Yak fattening method and system based on graded biochemical indexes

Abstract

The invention relates to the technical field of mathematical optimization, in particular to a graded biochemical index-based yak fattening method, which comprises the following steps of: acquiring yak sample data, calculating a plurality of groups of index mean value sets and a plurality of groups of index standard deviation sets, calculating an index comprehensive score set, and performing health state grading on the index comprehensive score set through graded biochemical indexes. Identifying a corresponding to-be-fattened yak group, obtaining a unit index demand set of each to-be-fattened yak group, constructing a fattening constraint condition set according to the unit index demand set, obtaining a unit feed cost threshold value, constructing a target fattening function based on the unit feed cost threshold value, and fattening the to-be-fattened yak group according to the target fattening function. And solving the fattening constraint condition set and the target fattening function by using a linear programming solver to obtain an optimal fattening feed ratio, and fattening the plurality of yak groups to be fattened according to the optimal fattening feed ratio. The intelligent degree of an existing yak fattening method can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mathematical optimization, and in particular to a yak fattening method, system, electronic equipment and computer-readable storage medium based on graded biochemical indicators. Background Art

[0002] Yak fattening is to increase the weight and meat quality of yaks and maximize their economic value and market competitiveness. With the development of breeding technology, the methods of yak fattening are also constantly improving.

[0003] Traditional yak fattening methods usually rely on manual management. Breeders manually adjust the fattening feed ratio based on fattening experience and the growth of yaks to achieve yak fattening.

[0004] Although the above method can achieve yak fattening, it relies on manual operation and requires the breeder to have rich breeding experience and in-depth understanding of the nutrition required by yaks in various health states. Therefore, the current yak fattening method has the problem of low intelligence. Summary of the invention

[0005] The present invention provides a yak fattening method based on graded biochemical indicators and a computer-readable storage medium, the main purpose of which is to improve the intelligence level of the yak fattening method and reduce excessive consumption of time and human resources.

[0006] To achieve the above object, the present invention provides a yak fattening method based on graded biochemical indicators, comprising:

[0007] Acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data;

[0008] Respectively calculating the multiple groups of index mean sets and the multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: a biochemical mean set and a growth mean set, and the multiple groups of index standard deviation sets include: a biochemical standard deviation set and a growth standard deviation set;

[0009] Calculate the comprehensive score set of indicators based on the mean set of multiple indicators and the standard deviation set of multiple indicators;

[0010] The health status of the index comprehensive score set is graded by preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, and the group of yaks to be fattened corresponding to each group of graded comprehensive score sets is identified to obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval;

[0011] Obtaining the unit indicator requirement set of each group of yaks to be fattened, and constructing a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition;

[0012] Obtain a unit feed cost threshold, and construct a target fattening function based on the unit feed cost threshold;

[0013] Using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio;

[0014] According to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened.

[0015] Optionally, the obtaining of yak sample data includes:

[0016] Acquiring multiple groups of yak blood sample sets based on a preset sampling period;

[0017] Performing anticoagulation operation on the multiple groups of yak blood sample sets using a pre-configured anticoagulant to obtain multiple groups of anticoagulated blood sample sets;

[0018] Performing biochemical index determination on multiple groups of anticoagulated blood sample sets to obtain biochemical index data, wherein the biochemical index data include: multiple groups of serum total protein sets, multiple groups of serum urea nitrogen sets, multiple groups of serum creatinine sets and multiple groups of blood glucose sets;

[0019] Collect multiple sets of yak periodic weight sets according to preset weighing cycles;

[0020] Calculate multiple groups of yak daily weight gain sets based on multiple groups of yak periodic weight sets;

[0021] The periodic body weight sets and daily weight gain sets of multiple groups of yaks were summarized to obtain growth index data.

[0022] Optionally, respectively calculating multiple groups of indicator mean sets and multiple groups of indicator standard deviation sets of the yak sample data includes:

[0023] Extracting yak data sets of the same group from the yak sample data in sequence, wherein the yak data sets of the same group include: a serum total protein set, a serum urea nitrogen set, a serum creatinine set, a blood sugar set, a yak periodic weight set, and a yak daily weight gain set;

[0024] The following formula is used to calculate the mean value set of indicators of the same group of yak data sets:

[0025]

[0026] Among them, μ iRefers to the mean value of the i-th physiological index in the same group of yak data sets, i is the serial number of the physiological index, n i Refers to the number of samples of the ith physiological index in the same group of yak data sets, x ij Refers to the jth sample value of the i-th physiological index in the same group of yak data sets, where j represents the sample value sequence number, wherein the physiological index refers to a biochemical index or a growth index;

[0027] Summarizing the indicator mean value sets to obtain multiple groups of indicator mean value sets;

[0028] The standard deviation of the indicators of the same group of yak data sets is calculated using the following formula:

[0029]

[0030] Among them, σ i Refers to the standard deviation of the ith physiological index in the same group of yak data sets;

[0031] The indicator standard deviation sets are summarized to obtain multiple groups of indicator standard deviation sets.

[0032] Optionally, the calculating of the comprehensive indicator score set based on the multiple indicator mean value sets and the multiple indicator standard deviation sets includes:

[0033] The indicator adjustment weight set of the same group of yak data sets is obtained, and the indicator comprehensive score is calculated based on the indicator adjustment weight set. The formula is as follows:

[0034]

[0035] Among them, S represents the comprehensive score of the index of the p-th group of yak sample data, p represents the group number of the yak sample data, ω1 represents the index adjustment weight of the first physiological index, ω I represents the index adjustment weight of the Ith physiological index, n1 represents the sample size of the first physiological index of the pth group of yak sample data, x 1j represents the jth sample value of the first physiological index of the pth group of yak sample data, μ1 represents the mean value of the first physiological index of the pth group of yak sample data, σ1 represents the standard deviation of the first physiological index of the pth group of yak sample data, n I represents the number of samples of the Ith physiological index of the pth group of yak sample data, x Ij represents the jth sample value of the Ith physiological index of the pth group of yak sample data, μ I represents the mean value of the Ith physiological index of the pth group of yak sample data, σ I represents the standard deviation of the Ith physiological index of the pth group of yak sample data, and I represents the total number of physiological indexes.

