Yak breeding amount optimization method and system based on analytic hierarchy process

Abstract

The invention relates to the technical field of curved surface construction, in particular to a yak breeding amount optimization method and system based on an analytic hierarchy process, and the method comprises the steps: obtaining a reference yak population data set, confirming an excellent yak population data set and eliminating the yak population data set, matching an excellent yak population, calculating the density of the excellent population, and obtaining hierarchical factors. The method comprises the steps of collecting excellent nutrition data, matching an eliminated yak population, calculating the density of the eliminated population, collecting eliminated nutrition data, establishing an analysis criterion layer, constructing an analysis matrix, calculating criterion weight, calculating an excellent evaluation value, calculating an elimination evaluation value, constructing a breeding curved surface, and obtaining optimized breeding parameters according to the breeding curved surface. And making an optimized propagation scheme based on the optimized propagation parameters. The yak breeding amount can be increased.

Description

Technical Field

[0001] The present invention relates to the technical field of surface construction, and in particular to a yak reproduction optimization method and system based on a hierarchical analysis method. Background Art

[0002] With the modernization of animal husbandry, yaks, as an important part of the plateau ecosystem, have great significance for optimizing their reproduction to increase meat and dairy production, enhance the health of yak populations and ensure ecological balance.

[0003] Traditional methods for optimizing yak breeding output usually rely on empirical judgment and simple manual screening, and lack comprehensive analysis of multidimensional data such as population status, environmental factors, and nutritional needs. At the same time, traditional methods for optimizing yak breeding output are difficult to adjust and optimize in a timely manner, resulting in the inability to flexibly respond to the influence of population density and nutritional composition during yak breeding, affecting the reproductive efficiency of the yak population. Therefore, how to improve yak breeding output is an important issue that needs to be solved urgently. Summary of the Invention

[0004] The present invention provides a yak reproduction optimization method and system based on the hierarchical analysis method, the main purpose of which is to improve the reproduction of yaks.

[0005] To achieve the above-mentioned object, the present invention provides a method for optimizing yak reproduction based on the analytic hierarchy process, comprising:

[0006] Obtain a reference yak population dataset, and identify a high-quality yak population dataset and a culled yak population dataset based on the reference yak population dataset;

[0007] Based on the excellent yak population dataset, the corresponding excellent yak population is matched, the excellent population density is calculated based on the excellent yak population, and the hierarchical factors are obtained. The hierarchical factors are composed of grassland environment, population density, nutritional composition and climate environment.

[0008] Performing nutritional sampling on the superior yak population to obtain superior nutritional data, matching the corresponding culled yak population according to the culled yak population data set, calculating the culled population density based on the culled yak population, and performing nutritional sampling on the culled yak population to obtain culled nutritional data;

[0009] Transmitting the superior population density, superior nutritional data, eliminated population density, and eliminated nutritional data to a pre-built data processing unit, and establishing an analysis criterion layer based on the grassland environment, population density, nutritional composition, and climate environment;

[0010] constructing an analysis matrix using the analysis criterion layer, and calculating criterion weights according to the analysis matrix;

[0011] Calculate the excellent evaluation value according to the criterion weights and the excellent yak population, and calculate the elimination evaluation value according to the criterion weights and the eliminated yak population;

[0012] Based on the excellent evaluation value, the elimination evaluation value, the excellent population density, the excellent nutrition data, the eliminated population density, the eliminated nutrition data received by the data processing unit, and the data processing unit, construct a reproduction surface, obtain optimized reproduction parameters according to the reproduction surface, and formulate an optimized reproduction plan based on the optimized reproduction parameters.

[0013] Optionally, the confirmation of the excellent yak population dataset and the eliminated yak population dataset from the reference yak population dataset includes:

[0014] Obtain judgment parameters, where the judgment parameters include: population reproduction rate, population survival rate, yak weight value, and yak height value;

[0015] Construct a yak evaluation function based on the population reproduction rate, the population survival rate, the yak weight value, and the yak height value;

[0016] Perform an evaluation calculation operation on all the reference yak population data in the reference yak population dataset using the yak evaluation function to obtain a yak population evaluation value set;

[0017] Extract the yak population evaluation values from the yak population evaluation value set in sequence, and perform the following operations on the extracted yak population evaluation values:

[0018] Remove the extracted yak population evaluation value from the yak population evaluation value set to obtain a yak population combination set;

[0019] Compare the yak population evaluation value with a preset population evaluation threshold;

[0020] When the yak population evaluation value is greater than the population evaluation threshold, confirm the yak population evaluation value as excellent population data, update the yak population evaluation value set using the yak population combination set, and use the updated yak population evaluation value set to return to the above step of extracting the yak population evaluation values from the yak population evaluation value set in sequence until the yak population evaluation value set is an empty set;

[0021] When the yak population evaluation value is not greater than the population evaluation threshold, confirm the yak population evaluation value as eliminated population data, update the yak population evaluation value set using the yak population combination set, and use the updated yak population evaluation value set to return to the above step of extracting the yak population evaluation values from the yak population evaluation value set in sequence until the yak population evaluation value set is an empty set;

[0022] Collect the excellent population data to obtain an excellent yak population dataset, and collect the eliminated population data to obtain an eliminated yak population dataset.

[0023] Optionally, constructing a yak evaluation function based on the population reproduction rate, population survival rate, yak weight value and yak height value includes:

[0024] A yak evaluation function is constructed based on the preset saturation adjustment parameter, the preset evaluation adjustment parameter, the population reproduction rate, the population survival rate, the yak weight value, and the yak height value. The yak evaluation function is as follows:

[0025]

[0026] Among them, Q refers to the yak evaluation function, w1 refers to the population reproduction rate, w2 refers to the population survival rate, e refers to the natural constant, β refers to the saturation adjustment parameter, α refers to the evaluation adjustment parameter, r1 refers to the yak weight value, t1 refers to the preset weight influence parameter, r2 refers to the yak height value, t2 refers to the preset height influence parameter, and γ refers to the preset combination adjustment parameter.

[0027] Optionally, the nutritional sampling of the excellent yak population to obtain excellent nutritional data includes:

[0028] Acquire a forage feeding area, perform random sampling in the forage feeding area based on a preset sample quantity to obtain sampled forage, and perform a collection operation on the sampled forage to obtain a sampled forage set;

[0029] Obtaining high-quality yak feed, performing nutritional analysis on the high-quality yak feed and the sampled forage set based on a preset number of detections, and obtaining a feed nutrient composition set and a forage nutrient composition set;

[0030] The mean value is calculated based on the feed nutrient component set and the forage nutrient component set to obtain excellent nutritional data.

[0031] Optionally, before transmitting the high-quality population density, high-quality nutritional data, eliminated population density and eliminated nutritional data to a pre-built data processing unit, the method further includes:

[0032] Calculating the processing busyness of the data processing unit;

[0033] Comparing the processing busyness with a preset busyness threshold;

[0034] If the processing busyness is greater than the busyness threshold, the excellent population density, excellent nutritional data, eliminated population density and eliminated nutritional data are stored in a pre-constructed data temporary storage unit, and the process returns to the above step of calculating the processing busyness of the data processing unit until the processing busyness is no greater than the busyness threshold, and the excellent population density, excellent nutritional data, eliminated population density and eliminated nutritional data in the data temporary storage unit are transmitted to the data processing unit;

[0035] If the processing busy degree is not greater than the busy degree threshold, transmit the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data to the data processing unit.

