] S2: Divide the yak herd into regional monitoring areas, track and record the movement trajectories of the yaks, and establish movement status activity curves of yak herds with different labels based on regional monitoring status analysis.

[0026] In an embodiment of the present invention, a yak herd is divided into areas for monitoring, the movement trajectories of the yak herd are monitored by a high-definition camera, and the yaks with different yak herd labels are color-coded and counted; the total movement trajectories of the first yak herd label, the second yak herd label and the third yak herd label are recorded respectively; the total movement trajectories of the first yak herd label, the second yak herd label and the third yak herd label are entered into an artificial intelligence computer, and the total distance of the movement trajectories of the yaks with different yak herd labels and the total duration of the yak movement trajectories are calculated; and an activity curve of movement status of yak herds with different labels is established according to the yak physical strength value, the yak disease value, the total distance of the yak movement trajectory and the total duration of the yak movement trajectory.

[0027] S3: Monitor the yak herd in different time periods, collect the body conditions of the yak herd in different time periods according to the life characteristics of the yak herd, and establish a body condition score curve for the yak.

[0028] In the embodiment of the present invention, the eating state, resting state and exercise state of a yak herd are monitored in different time periods; the body condition scores of yaks with different labels are evaluated according to the life characteristics of the yak herd; and a yak body condition score curve is constructed according to the body condition scores of the yak herd in different time periods of the monitored eating state, resting state and exercise state.

[0029] S4: Monitor the sleeping status of yaks, collect the total sleep duration of yak groups with different labels through artificial intelligence, and obtain the rest index of yak groups with different labels through the total sleep duration of yak groups with different labels.

[0030] In an embodiment of the present invention, the respiratory conditions and movement delay durations of yak groups with different labels are collected through artificial intelligence; the sleeping and waking states of the yaks are judged according to the respiratory conditions and movement delay durations of the yak groups; the total sleep duration of the yak groups with different labels is collected through the sleeping and waking states of the yaks; and the yak groups with different labels are scored according to the total sleep duration to obtain the rest index of the yak groups with different labels.

[0031] S5: Use composite event processing technology to identify the stress status and early signs of disease in yak herds with different labels, and generate a disease prediction map for yak herds.

[0032] In an embodiment of the present invention, the stress state and early signs of disease of yak groups with different labels are identified through composite event processing technology, and the stress state and early signs of disease data of yak groups with different labels are transmitted to artificial intelligence for calculation, analysis and estimation to obtain yak disease estimation data; the movement patterns and social interaction data of yaks are analyzed through big data, and high-dimensional data analysis of the dynamic changes of the group is performed to obtain a yak movement health map; the yak disease estimation data and the yak movement health map are integrated to obtain a yak disease estimation map.

[0033] S6: Integrate the activity curves of yak groups with different labels, the yak body condition score curves, the rest index of yak groups with different labels and the yak group disease prediction map to obtain a yak health index analysis model to conduct real-time analysis of the health status of the yaks.

[0034] In an embodiment of the present invention, data of the yak group-exercise state activity curve and the yak body state score curve with different labels are integrated, and the intersection points of the yak group-exercise state activity curve and the yak body state score curve with different labels are marked to obtain the first curve intersection point; the yak group disease prediction map is added to the yak group-exercise state activity curve and the yak body state score curve with different labels to obtain the second curve intersection point and the yak state-exercise curve graph area; the rest index of the yak group with different labels is added to the yak group-exercise state activity curve, the yak body state score curve and the yak group disease prediction map In the atlas, the rest index of yak groups with different labels and the intersection points of the first curve and the second curve are connected in sequence to obtain the yak health index curve; after obtaining the yak health index curve, it also includes: using the yak state-movement curve diagram area divided by the yak health index curve as the yak health index fluctuation range to obtain the yak health index analysis model; by collecting the yak's physical condition data and combining it with the yak health index analysis model, simulating the yak's living condition data to predict the yak's disease index; adding the yak's living environment influencing factors to the yak's disease index to estimate the yak's potential disease risk value.