[0036] Optionally, the step of obtaining an indicator adjustment weight set for the same group of yak data sets includes:

[0037] Calculating the indicator correlation of each physiological indicator in the same group of yak data sets to obtain an indicator correlation set;

[0038] According to the preset initial indicator weight set and indicator correlation set, the indicator adjustment weight is calculated using the following formula:

[0039]

[0040] Among them, ω i refers to the index adjustment weight of the i-th physiological index, ω i ′ represents the initial indicator weight of the i-th physiological indicator, corr(x ij ,μ i ) refers to x ij With μ i The correlation between indicators.

[0041] Optionally, the health status of the comprehensive score set of indicators is graded by preset graded biochemical indicators to obtain multiple sets of graded comprehensive score sets, including:

[0042] Extracting the indicator comprehensive scores in the indicator comprehensive score set in sequence, obtaining the upper limit of the morbid score interval based on the morbid state score interval, and comparing the indicator comprehensive score with the upper limit of the morbid score interval;

[0043] If the comprehensive score of the indicator is not greater than the upper limit of the pathological score range, the health status of the comprehensive score of the indicator is confirmed to be at the pathological level;

[0044] If the comprehensive index score is greater than the upper limit of the pathological score interval, the upper limit of the subhealth score interval is obtained, and the comprehensive index score is compared with the upper limit of the subhealth score interval;

[0045] If the comprehensive score of the index is not greater than the upper limit of the sub-health score range, then the health status of the comprehensive score of the index is confirmed to be a sub-health level;

[0046] If the comprehensive score of the index is greater than the upper limit of the sub-health score range, the health status of the comprehensive score of the index is confirmed to be the health level;

[0047] The morbid comprehensive score set, subhealthy comprehensive score set and healthy comprehensive score set corresponding to the morbid level, subhealthy level and healthy level are respectively identified to obtain multiple groups of graded comprehensive score sets.

[0048] Optionally, constructing a fattening constraint condition set according to a unit indicator requirement set includes:

[0049] Obtain the unit index requirement of energy, unit index requirement of crude protein, unit index requirement of crude fiber, unit index requirement of vitamins, unit index requirement of calcium and unit index requirement of phosphorus of health-level yaks;

[0050] Based on the unit index requirements of energy, crude protein, crude fiber, vitamins, calcium and phosphorus of health-grade yaks, the following formula is used to construct the fattening constraint condition set of health-grade yaks:

[0051]

[0052] m∈[1,6],m∈N

[0053] q∈[1,Q],q∈N

[0054] r∈(1,2,3)

[0055] X1+…+X Q =1

[0056] X1>0,…,X Q >0

[0057] Among them, q represents the feed number, Q represents the number of fattening feed categories, and m represents the nutritional index number. X represents the nutritional supply of the mth nutritional index of the rth healthy yak that can be provided by the unit mass of fattening feed with feed number q, q Indicates the unit weight of the fattening feed with feed number q. represents the unit index requirement of the mth nutritional index for the rth healthy yak, X Q It represents the unit weight of the fattening feed with feed number Q, and X1 represents the unit weight of the fattening feed with feed number 1.

[0058] Optionally, constructing a target fattening function based on a unit feed cost threshold comprises:

[0059] The unit feed cost set corresponding to the feed category set is obtained, and a target fattening function is constructed based on the unit feed cost threshold and the unit feed cost set. The target fattening function is as follows:

[0060]

[0061] Among them, M min represents the unit feed cost threshold, N q Represents the unit mass cost of fattening feed with feed number q.

[0062] Optionally, the method of using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio includes:

[0063] Constructing a linear programming model using the fattening constraint condition set and the target fattening function;

[0064] The linear programming model is input into a pre-built linear programming solver, and the optimal fattening feed ratio is calculated.

[0065] To achieve the above object, the present invention also provides a yak fattening system based on graded biochemical indicators, comprising:

[0066] A sample data acquisition module, used to acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data, and respectively calculate multiple groups of index mean sets and multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: biochemical mean set and growth mean set, and the multiple groups of index standard deviation sets include: biochemical standard deviation set and growth standard deviation set;

[0067] A health status grading module is used to calculate an indicator comprehensive score set based on multiple groups of indicator mean sets and multiple groups of indicator standard deviation sets, perform health status grading on the indicator comprehensive score set through preset graded biochemical indicators, obtain multiple groups of graded comprehensive score sets, identify the group of yaks to be fattened corresponding to each group of graded comprehensive score sets, and obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval;

[0068] A fattening constraint construction module is used to obtain the unit indicator requirement set of each group of yaks to be fattened, and to construct a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition;

[0069] The optimal feed ratio module is used to obtain a unit feed cost threshold, construct a target fattening function based on the unit feed cost threshold, use a pre-built linear programming solver to solve the fattening constraint set and the target fattening function, obtain the optimal fattening feed ratio, and fatten multiple groups of yaks to be fattened according to the optimal fattening feed ratio.

[0070] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0071] a memory storing at least one instruction; and

[0072] The processor executes the instructions stored in the memory to implement the above-mentioned yak fattening method based on graded biochemical indicators.

[0073] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned yak fattening method based on graded biochemical indicators.