[0036] Optionally, calculating the processing busy degree of the data processing unit includes:

[0037] Obtain the received data volume, data processing density, data calculation volume, system available resources, transmission delay, and storage access performance of the data processing unit, and calculate the processing busy degree based on the received data volume, data processing density, data calculation volume, system available resources, transmission delay, and storage access performance:

[0038]

[0039] Where, W refers to the processing busy degree, R refers to the received data volume, T refers to the data processing density, Y refers to the data calculation volume, E refers to the system available resources, δ refers to the preset transmission parameter, I refers to the transmission delay, and U refers to the storage access performance.

[0040] Optionally, constructing the analysis matrix by using the analysis criterion layer includes:

[0041] Obtain the relative proportion according to the analysis criterion layer, gather the relative proportions to obtain a relative proportion set, where the relative proportion set includes: grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion;

[0042] Construct an analysis matrix based on the grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion:

[0043]

[0044] Where, G refers to the analysis matrix, ρ1 refers to the population grassland proportion, ρ2 refers to the population climate proportion, ρ3 refers to the population nutrition proportion, ρ4 refers to the grassland population proportion, ρ5 refers to the grassland climate proportion, ρ6 refers to the grassland nutrition proportion, ρ7 refers to the climate population proportion, ρ8 refers to the climate grassland proportion, ρ9 refers to the climate nutrition proportion, ρ 10 refers to the nutrition population proportion, ρ 11 refers to the nutrition grassland proportion, ρ 12 refers to the nutrition climate proportion.

[0045] Optionally, calculating the criterion weight according to the analysis matrix includes:

[0046] Calculate the analysis vector based on the analysis matrix, calculate the maximum eigenvalue of the analysis matrix, and calculate the analysis weight according to the analysis vector and the maximum eigenvalue;

[0047] Obtain the matrix order of the analysis matrix, and calculate the consistency ratio based on the maximum eigenvalue and the matrix order:

[0048]

[0049] Where, F refers to the consistency ratio, l refers to the maximum eigenvalue, z refers to the matrix order, and c refers to the preset consistency index value;

[0050] Compare the consistency ratio with the preset consistency threshold;

[0051] If the consistency ratio is greater than or equal to the consistency threshold, construct an update matrix, update the analysis matrix using the update matrix, and return to the step of calculating the analysis vector based on the analysis matrix as described above using the updated analysis matrix;

[0052] If the consistency ratio is less than the consistency threshold, confirm the analysis weight as the criterion weight.

[0053] Optionally, the calculation of the excellent evaluation value according to the criterion weight and the excellent yak population includes:

[0054] Construct a yak population scoring scale, and calculate the population density score, soil organic matter content score, soil acid-base score, protein content score, fat content score, temperature score, humidity score, and precipitation score of the excellent yak population based on the yak population scoring scale;

[0055] Perform mean calculation according to the soil organic matter content score and the soil acid-base score to obtain the grassland environment score, perform mean calculation according to the protein content score and the fat content score to obtain the nutrient component score, and perform mean calculation according to the temperature score, humidity score, and precipitation score to obtain the climate environment score;

[0056] Calculate the excellent evaluation value based on the criterion weight, population density score, grassland environment score, nutrient component score, and climate environment score.

[0057] To achieve the above object, the present invention also provides a yak breeding quantity optimization system based on the analytic hierarchy process, including:

[0058] A population density calculation module, configured to obtain a reference yak population data set, and confirm an excellent yak population data set and an eliminated yak population data set according to the reference yak population data set;

[0059] Match the corresponding excellent yak population based on the excellent yak population dataset, calculate the excellent population density according to the excellent yak population, and obtain the hierarchical factors, where the hierarchical factors are composed of grassland environment, population density, nutritional components, and climate environment;

[0060] The analysis criterion construction module is used to perform nutritional sampling on the excellent yak population to obtain excellent nutritional data, match the corresponding eliminated yak population according to the eliminated yak population dataset, calculate the eliminated population density based on the eliminated yak population, and perform nutritional sampling on the eliminated yak population to obtain eliminated nutritional data;

[0061] Transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to the pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment;

[0062] The evaluation value calculation module is used to construct an analysis matrix using the analysis criterion layer and calculate the criterion weight according to the analysis matrix;

[0063] Calculate the excellent evaluation value according to the criterion weight and the excellent yak population, and calculate the eliminated evaluation value according to the criterion weight and the eliminated yak population;

[0064] The breeding plan formulation module is used to construct a breeding surface based on the excellent evaluation value, eliminated evaluation value, excellent population density, excellent nutritional data, eliminated population density, eliminated nutritional data received by the data processing unit, and the data processing unit, obtain optimized breeding parameters according to the breeding surface, and formulate an optimized breeding plan based on the optimized breeding parameters.

[0065] To solve the above problems, the present invention also provides an electronic device, which includes:

[0066] A memory that stores at least one instruction;

[0067] A processor that executes the instructions stored in the memory to implement the above-mentioned method for optimizing yak breeding quantity based on the analytic hierarchy process.

[0068] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for optimizing yak breeding quantity based on the analytic hierarchy process.

[0069] To solve the problems described in the background art, first, according to the obtained reference yak population dataset, the excellent yak population dataset and the eliminated yak population dataset are identified. The division of the excellent yak population dataset and the eliminated yak population dataset provides accurate and reliable data for formulating subsequent optimized breeding plans, improving the reliability of the optimized breeding plans. Secondly, the corresponding excellent yak populations are matched according to the excellent yak population dataset, the excellent population density of the excellent yak populations is calculated, and hierarchical factors are obtained. By calculating the excellent population density of the excellent yak populations, it is possible to more accurately understand how to reasonably place yaks to ensure better resource utilization and yak breeding volume. A reasonable population density helps reduce resource competition and improve the yak breeding volume and the growth rate of yaks. The hierarchical factors consist of four factors: grassland environment, population density, nutritional components, and climate environment. By comprehensively considering the four factors of grassland environment, population density, nutritional components, and climate environment, the yak breeding volume can be more comprehensively increased. After that, nutritional sampling is carried out on the excellent yak populations to obtain excellent nutritional data, and nutritional sampling is carried out on the eliminated yak populations to obtain eliminated nutritional data. Conducting nutritional sampling on the excellent yak populations and the eliminated yak populations respectively can accurately evaluate the nutritional status of these two types of yak populations. By comparing the excellent nutritional data with the eliminated nutritional data, the forage and feed can be adjusted targeted to increase the yak breeding volume. Further, the excellent evaluation value is calculated according to the criterion weights and the excellent yak populations, and the eliminated evaluation value is calculated according to the criterion weights and the eliminated yak populations. Calculating the excellent evaluation value and the eliminated evaluation value can help us better understand the overall situation of the yak populations. The excellent evaluation value and the eliminated evaluation value can directly reflect the breeding potential of the populations. Finally, a breeding surface is constructed, the optimized breeding parameters are obtained according to the breeding surface, and an optimized breeding plan is formulated based on the optimized breeding parameters. Through the optimized breeding parameters, the most reasonable plan to increase the yak breeding volume can be formulated according to the actual population density and nutritional data. Therefore, the present invention can increase the yak breeding volume. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 FIG. is a schematic flowchart of a method for optimizing yak breeding volume based on the analytic hierarchy process provided by an embodiment of the present invention;

[0071] Figure 2 FIG. is a functional module diagram of a system for optimizing yak breeding volume based on the analytic hierarchy process provided by an embodiment of the present invention;

[0072] Figure 3 FIG. is a schematic structural diagram of an electronic device for implementing the method for optimizing yak breeding volume based on the analytic hierarchy process provided by an embodiment of the present invention.