[0035] The beneficial effects of the present invention are as follows: the present invention collects medical records of yaks, classifies yak groups, establishes curve graphs according to the movement trajectories of the yaks, establishes curve graphs according to the physical conditions of the yaks at different time periods, obtains the rest index of the yak group by monitoring the sleep status of the yaks, identifies the stress status and early signs of diseases of yak groups with different labels through composite event processing technology, generates a yak group disease prediction map, and finally combines and analyzes a variety of data to establish a yak health index analysis model, which can analyze the health status of the yaks more systematically, and incorporates the characteristics of the yaks to make the health analysis more professional, predicts the disease status of the yaks, adds potential disease risk values, and can predict the disease status of the yaks in advance, preventing the disease from spreading in the yak group after the yaks are sick, resulting in excessive losses in the yak group.

[0036] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical schemes or characteristics in the scheme is not described in detail here; it should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the scheme of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for analyzing yak health index based on artificial intelligence, characterized in that: The method comprises: S1: Collect medical records of yaks, classify yak groups, and bind labels to create yak disease profiles; S2: Divide the yak herd into regional monitoring areas, track and record the movement trajectories of the yaks, and establish the activity curves of the movement status of yak herds with different labels based on the regional monitoring status analysis; S3: Monitor the yak herd in different time periods, collect the body conditions of the yak herd in different time periods according to the life characteristics of the yak herd, and establish a body condition score curve of the yak; S4: monitoring the sleep status of the yaks, collecting the total sleep duration of the yak groups with different labels through artificial intelligence, and obtaining the rest index of the yak groups with different labels through the total sleep duration of the yak groups with different labels; S5: Use composite event processing technology to identify the stress state and early signs of disease of yak herds with different labels, and generate a disease prediction map of yak herds; S6: Integrate the activity curve of the yak groups with different labels-exercise status, the yak body condition score curve, the rest index of the yak groups with different labels and the yak group disease prediction map to obtain a yak health index analysis model, and perform real-time analysis on the health status of the yaks.

2. The method for analyzing the health index of yaks based on artificial intelligence according to claim 1, characterized in that: The steps of collecting medical records of yaks, classifying yak groups, and binding tags to establish yak disease profiles include: Collect medical record information of yaks, divide yak groups into first yak groups, i.e. yak groups with case history, second yak groups, i.e. yak groups with no disease history, and third yak groups, i.e. yak groups with diseased status, and bind corresponding yak species labels, i.e. first yak group labels, second yak group labels, and third yak group labels; The yak information bound to the yak group label is entered into the artificial intelligence computer database for storage, and a yak disease profile of each yak is generated.

3. The method for analyzing the health index of yaks based on artificial intelligence according to claim 2, characterized in that: The yak information bound to the yak group tag includes: the body shape of the yak, the average length of the hair, the age of the yak, the movement state of the yak, and the sleep duration of the yak.

4. The method for analyzing yak health index based on artificial intelligence according to claim 3, characterized in that: The step of entering the yak information bound to the yak group tag into the artificial intelligence computer database for storage and generating a yak disease portrait of each yak includes: Establishing a virtual yak state model according to the body size of the yak, the average length of the hair and the age of the yak; The virtual yak state model generates a yak physical strength value according to artificial intelligence big data and the yak age; The virtual yak state model generates the yak disease rate according to the yak body shape and the average hair length, combined with the historical yak disease history; A time curve of yak susceptibility to disease is established according to the yak disease incidence rate, and a yak disease value is generated according to the time curve of yak susceptibility to disease.