[0074] In order to solve the problems described in the background technology, the present invention realizes the intelligent fattening management of yaks, including health status evaluation and fattening feed ratio optimization, by combining yak sample data collection, physiological index measurement, index comprehensive score calculation and linear programming optimization algorithm; firstly, the biochemical index data and growth index data of yaks are collected to ensure the comprehensiveness and accuracy of the data; then, the collected yak sample data are statistically analyzed, and the mean set and standard deviation set of multiple groups of indicators are calculated respectively, so as to provide accurate data for health status evaluation and ensure that the index comprehensive score is more accurate; then, the index comprehensive score set is calculated based on the index mean set and standard deviation set, the index comprehensive score is automatically calculated and multiple index data are analyzed, so as to reduce the subjectivity of manual evaluation and improve the accuracy of evaluation; then, the health status of the index comprehensive score set is graded by graded biochemical indicators, so as to realize the systematic evaluation of the health status of yaks. The system is evaluated systematically to make the classification more standardized; further, a fattening constraint condition set is constructed according to the unit indicator demand set to ensure that the fattening feed ratio of yaks at each health status level meets their nutritional needs, thereby improving the scientific nature of the use of fattening feed; then, a unit feed cost threshold is obtained, and a target fattening function is constructed based on the unit feed cost threshold, and the fattening feed ratio is automatically optimized using the target fattening function to improve the intelligence level of fattening feed cost control; then, the fattening constraint condition set and the target fattening function are solved using a pre-built linear programming solver to improve the accuracy and efficiency of fattening feed ratio calculation; finally, according to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened, and accurate fattening is achieved according to the optimal fattening feed ratio, thereby improving the intelligence level of the fattening process; therefore, the present invention can improve the intelligence level of yak fattening and reduce excessive consumption of time and human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic flow chart of a yak fattening method based on graded biochemical indicators provided by an embodiment of the present invention;

[0076] Figure 2 A functional module diagram of a yak fattening system based on graded biochemical indicators provided by an embodiment of the present invention;

[0077] Figure 3A schematic diagram of the structure of an electronic device for implementing the yak fattening method based on graded biochemical indicators provided by an embodiment of the present invention.

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0080] The embodiment of the present application provides a yak fattening method based on graded biochemical indicators. The execution subject of the yak fattening method based on graded biochemical indicators includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the yak fattening method based on graded biochemical indicators can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0081] Reference Figure 1 FIG. 1 is a flow chart of a yak fattening method based on graded biochemical indicators provided by an embodiment of the present invention. In this embodiment, the yak fattening method based on graded biochemical indicators includes:

[0082] S1. Obtain yak sample data, wherein the yak sample data includes: biochemical index data and growth index data.

[0083] It can be understood that yak sample data refers to the biochemical indicator data and growth indicator data obtained from the sample yaks, sample yaks refer to a collection of individual samples selected from the yak population, biochemical indicator data refer to multiple biochemical indicator data measured from yak blood samples to reflect the health status of yaks, yak blood samples refer to a collection of blood samples collected from sample yaks, growth indicator data refer to data reflecting the cyclical weight of yaks and the daily weight gain of yaks, yak cyclical weight refers to the weight of yak samples collected based on the weighing cycle, yak daily weight gain refers to the weight gain of sample yaks every day, and weighing cycle refers to the inverse of the frequency of weighing yak samples.

[0084] In detail, the obtaining of yak sample data includes:

[0085] Acquiring multiple groups of yak blood sample sets based on a preset sampling period;

[0086] Performing anticoagulation operation on the multiple groups of yak blood sample sets using a pre-configured anticoagulant to obtain multiple groups of anticoagulated blood sample sets;

[0087] Performing biochemical index determination on multiple groups of anticoagulated blood sample sets to obtain biochemical index data, wherein the biochemical index data include: multiple groups of serum total protein sets, multiple groups of serum urea nitrogen sets, multiple groups of serum creatinine sets and multiple groups of blood glucose sets;

[0088] Collect multiple sets of yak periodic weight sets according to preset weighing cycles;

[0089] Calculate multiple groups of yak daily weight gain sets based on multiple groups of yak periodic weight sets;

[0090] The periodic body weight sets and daily weight gain sets of multiple groups of yaks were summarized to obtain growth index data.

[0091] It can be understood that the sampling cycle refers to the inverse of the frequency of blood collection for yak samples, multiple groups of yak blood sample sets refer to yak blood sample sets of multiple yak samples, wherein each group of yak blood sample sets refers to a collection of blood samples collected from a yak sample, anticoagulant refers to a chemical substance that prevents the yak blood sample set from coagulating, anticoagulation operation refers to the operation of adding anticoagulant to the yak blood sample set to prevent the yak blood sample set from coagulating, multiple groups of anticoagulated blood sample sets refer to multiple groups of yak blood sample sets that have undergone anticoagulation operation, biochemical index determination refers to the process of performing biochemical index detection on multiple groups of anticoagulated blood sample sets through an automatic analyzer, automatic analyzer refers to an instrument used to detect biochemical index data in yak blood sample sets, multiple groups of yak periodic weight sets refer to a collection of yak periodic weights collected from multiple yak samples based on a weighing cycle, and multiple groups of yak daily weight gain sets refer to a collection of yak daily weight gain of multiple yak samples.

[0092] S2. Calculate the multiple groups of indicator mean sets and multiple groups of indicator standard deviation sets of the yak sample data respectively, wherein the multiple groups of indicator mean sets include: biochemical mean set and growth mean set, and the multiple groups of indicator standard deviation sets include: biochemical standard deviation set and growth standard deviation set.

[0093] It can be understood that the mean set of multiple groups of indicators refers to the set of means of multiple groups of biochemical indicator data and multiple groups of growth indicator data, and the standard deviation set of multiple groups of indicators refers to the set of standard deviations of multiple groups of biochemical indicator data and multiple groups of growth indicator data, wherein each group of indicator mean sets represents the indicator mean set of a sample yak, the biochemical mean set refers to the set of means of biochemical indicator data, the growth mean set refers to the set of means of growth indicator data, the biochemical standard deviation set refers to the set of standard deviations of biochemical indicator data, and the growth standard deviation set refers to the set of standard deviations of growth standard data.