[0073] DESCRIPTION OF THE REFERENCE NUMERALS:

[0074] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0075] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0077] The embodiments of the present application provide a method for optimizing yak reproduction based on the Analytic Hierarchy Process (AHP). The execution entity of the AHP-based yak reproduction optimization method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiments of the present application. In other words, the AHP-based yak reproduction optimization method 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.

[0078] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing yak reproduction based on the analytic hierarchy process according to an embodiment of the present invention. In this embodiment, the method for optimizing yak reproduction based on the analytic hierarchy process includes:

[0079] S1. Obtain a reference yak population dataset, and identify a high-quality yak population dataset and a eliminated yak population dataset based on the reference yak population dataset.

[0080] Interpretable, the reference yak population dataset refers to a collection of reference yak population data. Reference yak population data refers to the data of a yak population. A yak population refers to a group of yaks living in the same ecological environment and the same geographical area. The individual yaks in a yak population have certain genetic similarities and can reproduce in the same or similar environments. For example, a yak population lives in area A at an altitude of 4,000 meters on the Qinghai-Tibet Plateau. Area A at an altitude of 4,000 meters on the Qinghai-Tibet Plateau refers to a unified ecological environment and the same geographical area. Reference yak population data includes: population reproduction rate, population survival rate, yak weight, and yak height. The population reproduction rate refers to the reproductive rate of a yak population. The population survival rate refers to the ratio of surviving yak calves in a population to the initial number of yak calves after a specific period of time. The specific period of time refers to the time period for determining the survival rate of a yak population. The specific period of time can be 90 days. Within 90 days after birth, yak calves gradually transition from relying on breast milk to consuming forage. During this period, yak calves are able to eat relatively independently. Surviving individuals refer to yak calves that are still alive after the specific period of time. The initial number of yak calves refers to the total number of yak calves at the beginning. For example, if a yak population B produces 100 yak calves, and after a specific period of time of 90 days, 60 of the 100 yak calves survive, then 60 are considered surviving individuals and 100 are considered the initial number of yak calves. The yak weight value refers to the weight of the yak, and the yak height value refers to the height of the yak.

[0081] In detail, the identification of the excellent yak population dataset and the eliminated yak population dataset based on the reference yak population dataset includes:

[0082] Obtaining evaluation parameters, wherein the evaluation parameters include: population reproduction rate, population survival rate, yak weight value and yak height value;

[0083] Constructing a yak evaluation function based on the population reproduction rate, population survival rate, yak weight value and yak height value;

[0084] Using the yak evaluation function to perform an evaluation calculation operation on all reference yak population data in the reference yak population data set to obtain a yak population evaluation value set;

[0085] Extracting yak population assessment values from the yak population assessment value set in sequence, and performing the following operations on the extracted yak population assessment values:

[0086] Eliminating the extracted yak population assessment value from the yak population assessment value set to obtain a yak population combination set;

[0087] Comparison of yak population assessment values with pre-set population assessment thresholds;

[0088] When the yak population evaluation value is greater than the population evaluation threshold, the yak population evaluation value is confirmed as excellent population data, the yak population evaluation value set is updated using the yak population combination set, and the above step of sequentially extracting the yak population evaluation value from the yak population evaluation value set is returned using the updated yak population evaluation value set until the yak population evaluation value set is an empty set;

[0089] When the yak population evaluation value is not greater than the population evaluation threshold, the yak population evaluation value is confirmed as eliminated population data, the yak population evaluation value set is updated using the yak population combination set, and the above step of sequentially extracting the yak population evaluation value from the yak population evaluation value set is returned using the updated yak population evaluation value set until the yak population evaluation value set is an empty set;

[0090] Collect the excellent population data to obtain an excellent yak population data set, and collect the eliminated population data to obtain an eliminated yak population data set.

[0091] Interpretably, the judgment parameter refers to the parameter for judging the reference yak population data in the reference yak population data set, the yak evaluation function refers to the function constructed based on the population reproduction rate, population survival rate, yak weight value, and yak height value for performing an evaluation operation on all the reference yak population data in the reference yak population data set. The yak population evaluation value can be obtained according to the yak evaluation function. Based on the yak population evaluation value and the population evaluation threshold, it can be judged whether the reference yak population data is excellent population data. The yak population evaluation value set refers to the set composed of yak population evaluation values, and the yak population evaluation value refers to the value obtained by performing an evaluation calculation operation on the reference yak population data using the yak evaluation function. The yak population combination set refers to the set obtained after removing the extracted yak population evaluation value from the yak population evaluation value set. The population evaluation threshold refers to the value artificially set for determining the yak population evaluation value. The excellent population data refers to the reference yak population data whose yak population evaluation value is greater than the population evaluation threshold, and the eliminated population data refers to the reference yak population data whose yak population evaluation value is not greater than the population evaluation threshold. For example, if the population evaluation threshold is 5 and the yak population evaluation value of the reference yak population data a is 6, then the reference yak population data a is excellent population data. The excellent yak population data set refers to the set composed of excellent yak population data, and the eliminated yak population data set refers to the set composed of eliminated yak population data.

[0092] Specifically, constructing the yak evaluation function based on the population reproduction rate, population survival rate, yak weight value, and yak height value includes:

[0093] Construct a yak evaluation function based on a preset saturation adjustment parameter, a preset evaluation adjustment parameter, the population reproduction rate, the population survival rate, the yak weight value, and the yak height value, where the yak evaluation function is as follows:

[0094]

[0095] Among them, Q refers to the yak evaluation function, w1 refers to the population reproduction rate, w2 refers to the population survival rate, e refers to the natural constant, β refers to the saturation adjustment parameter, α refers to the evaluation adjustment parameter, r1 refers to the yak weight value, t1 refers to the preset weight influence parameter, r2 refers to the yak height value, and t2 refers to the preset height influence parameter, and γ refers to the preset combined adjustment parameter.