5. The method for analyzing yak health index based on artificial intelligence according to claim 4, characterized in that: The steps of dividing the yak herd into regional monitoring areas, tracking and recording the movement trajectories of the yaks, and establishing the movement status activity curves of yak herds with different labels based on regional monitoring status analysis include: Divide the yak herd into areas for monitoring, monitor the movement tracks of the yak herd through high-definition cameras, and count the yaks with different yak herd labels by color; Respectively record the total movement tracks of the first yak group tag, the second yak group tag and the third yak group tag; The total movement trajectories of the first yak group tag, the second yak group tag and the third yak group tag are entered into an artificial intelligence computer to calculate the total distance and total duration of the yak movement trajectories of different yak group tags; According to the yak's physical strength value, the yak's disease value, the total distance of the yak's movement trajectory and the total duration of the yak's movement trajectory, an activity curve of movement status of yak groups with different labels is established.

6. The method for analyzing yak health index based on artificial intelligence according to claim 2, characterized in that: The steps of monitoring the yak herd in different time periods, collecting the body conditions of the yak herd in different time periods according to the life characteristics of the yak herd, and establishing a body condition score curve of the yak herd include: Monitor the eating, resting and exercise status of the yak herd in different time periods; According to the living characteristics of the yak herd, the body condition scores of yaks with different labels are evaluated; A yak body condition score curve is constructed according to the monitored yak body condition scores of the yak herd at different time periods of eating state, resting state and exercise state.

7. The method for analyzing yak health index based on artificial intelligence according to claim 3, characterized in that: The steps of monitoring the sleep status of yaks, collecting the total sleep duration of yak groups with different labels through artificial intelligence, and obtaining the rest index of yak groups with different labels through the total sleep duration of yak groups with different labels include: Collect the respiratory status and movement delay duration of yak groups with different labels through artificial intelligence; Determine the sleeping and waking status of the yaks according to the breathing status and movement delay duration of the yaks; The total sleep duration of yak groups with different labels is collected through the sleeping and waking states of the yaks; Scoring is performed according to the total sleep duration of the yak groups with different labels to obtain the rest index of the yak groups with different labels.

8. The method for analyzing yak health index based on artificial intelligence according to claim 2, characterized in that: The step of identifying the stress state and early signs of disease of yak herds with different labels by using the composite event processing technology and generating a disease prediction map of the yak herd includes: The stress state and early signs of diseases of yak herds with different labels are identified by composite event processing technology, and the stress state and early signs of diseases of yak herds with different labels are input into artificial intelligence for calculation, analysis and estimation to obtain yak disease estimation data; Through big data analysis of yaks' movement patterns and social interaction data, high-dimensional data analysis of group dynamics was performed to obtain a yak movement health map; The yak disease prediction data and the yak movement health map are integrated to obtain the yak disease prediction map.

9. The method for analyzing yak health index based on artificial intelligence according to claim 8, characterized in that: The step of integrating the activity curve of the yak groups with different labels-exercise status, the yak body status score curve, the rest index of the yak groups with different labels and the yak group disease prediction map to obtain a yak health index analysis model and performing real-time analysis on the health status of the yaks includes: Data integration is performed on the yak group-movement state activity curve with different labels and the yak body condition score curve, and the intersection of the yak group-movement state activity curve with different labels and the yak body condition score curve is marked to obtain a first curve intersection point; Adding the yak herd disease prediction map to the different label yak herd-exercise state activity curve and the yak body state score curve to obtain the second curve intersection and the yak state-exercise curve graph area; The rest index of the yak groups with different labels is added to the yak groups with different labels-exercise status activity curve, the yak body condition score curve and the yak group disease prediction map, and the rest index of the yak groups with different labels and the intersection points of the first curve and the second curve are connected in sequence to obtain the yak health index curve.

10. The method for analyzing yak health index based on artificial intelligence according to claim 9, characterized in that: After obtaining the yak health index curve, the method further includes: using the yak state-movement curve diagram area divided by the yak health index curve as the yak health index fluctuation interval to obtain a yak health index analysis model; By collecting the physical condition data of the yaks and combining it with the yak health index analysis model, the living condition data of the yaks are simulated to predict the disease index of the yaks; The influencing factors of the yak's living environment are added to the yak's disease index to estimate the yak's potential disease risk value.

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