[0094] In detail, the method of respectively calculating the multiple groups of indicator mean sets and the multiple groups of indicator standard deviation sets of the yak sample data includes:

[0095] Extracting yak data sets of the same group from the yak sample data in sequence, wherein the yak data sets of the same group include: a serum total protein set, a serum urea nitrogen set, a serum creatinine set, a blood sugar set, a yak periodic weight set, and a yak daily weight gain set;

[0096] The following formula is used to calculate the mean value set of indicators of the same group of yak data sets:

[0097]

[0098] Among them, μ i Refers to the mean value of the i-th physiological index in the same group of yak data sets, i is the serial number of the physiological index, n i Refers to the number of samples of the ith physiological index in the same group of yak data sets, x ij Refers to the jth sample value of the i-th physiological index in the same group of yak data sets, where j represents the sample value sequence number, wherein the physiological index refers to a biochemical index or a growth index;

[0099] Summarizing the indicator mean value sets to obtain multiple groups of indicator mean value sets;

[0100] The standard deviation of the indicators of the same group of yak data sets is calculated using the following formula:

[0101]

[0102] Among them, σ i Refers to the standard deviation of the ith physiological index in the same group of yak data sets;

[0103] The indicator standard deviation sets are summarized to obtain multiple groups of indicator standard deviation sets.

[0104] It can be understood that the same group of yak data sets refers to a collection of physiological indicator data collected from a sample yak.

[0105] S3. Calculate the comprehensive score set of indicators based on the mean set of multiple groups of indicators and the standard deviation set of multiple groups of indicators.

[0106] The interpretable,comprehensive indicator score set refers to the set of scores obtained after evaluating the,biochemical indicator data and growth indicator data.

[0107] In detail, the calculation of the comprehensive score set of indicators based on the mean value sets of multiple groups of indicators and the standard deviation sets of multiple groups of indicators includes:

[0108] The indicator adjustment weight set of the same group of yak data sets is obtained, and the indicator comprehensive score is calculated based on the indicator adjustment weight set. The formula is as follows:

[0109]

[0110] Among them, S represents the comprehensive score of the index of the p-th group of yak sample data, p represents the group number of the yak sample data, ω1 represents the index adjustment weight of the first physiological index, ω I represents the index adjustment weight of the Ith physiological index, n1 represents the sample size of the first physiological index of the pth group of yak sample data, x 1j represents the jth sample value of the first physiological index of the pth group of yak sample data, μ1 represents the mean value of the first physiological index of the pth group of yak sample data, σ1 represents the standard deviation of the first physiological index of the pth group of yak sample data, n I represents the number of samples of the Ith physiological index of the pth group of yak sample data, x Ij represents the jth sample value of the Ith physiological index of the pth group of yak sample data, μ I represents the mean value of the Ith physiological index of the pth group of yak sample data, σ I represents the standard deviation of the Ith physiological index of the pth group of yak sample data, and I represents the total number of physiological indexes.

[0111] It can be understood that the indicator adjustment weight refers to the calculated weight assigned according to the degree of influence of each physiological indicator on the health status of the yak.

[0112] In detail, the indicator adjustment weight set of the same group of yak data sets is obtained, including:

[0113] Calculating the indicator correlation of each physiological indicator in the same group of yak data sets to obtain an indicator correlation set;

[0114] According to the preset initial indicator weight set and indicator correlation set, the indicator adjustment weight is calculated using the following formula:

[0115]

[0116] Among them, ω i refers to the index adjustment weight of the i-th physiological index, ω i ′ represents the initial indicator weight of the i-th physiological indicator, corr(x ij ,μ i ) refers to x ij With μ i The correlation between indicators.

[0117] Interpretable, indicator correlation refers to the statistical relationship and dependence between different physiological indicators, reflecting the degree of mutual influence of these indicators. Statistical relationship refers to the relationship between physiological indicators, which can be linear or nonlinear. Dependence refers to the dependence of one physiological indicator on another or more physiological indicators. It is usually used to describe the causal relationship or correlation between physiological indicators. For example, the cyclical weight of yaks depends on the daily weight gain of yaks, because the increase in the daily weight gain of yaks will lead to the increase in the cyclical weight of yaks. Indicator correlation is a prior art and will not be repeated here.

[0118] For example, assume that the yak cycle weight set (weight unit is unified in kg) is: 300, 320, 310, 305, 315, the yak daily weight gain set is: 0.8, 0.9, 0.85, 0.82, 0.88, the serum total protein set (unit is unified in g / L) is: 7.0, 6.8, 7.2, 7.1, 6.9, the serum urea nitrogen set (unit is unified in mg / dL) is: 15, 14, 16, 15, 14, the serum creatinine set (unit is unified in mg / dL) is: 1.2, 1.1, 1.3, 1.2, 1.1, and the blood glucose set (unit is unified in mg / dL) is: 90, 92, 88, 91, 89. The mean of the yak cycle weight set is calculated as The standard deviation of the yak cycle weight set is 7.07, the mean of the yak daily weight gain set is 0.85, and the standard deviation of the yak daily weight gain set is 0.03. The mean of the serum total protein set is 7.0, and the standard deviation of the serum total protein set is 0.14. The mean of the serum urea nitrogen set is 14.8, and the standard deviation of the serum urea nitrogen set is 0.84. The mean of the serum creatinine set is 1.18, and the standard deviation of the serum creatinine set is 0.08. The mean of the blood glucose set is 90, and the standard deviation of the blood glucose set is 1.41. Assuming that the initial indicator weights of all indicators are equal, corr(x ij ,μ i ) is 0.1 and is 0.6, and the calculated index adjustment weight is 0.1389. The comprehensive score of the index is calculated.

[0119] S4. The health status of the indicator comprehensive score set is graded by preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, and the group of yaks to be fattened corresponding to each group of graded comprehensive score sets is identified to obtain multiple groups of yaks to be fattened.

[0120] It can be explained that graded biochemical indicators refer to indicators used to evaluate the health status of yak samples, multi-group graded comprehensive score sets refer to the collection of comprehensive score sets corresponding to the health status levels of multiple groups of yak samples, and yak groups to be fattened refer to different yak groups divided according to different health status levels.