[0096] Interpretably, the saturation adjustment parameter refers to the parameter used to control the influence degree of w1 + w2 on the yak population evaluation value. The larger the saturation adjustment parameter, the greater the influence degree of w1 + w2 on the yak population evaluation value. The evaluation adjustment parameter refers to the parameter used to control the influence degree on the yak population evaluation value. The larger the evaluation adjustment parameter, the greater the influence degree on the yak population evaluation value. The weight influence parameter refers to the parameter used to control the influence degree of the yak weight value on the yak population evaluation value. The larger the weight influence parameter, the greater the influence degree of the yak weight value on the yak population evaluation value. The height influence parameter refers to the parameter used to control the influence degree of the yak height value on the yak population evaluation value. The larger the height influence parameter, the greater the influence degree of the yak height value on the yak population evaluation value. The combined adjustment parameter refers to the parameter used to control the influence degree of r1 × r2 on the yak population evaluation value. For example, the larger the yak weight value, the greater the influence degree of the yak weight value on the yak population evaluation value. Therefore, the weight influence parameter is set to a relatively large value to correctly reflect the influence of the yak weight value on the yak population evaluation value.

[0097] S2. Match the corresponding excellent yak population based on the excellent yak population dataset, calculate the excellent population density according to the excellent yak population, and obtain the hierarchical factors, where the hierarchical factors are composed of the grassland environment, population density, nutritional components, and climate environment.

[0098] Interpretable, an excellent yak population refers to the yak population corresponding to the excellent yak population data in the excellent yak population dataset. Matching the corresponding excellent yak population based on the excellent yak population dataset means matching the corresponding yak population according to the excellent yak population data in the excellent yak population dataset. Calculating the excellent population density based on the excellent yak population means calculating the population density of each excellent yak population and performing an average calculation operation on the population densities of all excellent yak populations to obtain the excellent population density. For example, if there are three excellent yak populations, the population density of the first excellent yak population is 10 heads per hectare, the population density of the second excellent yak population is 20 heads per hectare, and the population density of the third excellent yak population is 6 heads per hectare, then the excellent population density is 36 / 3 = 12 heads per hectare. Population density refers to the ratio of the number of population individuals to the habitat area. The number of population individuals refers to the number of yaks within the yak population, and the habitat area refers to the area of the living region of the yak population. Hierarchical factors refer to the parameters for constructing the analysis criterion layer. Hierarchical factors consist of grassland environment, population density, nutritional components, and climate environment. The grassland environment includes: soil organic matter content and soil pH value. Nutritional components refer to the components of the food ingested by yaks. Nutritional components include: protein and fat. The climate environment refers to the temperature, humidity, and precipitation in the area where the yak population lives. Soil organic matter content refers to the total amount of organic matter in the soil. Organic matter is an important indicator for measuring soil fertility. Organic matter includes: soil humus, organic waste, and soil microorganisms. For example, the leaves, roots of plants, and remains of animals and plants are all organic waste, humic acid, humic alkali, and humic acid particles are all soil humus, and bacteria, fungi, actinomycetes, and algae are all soil microorganisms. Soil pH value refers to the value of the acidity or alkalinity of the soil.

[0099] S3. Conduct nutritional sampling on the excellent yak population to obtain excellent nutritional data, match the corresponding eliminated yak population based on the eliminated yak population dataset, calculate the eliminated population density based on the eliminated yak population, and conduct nutritional sampling on the eliminated yak population to obtain eliminated nutritional data.

[0100] Interpretable, excellent nutritional data refers to the data of the nutritional components contained in the food eaten by the excellent yak population. The eliminated yak population refers to the yak population corresponding to the eliminated yak population data in the eliminated yak population dataset. The eliminated population density refers to the population density of the eliminated yak population. The eliminated nutritional data refers to the data of the nutritional components contained in the food eaten by the eliminated yak population.

[0101] Specifically, the conducting of nutritional sampling on the excellent yak population to obtain excellent nutritional data includes:

[0102] Obtain the forage feeding area, conduct random sampling within the forage feeding area based on a preset sample quantity to obtain sampled forage, and perform a pooling operation on the sampled forage to obtain a sampled forage set;

[0103] Obtain high-quality yak feed, and perform nutritional analysis on the high-quality yak feed and the sampled forage grass set respectively based on the preset number of detections to obtain a feed nutrient composition set and a forage grass nutrient composition set;

[0104] Perform mean calculation based on the feed nutrient composition set and the forage grass nutrient composition set to obtain high-quality nutrient data.

[0105] Explainable, the forage grass feeding area refers to the grassland area where the high-quality yak population feeds. For example, the high-quality yak population a feeds at area B with an altitude of 4000 meters on the Qinghai-Tibet Plateau, and area B is the forage grass feeding area. The sample quantity refers to the number of sampling points, and the sampling point refers to the position where random sampling is carried out within the forage grass feeding area. For example, the sample quantity is 4, and based on the sample quantity, random sampling is carried out within the forage grass feeding area to obtain the sampled forage grass at four different positions within the forage grass feeding area. The sampled forage grass refers to the forage grass obtained after random sampling within the forage grass feeding area, and the sampled forage grass set refers to the set composed of the sampled forage grass. The high-quality yak feed refers to the feed eaten by the high-quality yak population, and the number of detections refers to the number of times of nutritional analysis on the high-quality yak feed and the sampled forage grass set. For example, the number of detections is 3 times, and according to the number of detections, nutritional analysis is carried out on the high-quality yak feed and the sampled forage grass set 3 times respectively. The feed nutrient composition set refers to the set composed of the feed nutrient components, and the feed nutrient component refers to the nutrient component of the high-quality yak feed obtained by performing nutritional analysis on the high-quality yak feed. The forage grass nutrient composition set refers to the set composed of the forage grass nutrient components, and the forage grass nutrient component refers to the nutrient component of the sampled forage grass obtained by performing nutritional analysis on the sampled forage grass. Performing mean calculation based on the feed nutrient composition set and the forage grass nutrient composition set means performing mean calculation on the feed nutrient components in the feed nutrient composition set to obtain the average feed nutrient components, performing mean calculation on the forage grass nutrient components in the forage grass nutrient composition set to obtain the average forage grass nutrient components, and summarizing the corresponding nutrient components in the average feed nutrient components and the average forage grass nutrient components to obtain high-quality nutrient data. For example, the number of detections is 3, the forage grass nutrient composition set includes forage grass nutrient component 1, forage grass nutrient component 2, and forage grass nutrient component 3. In forage grass nutrient component 1, the protein is 180 g / kg and the fat is 12 g / kg. In forage grass nutrient component 2, the protein is 190 g / kg and the fat is 10 g / kg. In forage grass nutrient component 3, the protein is 170 g / kg and the fat is 12 g / kg. Performing mean calculation on the forage grass nutrient composition set, the protein is obtained as 180 g / kg and the fat is 11.3 g / kg. The method of performing mean calculation on the feed nutrient composition set is the same as that of performing mean calculation on the forage grass nutrient composition set, and will not be elaborated here.

[0106] S4. Transmit the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data to a pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment.

[0107] Interpretably, the data processing unit refers to a unit used to receive the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data and construct a reproduction surface. The analysis criterion layer refers to the criterion layer in the analytic hierarchy process, which is prior art and will not be elaborated here.

[0108] Specifically, before transmitting the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data to the pre-constructed data processing unit, it further includes:

[0109] Calculate the processing busy degree of the data processing unit;

[0110] Compare the processing busy degree with a preset busy degree threshold;

[0111] If the processing busy degree is greater than the busy degree threshold, store the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data in a pre-constructed data temporary storage unit, and return to the step of calculating the processing busy degree of the data processing unit above until the processing busy degree is not greater than the busy degree threshold, then transmit the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data in the data temporary storage unit to the data processing unit;

[0112] If the processing busy degree is not greater than the busy degree threshold, transmit the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data to the data processing unit.

[0113] It can be explained that processing busyness is a quantification of the load on the data processing unit. The greater the processing busyness, the greater the load on the data processing unit, and the data processing unit may fail. The busyness threshold refers to a threshold set manually for judging processing busyness. The data temporary storage unit refers to a unit used to temporarily store data when the processing busyness exceeds the busyness threshold, acting as an intermediate cache during the data processing process. When the processing busyness exceeds the busyness threshold, the data processing unit cannot process all data in real time. As a data storage unit, the data temporary storage unit can temporarily store data and transfer the data from the data temporary storage unit to the data processing unit when the processing busyness of the data processing unit is no greater than the busyness threshold. When the processing busyness of the data processing unit is greater than the busyness threshold, the data is directly processed, resulting in a decrease in the stability of the data processing unit. After the data is stored in the data temporary storage unit, the data processing process is divided into multiple stages, avoiding overload. This allows the data processing unit to process the data stored in the data temporary storage unit when the processing busyness is no greater than the busyness threshold, improving the stability of the data processing unit during operation.

[0114] In detail, the calculating of the processing busyness of the data processing unit includes:

[0115] Obtaining the amount of received data, data processing density, data calculation amount, system available resources, transmission delay, and storage access performance of the data processing unit, and calculating the processing busyness based on the amount of received data, data processing density, data calculation amount, system available resources, transmission delay, and storage access performance:

[0116]

[0117] Among them, W refers to processing busyness, R refers to the amount of received data, T refers to data processing density, Y refers to data computing amount, E refers to system available resources, δ refers to preset transmission parameters, I refers to transmission delay, and U refers to storage access performance.

[0118] Interpretability: The received data volume refers to the amount of data received by the data processing unit, with the unit of byte. The larger the received data volume, the more data the data processing unit needs to process, and the higher the processing busy degree of the data processing unit will be. The data processing density refers to the amount of data that the data processing unit can process per unit time. The data calculation amount refers to the amount of calculation required when the data processing unit processes data. The system available resources refer to the unoccupied rate of the CPU of the data processing unit. For example, if the CPU occupancy rate of the data processing unit is 70%, then the unoccupied rate of the CPU of the data processing unit is 30%. The transmission delay refers to the delay during the data transmission process. The transmission process refers to the process of data transmission to the data processing unit. For example, if it takes 0.1 s for the data to be transmitted to the data processing unit, then 0.1 s is the transmission delay. The storage access performance refers to the read / write speed of the data processing unit, with the unit of MB / s. The transmission parameter refers to a parameter manually set to adjust the degree of influence on the processing busy degree. The larger the transmission parameter, the greater the degree of influence on the processing busy degree. For example, the greater the transmission delay and the smaller the storage access performance, the greater it is, the greater the influence on the processing busy degree. Therefore, the transmission parameter is set to a relatively large value.

[0119] S5. Construct an analysis matrix using the analysis criterion layer, and calculate the criterion weights according to the analysis matrix.

[0120] Interpretability: The analysis matrix refers to a matrix that integrates the grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion. The analysis matrix is used to quantify the relationships among the grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion. The criterion weights refer to the values of the grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion obtained by calculating the analysis matrix using the eigenvalue decomposition method. The eigenvalue decomposition method is a prior art and will not be elaborated here.

[0121] Specifically, the construction of the analysis matrix using the analysis criterion layer includes:

[0122] Obtain the relative weights according to the analysis criterion layer, aggregate the relative weights to obtain a set of relative weights, where the set of relative weights includes: grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion;

[0123] Construct an analysis matrix based on the grassland population proportion, population grassland proportion, grassland nutrition proportion, nutrition grassland proportion, grassland climate proportion, climate grassland proportion, population nutrition proportion, nutrition population proportion, population climate proportion, climate population proportion, nutrition climate proportion, and climate nutrition proportion:

[0124]

[0125] Among them, G refers to the analysis matrix, ρ1 refers to the population grassland proportion, ρ2 refers to the population climate proportion, ρ3 refers to the population nutrition proportion, ρ4 refers to the grassland population proportion, ρ5 refers to the grassland climate proportion, ρ6 refers to the grassland nutrition proportion, ρ7 refers to the climate population proportion, ρ8 refers to the climate grassland proportion, ρ9 refers to the climate nutrition proportion, ρ 10 refers to the nutrition population proportion, ρ 11 refers to the nutrition grassland proportion, ρ 12 refers to the nutrition climate proportion.

[0126] Interpretability. The relative weight refers to the relative importance between the grassland environment, population density, nutritional components, and climate environment in pairs. The relative weight is represented by nine numbers from 1 to 9. For example, if the relative weight of the grassland environment relative to the population density is 1, it means that the grassland environment is equally important as the population density. The larger the number of the relative weight, the greater the importance of the grassland environment compared to the population density. The relative weight is obtained by experts in the field of yaks. The relative weight is an important component in the analytic hierarchy process and is an existing technology, so it will not be elaborated here. The relative weight set refers to the set composed of relative weights. The grassland population weight refers to a parameter that reflects the degree of influence of the grassland environment relative to the population density on the yak reproduction quantity. For example, if the grassland population weight is 1, then the degree of influence of the grassland environment on the yak reproduction quantity is the same as that of the population density on the yak reproduction quantity. The population grassland weight refers to a parameter that reflects the degree of influence of the population density relative to the grassland environment on the yak reproduction quantity. The grassland nutrition weight refers to a parameter that reflects the degree of influence of the grassland environment relative to the nutritional components on the yak reproduction quantity. The nutrition grassland weight refers to a parameter that reflects the degree of influence of the nutritional components relative to the grassland environment on the yak reproduction quantity. The grassland climate weight refers to a parameter that reflects the degree of influence of the grassland environment relative to the climate environment on the yak reproduction quantity. The climate grassland weight refers to a parameter that reflects the degree of influence of the climate environment relative to the grassland environment on the yak reproduction quantity. The population nutrition weight refers to a parameter that reflects the degree of influence of the population density relative to the nutritional components on the yak reproduction quantity. The nutrition population weight refers to a parameter that reflects the degree of influence of the nutritional components relative to the population density on the yak reproduction quantity. The population climate weight refers to a parameter that reflects the degree of influence of the population density relative to the climate environment on the yak reproduction quantity. The climate population weight refers to a parameter that reflects the degree of influence of the climate environment relative to the population density on the yak reproduction quantity. The nutrition climate weight refers to a parameter that reflects the degree of influence of the nutritional components relative to the climate environment on the yak reproduction quantity. The climate nutrition weight refers to a parameter that reflects the degree of influence of the climate environment relative to the nutritional components on the yak reproduction quantity.