[0121] In detail, the health status of the indicator comprehensive score set is graded by using preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, including:

[0122] Extracting the indicator comprehensive scores in the indicator comprehensive score set in sequence, obtaining the upper limit of the morbid score interval based on the morbid state score interval, and comparing the indicator comprehensive score with the upper limit of the morbid score interval;

[0123] If the comprehensive score of the indicator is not greater than the upper limit of the pathological score range, the health status of the comprehensive score of the indicator is confirmed to be at the pathological level;

[0124] If the comprehensive index score is greater than the upper limit of the pathological score interval, the upper limit of the subhealth score interval is obtained, and the comprehensive index score is compared with the upper limit of the subhealth score interval;

[0125] If the comprehensive score of the index is not greater than the upper limit of the sub-health score range, then the health status of the comprehensive score of the index is confirmed to be a sub-health level;

[0126] If the comprehensive score of the index is greater than the upper limit of the sub-health score range, the health status of the comprehensive score of the index is confirmed to be the health level;

[0127] The morbid comprehensive score set, subhealthy comprehensive score set and healthy comprehensive score set corresponding to the morbid level, subhealthy level and healthy level are respectively identified to obtain multiple groups of graded comprehensive score sets.

[0128] Explainably, health status grading refers to the operation of substituting the comprehensive score set of indicators into the graded biochemical indicators to complete the grading operation, morbid state score interval refers to the score range of the comprehensive score of indicators of the morbid level, sub-health score interval refers to the score range of the comprehensive score of indicators of the sub-health level, morbid level, sub-health level and health level refer to the health status levels used to evaluate the health status of yak samples, morbid comprehensive score set refers to the set of comprehensive scores of indicators belonging to the morbid level, sub-health comprehensive score set refers to the set of comprehensive scores of indicators belonging to the sub-health level, and health comprehensive score set refers to the set of comprehensive scores of indicators belonging to the health level.

[0129] For example, the graded biochemical indexes are: sick level: S<-0.5, sub-health level: -0.5≤S<0.5, health level: S≥0.5. The comprehensive score of the index is -0.049, and the health status of the yak sample is confirmed to be sub-healthy based on the graded biochemical indexes.

[0130] S5. Obtain a unit indicator requirement set for each group of yaks to be fattened, and construct a fattening constraint condition set according to the unit indicator requirement set.

[0131] Explainably, the unit indicator requirement set refers to the minimum nutrient supply contained in the unit mass mixed feed required to meet the growth of the sample yaks, and the fattening constraint condition set refers to a set of a series of conditions that need to be met when formulating the yak fattening strategy. Exemplarily, the fattening constraint condition set includes: energy constraint condition set, crude protein constraint condition set, crude fiber constraint condition set, vitamin constraint condition set, calcium constraint condition set and phosphorus constraint condition set. The unit mass is 1kg.

[0132] In detail, the fattening constraint condition set is constructed according to the unit indicator requirement set, including:

[0133] Obtain the unit index requirement of energy, unit index requirement of crude protein, unit index requirement of crude fiber, unit index requirement of vitamins, unit index requirement of calcium and unit index requirement of phosphorus of health-level yaks;

[0134] Based on the unit index requirements of energy, crude protein, crude fiber, vitamins, calcium and phosphorus of health-grade yaks, the following formula is used to construct the fattening constraint condition set of health-grade yaks:

[0135]

[0136] m∈[1,6],m∈N

[0137] q∈[1,Q],q∈N

[0138] r∈(1,2,3)

[0139] X1+…+X Q =1

[0140] X1>0,…,X Q >0

[0141] Among them, q represents the feed number, Q represents the number of fattening feed categories, and m represents the nutritional index number. X represents the nutritional supply of the mth nutritional index of the rth healthy yak that can be provided by the unit mass of fattening feed with feed number q, q Indicates the unit weight of the fattening feed with feed number q. represents the unit index requirement of the mth nutritional index for the rth healthy yak, X Q It represents the unit weight of the fattening feed with feed number Q, and X1 represents the unit weight of the fattening feed with feed number 1.

[0142] It can be understood that the feed serial number refers to the serial number used to traverse all types of fattening feed, and each feed has a serial number. The number of fattening feed categories refers to the total number of fattening feed types. The nutritional index serial number refers to the serial number used to traverse all nutritional indexes, and each nutritional index has its corresponding nutritional index serial number. The nutritional supply refers to the nutrition that fattening feed can provide, and the unit proportion refers to the proportion of one fattening feed to the total fattening feed per unit mass.

[0143] For example, assuming that there are three types of fattening feed, fattening feed one is D1, fattening feed two is D2, and fattening feed three is D3, the unit index requirement of energy is 90MJ, the unit index requirement of crude protein is 16g, the unit index requirement of crude fiber is 6g, the unit index requirement of vitamins is 11mg, the unit index requirement of calcium is 1.2mg, and the unit index requirement of phosphorus is 1.3mg. The following units have been omitted. Please refer to the above description for details. Fattening feed one can provide: unit index energy is 100, unit index crude protein ... The unit protein is 20, the unit crude fiber is 5, the unit vitamin is 11, the unit calcium is 1, and the unit phosphorus is 1. The fattening feed 2 can provide: the unit energy is 80, the unit crude protein is 15, the unit crude fiber is 7, the unit vitamin is 12, the unit calcium is 2, and the unit phosphorus is 2. The fattening feed 3 can provide: the unit energy is 90, the unit crude protein is 18, the unit crude fiber is 6, the unit vitamin is 11, the unit calcium is 1.5, and the unit phosphorus is 1.5. Construct the fattening constraint condition set:

[0144] 100D1+80D2+90D3≤90

[0145] 120D1+15D2+18D3≤16

[0146] 5D1+7D2+6D3≤6

[0147] 10D1+12D2+11D3≤11

[0148] 1D1+2D2+1.5D3≤1.2

[0149] 1D1+2D2+1.5D3≤1.3

[0150] Complete the construction of the fattening constraint set.