[0127] Specifically, calculating the criterion weight according to the analysis matrix includes:

[0128] Calculating the analysis vector based on the analysis matrix, calculating the maximum eigenvalue of the analysis matrix, and calculating the analysis weight according to the analysis vector and the maximum eigenvalue;

[0129] Obtaining the matrix order of the analysis matrix, and calculating the consistency ratio based on the maximum eigenvalue and the matrix order:

[0130]

[0131] Wherein, F refers to the consistency ratio, l refers to the maximum eigenvalue, z refers to the matrix order, and c refers to the preset consistency index value;

[0132] Comparing the consistency ratio with the preset consistency threshold;

[0133] If the consistency ratio is greater than or equal to the consistency threshold, an updated matrix is constructed, the analysis matrix is updated using the updated matrix, and the above steps of calculating the analysis vector based on the analysis matrix are returned using the updated analysis matrix;

[0134] If the consistency ratio is less than the consistency threshold, the analysis weights are confirmed as the criterion weights.

[0135] It is understandable that the analysis vector refers to the eigenvector of the analysis matrix, the matrix order refers to the order of the analysis matrix, the matrix order is the same as the number of factors in the hierarchical factors, and the hierarchical factors are composed of grassland environment, population density, nutrient composition and climate environment, so the matrix order is 4th order. The consistency ratio refers to an index used to evaluate the consistency degree of the analysis matrix in the analytic hierarchy process, which is a prior art and will not be elaborated here. The consistency index value refers to a value set according to the matrix order. Optionally, when the matrix order is 4th order, the consistency index value is 0.9. The consistency threshold refers to a value used to judge the consistency ratio. Optionally, the consistency threshold is 0.1. The updated matrix refers to a matrix constructed based on the newly obtained relative weights, and the construction method of the updated matrix is the same as that of the analysis matrix, which will not be elaborated here. The analysis weights refer to the parameters calculated based on the analysis matrix, reflecting the influence degrees of the grassland environment, population density, nutrient composition and climate environment on the yak reproduction quantity. The criterion weights refer to the analysis weights of the analysis matrix when the consistency ratio is less than the consistency threshold, and the criterion weights include: grassland weight, population weight, nutrient weight and climate weight. The grassland weight refers to the influence degree of the grassland environment on the yak reproduction quantity, the population weight refers to the influence degree of the population density on the yak reproduction quantity, the nutrient weight refers to the influence degree of the nutrient composition on the yak reproduction quantity, and the climate weight refers to the influence degree of the climate environment on the yak reproduction quantity. For example, if the grassland weight is 0.2, the population weight is 0.4, the nutrient weight is 0.1, and the climate weight is 0.3, then the influence degree of the grassland environment on the yak reproduction quantity is 20%, the influence degree of the population density on the yak reproduction quantity is 40%, the influence degree of the nutrient composition on the yak reproduction quantity is 10%, and the influence degree of the climate environment on the yak reproduction quantity is 30%.

[0136] S6. Calculate the excellent evaluation value according to the criterion weights and the excellent yak population, and calculate the elimination evaluation value according to the criterion weights and the eliminated yak population.

[0137] Specifically, the calculation of the excellent evaluation value according to the criterion weights and the excellent yak population includes:

[0138] Construct a yak population scoring scale, and calculate the population density score, soil organic matter content score, soil acidity and alkalinity score, protein content score, fat content score, temperature score, humidity score and precipitation score of the excellent yak population based on the yak population scoring scale;

[0139] The average value is calculated based on the soil organic matter content score and the soil acidity-alkalinity score to obtain the grassland environment score. The average value is calculated based on the protein content score and the fat content score to obtain the nutrient component score. The average value is calculated based on the temperature score, the humidity score and the precipitation score to obtain the climate environment score;

[0140] Based on the criterion weights, population density score, grassland environment score, nutrient component score and climate environment score, the excellent evaluation value is calculated.

[0141] Interpretive, the yak population scoring scale refers to the rules artificially set for scoring population density, soil organic matter content, soil pH value, protein content, fat content, temperature, humidity, and precipitation. The yak population scoring scale is divided into four grades: 1, 2, 3, and 4. The yak population scoring scale includes: population density rules, soil organic matter content rules, soil pH value rules, protein content rules, fat content rules, temperature rules, humidity rules, and precipitation rules. The population density rules refer to the rules for classifying population density. The soil organic matter content rules refer to the rules for classifying soil organic matter content. The soil pH value rules refer to the rules for classifying soil pH value. The protein content rules refer to the rules for classifying protein content. The fat content rules refer to the rules for classifying fat content. The temperature rules refer to the rules for classifying the air temperature in the area where the yak population lives. The humidity rules refer to the rules for classifying the relative humidity in the area where the yak population lives. The precipitation rules refer to the rules for classifying the annual precipitation in the area where the yak population lives.Optionally, the population density rule is as follows: when the population density is greater than 30 per square kilometer, the grade is 1; when the population density is between 20 and 30 per square kilometer, the grade is 2; when the population density is between 10 and 20 per square kilometer, the grade is 3; when the population density is less than 10 per square kilometer, the grade is 4. The soil organic matter content rule is as follows: when the soil organic matter content is less than 1%, the grade is 1; when the soil organic matter content is between 1% and 3%, the grade is 2; when the soil organic matter content is between 3% and 5%, the grade is 3; when the soil organic matter content is greater than 5%, the grade is 4. The soil pH value rule is as follows: when the soil pH value is less than 4.5, the grade is 1; when the soil pH value is between 4.5 and 5.5, the grade is 2; when the soil pH value is between 5.5 and 6.0, the grade is 3; when the soil pH value is between 6.0 and 7.5, the grade is 4. The protein content rule is as follows: when the protein content is less than 100 grams per kilogram, the grade is 1; when the protein content is between 100 grams per kilogram and 130 grams per kilogram, the grade is 2; when the protein content is between 130 grams per kilogram and 160 grams per kilogram, the grade is 3; when the protein content is greater than 160 grams per kilogram, the grade is 4. The fat content rule is as follows: when the fat content is less than 30 grams per kilogram, the grade is 1; when the fat content is between 30 grams per kilogram and 50 grams per kilogram, the grade is 2; when the fat content is between 50 grams per kilogram and 70 grams per kilogram, the grade is 3; when the fat content is greater than 70 grams per kilogram, the grade is 4. The temperature rule is as follows: when the temperature is greater than 15°C, the grade is 1; when the temperature is between 10°C and 15°C, the grade is 2; when the temperature is between 5°C and 10°C, the grade is 3; when the temperature is between -5°C and 5°C, the grade is 4. The humidity rule is as follows: when the humidity is greater than 70%, the grade is 1; when the humidity is between 30% and 40%, the grade is 2; when the humidity is between 40% and 50%, the grade is 3; when the humidity is between 50% and 70%, the grade is 4. The precipitation rule is as follows: when the annual precipitation is less than 300 mm, the grade is 1; when the annual precipitation is between 300 mm and 600 mm, the grade is 2; when the annual precipitation is between 600 mm and 1000 mm, the grade is 3; when the annual precipitation is greater than 1000 mm, the grade is 4. The population density score refers to the score obtained after classifying the population density using the population density rule. The size of the score is the same as the size of the grade. For example, if the grade of the population density is 4, then the population density score is also 4.The soil organic matter content score refers to the score obtained after classifying the soil organic matter content according to the soil organic matter content rules. The soil acid-base score refers to the score obtained after classifying the soil pH value according to the soil pH value rules. The protein content score refers to the score obtained after classifying the protein content according to the protein content rules. The fat content score refers to the score obtained after classifying the fat content according to the fat content rules. The temperature score refers to the score obtained after classifying the air temperature according to the temperature rules. The humidity score refers to the score obtained after classifying the relative humidity according to the humidity rules. The precipitation score refers to the score obtained after classifying the annual precipitation according to the precipitation rules. The grassland environment score refers to the score obtained by calculating the average value of the soil organic matter content score and the soil acid-base score. For example, if the soil organic matter content score is 2 and the soil acid-base score is 4, then the grassland environment score is 3. The nutrient component score refers to the score obtained by calculating the average value of the protein content score and the fat content score. The climate environment score refers to the score obtained by calculating the average value of the temperature score, the humidity score and the precipitation score. The excellent evaluation value refers to the evaluation value calculated based on the criterion weight, the population density score, the grassland environment score, the nutrient component score and the climate environment score. The excellent evaluation value is a numerical value used to evaluate the yak reproduction quantity of the excellent yak population. The larger the excellent evaluation value, the larger the yak reproduction quantity of the excellent yak population. For example, the population density score is 1, the grassland environment score is 2, the nutrient component score is 3, the climate environment score is 4, the grassland weight is 0.2, the population weight is 0.4, the nutrient weight is 0.1, and the climate weight is 0.3. The excellent evaluation value = 1×0.4 + 2×0.2 + 3×0.1 + 4×0.3 = 2.3. The yak reproduction quantity refers to the number of cubs that are reproduced and successfully survive in the yak population within a specific time period. Optionally, the specific time period is 90 days. The elimination evaluation value refers to the numerical value used to evaluate the yak reproduction quantity of the eliminated yak population. The calculation method of the elimination evaluation value is the same as that of the excellent evaluation value and will not be elaborated here.