[0151] S6. Obtain a unit feed cost threshold, and construct a target fattening function based on the unit feed cost threshold.

[0152] It can be understood that the unit feed cost threshold refers to the maximum acceptable fattening feed cost, and the target fattening function refers to the function that maximizes the yak fattening effect under the premise of not exceeding the unit feed cost threshold.

[0153] In detail, the target fattening function is constructed based on the unit feed cost threshold, including:

[0154] The unit feed cost set corresponding to the feed category set is obtained, and a target fattening function is constructed based on the unit feed cost threshold and the unit feed cost set. The target fattening function is as follows:

[0155]

[0156] Among them, M min represents the unit feed cost threshold, N q Represents the unit mass cost of fattening feed with feed number q.

[0157] It can be understood that the feed category set refers to the set of fattening feed types, the unit feed cost set refers to the set of unit costs of each fattening feed, and the unit mass cost refers to the cost of each unit mass of fattening feed.

[0158] S7. Using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function, obtain an optimal fattening feed ratio.

[0159] Explainable,Linear Programming Solver is a computational tool used to solve linear programming problems, and the optimal fattening feed ratio refers to the fattening feed ratio that maximizes the fattening effect of yaks under the premise of not exceeding the unit feed cost threshold.

[0160] In detail, the method of using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain the optimal fattening feed ratio includes:

[0161] Constructing a linear programming model using the fattening constraint condition set and the target fattening function;

[0162] The linear programming model is input into a pre-built linear programming solver, and the optimal fattening feed ratio is calculated.

[0163] It can be understood that the linear programming model refers to a mathematical model used to solve the optimal fattening feed ratio. The linear programming model is a prior art and will not be described in detail here.

[0164] S8. Fattening multiple groups of yaks to be fattened according to the optimal fattening feed ratio.

[0165] For example, there are three kinds of fattening feeds, and the optimal fattening feed ratio solved by the linear programming solver is fattening feed one: 30%, fattening feed two: 50%, and fattening feed three: 20%. Then, yak fattening is completed according to the solved optimal fattening ratio.

[0166] In order to solve the problems described in the background technology, the present invention realizes the intelligent fattening management of yaks, including health status evaluation and fattening feed ratio optimization, by combining yak sample data collection, physiological index measurement, index comprehensive score calculation and linear programming optimization algorithm; firstly, the biochemical index data and growth index data of yaks are collected to ensure the comprehensiveness and accuracy of the data; then, the collected yak sample data are statistically analyzed, and the mean set and standard deviation set of multiple groups of indicators are calculated respectively, so as to provide accurate data for health status evaluation and ensure that the index comprehensive score is more accurate; then, the index comprehensive score set is calculated based on the index mean set and standard deviation set, the index comprehensive score is automatically calculated and multiple index data are analyzed, so as to reduce the subjectivity of manual evaluation and improve the accuracy of evaluation; then, the health status of the index comprehensive score set is graded by graded biochemical indicators, so as to realize the systematic evaluation of the health status of yaks. The system is evaluated systematically to make the classification more standardized; further, a fattening constraint condition set is constructed according to the unit indicator demand set to ensure that the fattening feed ratio of yaks at each health status level meets their nutritional needs, thereby improving the scientific nature of the use of fattening feed; then, a unit feed cost threshold is obtained, and a target fattening function is constructed based on the unit feed cost threshold, and the fattening feed ratio is automatically optimized using the target fattening function to improve the intelligence level of fattening feed cost control; then, the fattening constraint condition set and the target fattening function are solved using a pre-built linear programming solver to improve the accuracy and efficiency of fattening feed ratio calculation; finally, according to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened, and accurate fattening is achieved according to the optimal fattening feed ratio, thereby improving the intelligence level of the fattening process; therefore, the present invention can improve the intelligence level of yak fattening and reduce excessive consumption of time and human resources.

[0167] like Figure 2 , which is a functional module diagram of a yak fattening system based on graded biochemical indicators provided by an embodiment of the present invention.

[0168] The yak fattening system 100 based on graded biochemical indicators of the present invention can be installed in an electronic device. According to the functions implemented, the yak fattening system 100 based on graded biochemical indicators can include a sample data acquisition module 101, a health status classification module 102, a fattening constraint construction module 103 and an optimal feed ratio module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0169] The sample data acquisition module 101 is used to acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data, and respectively calculate multiple groups of index mean sets and multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: biochemical mean set and growth mean set, and the multiple groups of index standard deviation sets include: biochemical standard deviation set and growth standard deviation set;

[0170] The health status grading module 102 is used to calculate the index comprehensive score set based on the multiple groups of index mean value sets and the multiple groups of index standard deviation sets, perform health status grading on the index comprehensive score set by using preset graded biochemical indicators, obtain multiple groups of graded comprehensive score sets, identify the group of yaks to be fattened corresponding to each group of graded comprehensive score sets, and obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval;

[0171] The fattening constraint construction module 103 is used to obtain the unit indicator requirement set of each group of yaks to be fattened, and construct a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition;

[0172] The optimal feed ratio module 104 is used to obtain a unit feed cost threshold, construct a target fattening function based on the unit feed cost threshold, use a pre-built linear programming solver to solve the fattening constraint set and the target fattening function, and obtain an optimal fattening feed ratio. According to the optimal fattening feed ratio, fatten multiple groups of yaks to be fattened.

[0173] In detail, each module in the yak fattening system 100 based on graded biochemical indicators in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the yak fattening method based on graded biochemical indicators described in the text can produce the same technical effects, so I will not go into details here.

[0174] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a yak fattening method based on graded biochemical indicators provided by an embodiment of the present invention.

[0175] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a yak fattening method program based on graded biochemical indices.