[0142] S7. Based on the excellent evaluation value, the elimination evaluation value, the excellent population density, the excellent nutrient data, the elimination population density, the elimination nutrient data received by the data processing unit, and the data processing unit, construct a reproduction surface, obtain optimized reproduction parameters according to the reproduction surface, and formulate an optimized reproduction plan based on the optimized reproduction parameters.

[0143] The interpretable reproduction surface refers to a multi-dimensional surface constructed based on excellent evaluation values, elimination evaluation values, excellent population density, excellent nutritional data, elimination population density, elimination nutritional data, and a data processing unit. The reproduction surface is obtained by fitting a spline surface. Specifically, the MATLAB functions spline and fit are used for surface fitting. This is prior art and will not be elaborated here. Obtaining optimized reproduction parameters based on the reproduction surface means obtaining optimized reproduction parameters through an optimization algorithm and the reproduction surface. This is prior art and will not be elaborated here. Optionally, the optimization algorithm is a particle swarm optimization algorithm. Optimized reproduction parameters refer to the optimal input parameters obtained through an optimization algorithm and the reproduction surface. The optimal input parameters include: optimal population density and optimal nutritional data. The optimal population density refers to the population density that maximizes the yak reproduction amount obtained through an optimization algorithm and the reproduction surface. The optimal nutritional data refers to the data of the nutritional components that maximizes the yak reproduction amount obtained through an optimization algorithm and the reproduction surface. Formulating an optimized reproduction plan based on the optimized reproduction parameters means adjusting the forage, feed, and population density of the yak population based on the optimal nutritional data and the optimal population density, such that the nutritional components in the adjusted forage and feed are the same as the optimal nutritional data, and the adjusted population density is the same as the optimal population density.

[0144] The present invention solves the problems described in the background technology. First, based on the obtained reference yak population data set, the excellent yak population data set and the eliminated yak population data set are identified. The division of the excellent yak population data set and the eliminated yak population data set provides accurate and reliable data for the subsequent formulation of an optimized breeding plan, thereby improving the reliability of the optimized breeding plan; secondly, the corresponding excellent yak population is matched according to the excellent yak population data set, the excellent population density of the excellent yak population is calculated, and the hierarchical factors are obtained. By calculating the excellent population density of the excellent yak population, it is possible to more accurately understand how to reasonably place yaks to ensure better resource utilization and yak reproduction. Reasonable population density helps to reduce resource competition and improve yak reproduction and yak growth rate. The hierarchical factors are composed of four factors: grassland environment, population density, nutritional components and climate environment. By comprehensively considering the four factors of grassland environment, population density, nutritional components and climate environment, the yak reproduction can be improved more comprehensively. Afterwards, nutritional sampling is performed on the excellent yak population to obtain excellent nutritional data, nutritional sampling is performed on the eliminated yak population to obtain eliminated nutritional data, and nutritional sampling is performed on the excellent yak population and the eliminated yak population respectively, so that the nutritional status of the two types of yak populations can be accurately assessed. By comparing the excellent nutritional data with the eliminated nutritional data, forage and feed can be adjusted in a targeted manner to increase the yak reproduction amount. Further, an excellent evaluation value is calculated based on the criterion weight and the excellent yak population, and an elimination evaluation value is calculated based on the criterion weight and the eliminated yak population. Calculating the excellent evaluation value and the elimination evaluation value can help us better understand the overall situation of the yak population, and the excellent evaluation value and the elimination evaluation value can directly reflect the potential of population reproduction. Finally, a reproduction surface is constructed, and optimized reproduction parameters are obtained according to the reproduction surface. An optimized reproduction plan is formulated based on the optimized reproduction parameters. By optimizing the reproduction parameters, the most reasonable plan for increasing the yak reproduction amount can be formulated according to the actual population density and nutritional data. Therefore, the present invention can increase the yak reproduction amount.

[0145] like Figure 2 FIG. 1 is a functional module diagram of a yak reproduction optimization system based on the analytic hierarchy process provided by an embodiment of the present invention.

[0146] The yak reproduction optimization system 100 based on the AHP method described in the present invention can be installed in an electronic device. Depending on the functions implemented, the yak reproduction optimization system 100 based on the AHP method can include a population density calculation module 101, an analysis criterion construction module 102, an evaluation value calculation module 103, and a reproduction plan formulation module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0147] The population density calculation module 101 is used to obtain a reference yak population dataset, and confirm an excellent yak population dataset and an eliminated yak population dataset according to the reference yak population dataset;

[0148] Match the corresponding excellent yak population based on the excellent yak population dataset, calculate the excellent population density according to the excellent yak population, and obtain hierarchical factors, where the hierarchical factors are composed of grassland environment, population density, nutritional components, and climate environment;

[0149] The analysis criterion construction module 102 is used to perform nutritional sampling on the excellent yak population to obtain excellent nutritional data, match the corresponding eliminated yak population according to the eliminated yak population dataset, calculate the eliminated population density based on the eliminated yak population, and perform nutritional sampling on the eliminated yak population to obtain eliminated nutritional data;

[0150] Transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to a pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment;

[0151] The evaluation value calculation module 103 is used to construct an analysis matrix using the analysis criterion layer and calculate the criterion weight according to the analysis matrix;

[0152] Calculate an excellent evaluation value according to the criterion weight and the excellent yak population, and calculate an eliminated evaluation value according to the criterion weight and the eliminated yak population;

[0153] The breeding plan formulation module 104 is used to construct a breeding surface based on the excellent evaluation value, the eliminated evaluation value, the excellent population density, the excellent nutritional data, the eliminated population density, the eliminated nutritional data received by the data processing unit, and the data processing unit, obtain optimized breeding parameters according to the breeding surface, and formulate an optimized breeding plan based on the optimized breeding parameters.