[0176] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the yak fattening method program based on graded biochemical indicators, but also can be used to temporarily store data that has been output or is to be output.

[0177] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a yak fattening method program based on graded biochemical indicators, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0178] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0179] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0180] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0181] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0182] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0183] The yak fattening method program based on graded biochemical indicators stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0184] Acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data;

[0185] Respectively calculating the multiple groups of index mean sets and the multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: a biochemical mean set and a growth mean set, and the multiple groups of index standard deviation sets include: a biochemical standard deviation set and a growth standard deviation set;

[0186] Calculate the comprehensive score set of indicators based on the mean set of multiple indicators and the standard deviation set of multiple indicators;

[0187] The health status of the index comprehensive score set is graded by preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, and the group of yaks to be fattened corresponding to each group of graded comprehensive score sets is identified to obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval;

[0188] Obtaining the unit indicator requirement set of each group of yaks to be fattened, and constructing a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition;

[0189] Obtain a unit feed cost threshold, and construct a target fattening function based on the unit feed cost threshold;

[0190] Using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio;

[0191] According to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened.

[0192] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0193] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0194] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0195] Acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data;

[0196] Respectively calculating the multiple groups of index mean sets and the multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: a biochemical mean set and a growth mean set, and the multiple groups of index standard deviation sets include: a biochemical standard deviation set and a growth standard deviation set;

[0197] Calculate the comprehensive score set of indicators based on the mean set of multiple indicators and the standard deviation set of multiple indicators;

[0198] The health status of the index comprehensive score set is graded by preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, and the group of yaks to be fattened corresponding to each group of graded comprehensive score sets is identified to obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval;

[0199] Obtaining the unit indicator requirement set of each group of yaks to be fattened, and constructing a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition;

[0200] Obtain a unit feed cost threshold, and construct a target fattening function based on the unit feed cost threshold;

[0201] Using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio;

[0202] According to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened.

[0203] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0204] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0205] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0206] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0207] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A yak fattening method based on graded biochemical indicators, characterized in that: The method comprises: Acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data; Respectively calculating the multiple groups of index mean sets and the multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: a biochemical mean set and a growth mean set, and the multiple groups of index standard deviation sets include: a biochemical standard deviation set and a growth standard deviation set; Calculate the comprehensive score set of indicators based on the mean set of multiple indicators and the standard deviation set of multiple indicators; The health status of the index comprehensive score set is graded by preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, and the group of yaks to be fattened corresponding to each group of graded comprehensive score sets is identified to obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval; Obtaining the unit indicator requirement set of each group of yaks to be fattened, and constructing a fattening constraint condition set according to the unit indicator requirement set, wherein the fattening constraint condition set includes: energy requirement constraint condition, crude protein constraint condition, crude fiber constraint condition, vitamin constraint condition, calcium constraint condition and phosphorus constraint condition; Obtain a unit feed cost threshold, and construct a target fattening function based on the unit feed cost threshold; Using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio; According to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened.

2. The yak fattening method based on graded biochemical indicators according to claim 1, characterized in that: The obtaining of yak sample data comprises: Acquiring multiple groups of yak blood sample sets based on a preset sampling period; Performing anticoagulation operation on the multiple groups of yak blood sample sets using a pre-configured anticoagulant to obtain multiple groups of anticoagulated blood sample sets; Performing biochemical index determination on multiple groups of anticoagulated blood sample sets to obtain biochemical index data, wherein the biochemical index data include: multiple groups of serum total protein sets, multiple groups of serum urea nitrogen sets, multiple groups of serum creatinine sets and multiple groups of blood glucose sets; Collect multiple sets of yak periodic weight sets according to preset weighing cycles; Calculate multiple groups of yak daily weight gain sets based on multiple groups of yak periodic weight sets; The periodic body weight sets and daily weight gain sets of multiple groups of yaks were summarized to obtain growth index data.

3. The yak fattening method based on graded biochemical indicators according to claim 2, characterized in that: The method of respectively calculating the multiple groups of indicator mean sets and the multiple groups of indicator standard deviation sets of the yak sample data comprises: Extracting yak data sets of the same group from the yak sample data in sequence, wherein the yak data sets of the same group include: a serum total protein set, a serum urea nitrogen set, a serum creatinine set, a blood sugar set, a yak periodic weight set, and a yak daily weight gain set; The following formula is used to calculate the mean value set of indicators of the same group of yak data sets: Among them, μ i Refers to the mean value of the i-th physiological index in the same group of yak data sets, i is the serial number of the physiological index, n i Refers to the number of samples of the ith physiological index in the same group of yak data sets, x ij Refers to the jth sample value of the i-th physiological index in the same group of yak data sets, where j represents the sample value sequence number, wherein the physiological index refers to a biochemical index or a growth index; Summarizing the indicator mean value sets to obtain multiple groups of indicator mean value sets; The standard deviation of the indicators of the same group of yak data sets is calculated using the following formula: Among them, σ i Refers to the standard deviation of the ith physiological index in the same group of yak data sets; The indicator standard deviation sets are summarized to obtain multiple groups of indicator standard deviation sets.

4. The yak fattening method based on graded biochemical indicators according to claim 3, characterized in that: The calculation of the comprehensive score set of indicators based on the mean value sets of multiple indicators and the standard deviation sets of multiple indicators includes: The indicator adjustment weight set of the same group of yak data sets is obtained, and the indicator comprehensive score is calculated based on the indicator adjustment weight set. The formula is as follows: Among them, S represents the comprehensive score of the index of the p-th group of yak sample data, p represents the group number of the yak sample data, ω1 represents the index adjustment weight of the first physiological index, ω I represents the index adjustment weight of the Ith physiological index, n1 represents the sample size of the first physiological index of the pth group of yak sample data, x 1j represents the jth sample value of the first physiological index of the pth group of yak sample data, μ1 represents the mean value of the first physiological index of the pth group of yak sample data, σ1 represents the standard deviation of the first physiological index of the pth group of yak sample data, n I represents the number of samples of the Ith physiological index of the pth group of yak sample data, x Ij represents the jth sample value of the Ith physiological index of the pth group of yak sample data, μ I represents the mean value of the Ith physiological index of the pth group of yak sample data, σ I represents the standard deviation of the Ith physiological index of the pth group of yak sample data, and I represents the total number of physiological indexes.

5. The yak fattening method based on graded biochemical indicators according to claim 4, characterized in that: The step of obtaining the indicator adjustment weight set of the same group of yak data sets includes: Calculating the indicator correlation of each physiological indicator in the same group of yak data sets to obtain an indicator correlation set; According to the preset initial indicator weight set and indicator correlation set, the indicator adjustment weight is calculated using the following formula: Among them, ω i refers to the index adjustment weight of the i-th physiological index, ω i ′ represents the initial indicator weight of the i-th physiological indicator, corr(x ij ,μ i ) refers to x ij With μ i The correlation between indicators.

6. The yak fattening method based on graded biochemical indicators according to claim 4, characterized in that: The health status of the indicator comprehensive score set is graded by using preset graded biochemical indicators to obtain multiple groups of graded comprehensive score sets, including: Extracting the indicator comprehensive scores in the indicator comprehensive score set in sequence, obtaining the upper limit of the morbid score interval based on the morbid state score interval, and comparing the indicator comprehensive score with the upper limit of the morbid score interval; If the comprehensive score of the indicator is not greater than the upper limit of the pathological score range, the health status of the comprehensive score of the indicator is confirmed to be at the pathological level; If the comprehensive index score is greater than the upper limit of the pathological score interval, the upper limit of the subhealth score interval is obtained, and the comprehensive index score is compared with the upper limit of the subhealth score interval; If the comprehensive score of the index is not greater than the upper limit of the sub-health score range, then the health status of the comprehensive score of the index is confirmed to be a sub-health level; If the comprehensive score of the index is greater than the upper limit of the sub-health score range, the health status of the comprehensive score of the index is confirmed to be the health level; The morbid comprehensive score set, subhealthy comprehensive score set and healthy comprehensive score set corresponding to the morbid level, subhealthy level and healthy level are respectively identified to obtain multiple groups of graded comprehensive score sets.

7. The yak fattening method based on graded biochemical indicators according to claim 1, characterized in that: The fattening constraint condition set is constructed according to the unit indicator demand set, including: Obtain the unit index requirement of energy, unit index requirement of crude protein, unit index requirement of crude fiber, unit index requirement of vitamins, unit index requirement of calcium and unit index requirement of phosphorus of health-level yaks; Based on the unit index requirements of energy, crude protein, crude fiber, vitamins, calcium and phosphorus of health-grade yaks, the following formula is used to construct the fattening constraint condition set of health-grade yaks: m∈[1,6],m∈N q∈[1,Q],q∈N r∈(1,2,3) X1+…+X Q =1 X1>0,…,X Q >0 Among them, q represents the feed number, Q represents the number of fattening feed categories, and m represents the nutritional index number. X represents the nutritional supply of the mth nutritional index of the rth healthy yak that can be provided by the unit mass of fattening feed with feed number q, q Indicates the unit weight of the fattening feed with feed number q. represents the unit index requirement of the mth nutritional index for the rth healthy yak, X Q It represents the unit weight of the fattening feed with feed number Q, and X1 represents the unit weight of the fattening feed with feed number 1.

8. The yak fattening method based on graded biochemical indicators according to claim 7, characterized in that: The target fattening function is constructed based on the unit feed cost threshold, including: The unit feed cost set corresponding to the feed category set is obtained, and a target fattening function is constructed based on the unit feed cost threshold and the unit feed cost set. The target fattening function is as follows: Among them, M min represents the unit feed cost threshold, N q Represents the unit mass cost of fattening feed with feed number q.

9. The yak fattening method based on graded biochemical indicators according to claim 8, characterized in that: The method of using a pre-built linear programming solver to solve the fattening constraint condition set and the target fattening function to obtain an optimal fattening feed ratio includes: Constructing a linear programming model using the fattening constraint condition set and the target fattening function; The linear programming model is input into a pre-built linear programming solver, and the optimal fattening feed ratio is calculated.

10. A yak fattening system based on graded biochemical indicators, characterized in that: The system comprises: A sample data acquisition module, used to acquire yak sample data, wherein the yak sample data includes: biochemical index data and growth index data, and respectively calculate multiple groups of index mean sets and multiple groups of index standard deviation sets of the yak sample data, wherein the multiple groups of index mean sets include: biochemical mean set and growth mean set, and the multiple groups of index standard deviation sets include: biochemical standard deviation set and growth standard deviation set; A health status grading module is used to calculate an indicator comprehensive score set based on multiple groups of indicator mean sets and multiple groups of indicator standard deviation sets, perform health status grading on the indicator comprehensive score set through preset graded biochemical indicators, obtain multiple groups of graded comprehensive score sets, identify the group of yaks to be fattened corresponding to each group of graded comprehensive score sets, and obtain multiple groups of yaks to be fattened, wherein the graded biochemical indicators include: a healthy state score interval, a sub-healthy state score interval, and a morbid state score interval; The fattening constraint construction module is used to obtain the unit index requirement set of each group of yaks to be fattened, and to construct a fattening constraint condition set according to the unit index requirement set, wherein the fattening constraint condition set includes: Energy requirement constraints, crude protein constraints, crude fiber constraints, vitamin constraints, calcium constraints, and phosphorus constraints; The optimal feed ratio module is used to obtain a unit feed cost threshold, construct a target fattening function based on the unit feed cost threshold, use a pre-built linear programming solver to solve the fattening constraint set and the target fattening function, and obtain an optimal fattening feed ratio. According to the optimal fattening feed ratio, multiple groups of yaks to be fattened are fattened.

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