[0154] Specifically, each module in the yak breeding quantity optimization system 100 based on the analytic hierarchy process in the embodiments of the present invention adopts the same technical means as the Figure 1 yak breeding quantity optimization method based on the analytic hierarchy process described above, and can produce the same technical effects, which will not be elaborated here.

[0155] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the yak breeding quantity optimization method based on the analytic hierarchy process provided by an embodiment of the present invention.

[0156] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a yak breeding quantity optimization method program based on the analytic hierarchy process.

[0157] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, magnetic disks, optical disks, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In some other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and also includes external storage devices. The memory 11 can not only be used to store application software installed on the electronic device 1 and various types of data, such as the code of the yak breeding quantity optimization method program based on the analytic hierarchy process, etc., but can also be used to temporarily store data that has been output or will be output.

[0158] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the yak breeding quantity optimization method program based on the analytic hierarchy process, etc.), and calling data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0159] The bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is set to achieve connection and communication between the memory 11 and at least one processor 10, etc.

[0160] Figure 3 Only an electronic device with components is shown. It can be understood by those skilled in the art that Figure 3 the shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than those shown, or combine certain components, or have different component arrangements.

[0161] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0162] Furthermore, the electronic device 1 may further 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.

[0163] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be 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 liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0164] The program of the yak breeding quantity optimization method based on the analytic hierarchy process stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can implement:

[0165] Obtain a reference yak population data set, and confirm an excellent yak population data set and an eliminated yak population data set according to the reference yak population data set;

[0166] Match the corresponding excellent yak population based on the excellent yak population dataset, calculate the excellent population density according to the excellent yak population, and obtain hierarchical factors, where the hierarchical factors consist of grassland environment, population density, nutritional components, and climate environment;

[0167] Conduct nutritional sampling on the excellent yak population to obtain excellent nutritional data, match the corresponding eliminated yak population according to the eliminated yak population dataset, calculate the eliminated population density based on the eliminated yak population, and conduct nutritional sampling on the eliminated yak population to obtain eliminated nutritional data;

[0168] Transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to a pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment;

[0169] Use the analysis criterion layer to construct an analysis matrix and calculate the criterion weights according to the analysis matrix;

[0170] Calculate the excellent evaluation value according to the criterion weights and the excellent yak population, and calculate the eliminated evaluation value according to the criterion weights and the eliminated yak population;

[0171] Based on the excellent evaluation value, eliminated evaluation value, excellent population density, excellent nutritional data, eliminated population density, eliminated nutritional data received by the data processing unit, and the data processing unit, construct a reproduction surface, obtain optimized reproduction parameters according to the reproduction surface, and formulate an optimized reproduction plan based on the optimized reproduction parameters.

[0172] Specifically, the specific implementation method of the above instructions by the processor 10 can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be elaborated here.

[0173] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they 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, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0174] The present invention also provides a computer-readable storage medium, and the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0175] Obtain a reference yak population dataset, and confirm an excellent yak population dataset and an eliminated yak population dataset according to the reference yak population dataset;

[0176] Match the corresponding excellent yak population based on the excellent yak population dataset, calculate the excellent population density according to the excellent yak population, and obtain hierarchical factors, where the hierarchical factors are composed of grassland environment, population density, nutritional components, and climate environment;

[0177] Conduct nutritional sampling on the excellent yak population to obtain excellent nutritional data, match the corresponding eliminated yak population according to the eliminated yak population dataset, calculate the eliminated population density based on the eliminated yak population, and conduct nutritional sampling on the eliminated yak population to obtain eliminated nutritional data;

[0178] Transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to a pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment;

[0179] Use the analysis criterion layer to construct an analysis matrix, and calculate the criterion weights according to the analysis matrix;

[0180] Calculate the excellent evaluation value according to the criterion weights and the excellent yak population, and calculate the eliminated evaluation value according to the criterion weights and the eliminated yak population;

[0181] Based on the excellent evaluation value, eliminated evaluation value, excellent population density, excellent nutritional data, eliminated population density, eliminated nutritional data received by the data processing unit, and the data processing unit, construct a reproduction surface, obtain optimized reproduction parameters according to the reproduction surface, and formulate an optimized reproduction plan based on the optimized reproduction parameters.

[0182] In 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 there can be other division methods in actual implementation.

[0183] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, the functional modules in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0185] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An optimization method for yak reproduction quantity based on the analytic hierarchy process, characterized in that The method includes: Obtaining a reference yak population dataset, and identifying an excellent yak population dataset and an eliminated yak population dataset according to the reference yak population dataset; Matching a corresponding excellent yak population based on the excellent yak population dataset, calculating the excellent population density according to the excellent yak population, and obtaining hierarchical factors, where the hierarchical factors are composed of grassland environment, population density, nutritional components, and climate environment; Conducting nutritional sampling on the excellent yak population to obtain excellent nutritional data, matching a corresponding eliminated yak population according to the eliminated yak population dataset, calculating the eliminated population density based on the eliminated yak population, and conducting nutritional sampling on the eliminated yak population to obtain eliminated nutritional data; Transmitting the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to a pre-constructed data processing unit, and establishing an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment; Constructing an analysis matrix using the analysis criterion layer, and calculating the criterion weights according to the analysis matrix; Calculating an excellent evaluation value according to the criterion weights and the excellent yak population, and calculating an eliminated evaluation value according to the criterion weights and the eliminated yak population; Based on the excellent evaluation value, eliminated evaluation value, excellent population density, excellent nutritional data, eliminated population density, eliminated nutritional data received by the data processing unit, and the data processing unit, constructing a reproduction surface, obtaining optimized reproduction parameters according to the reproduction surface, and formulating an optimized reproduction plan based on the optimized reproduction parameters.

2. The yak breeding quantity optimization method based on the analytic hierarchy process according to claim 1, characterized in that, The identifying of the excellent yak population dataset and the eliminated yak population dataset according to the reference yak population dataset includes: Obtaining judgment parameters, where the judgment parameters include: population reproduction rate, population survival rate, yak weight value, and yak height value; Constructing a yak evaluation function based on the population reproduction rate, population survival rate, yak weight value, and yak height value; Performing an evaluation calculation operation on all the reference yak population data in the reference yak population dataset using the yak evaluation function to obtain a yak population evaluation value set; Sequentially extracting yak population evaluation values from the yak population evaluation value set, and performing the following operations on the extracted yak population evaluation values: Removing the extracted yak population evaluation value from the yak population evaluation value set to obtain a yak population combination set; Comparing the yak population evaluation value with a preset population evaluation threshold; When the yak population evaluation value is greater than the population evaluation threshold, confirming the yak population evaluation value as excellent population data, updating the yak population evaluation value set using the yak population combination set, and returning to the step of sequentially extracting yak population evaluation values from the yak population evaluation value set using the updated yak population evaluation value set until the yak population evaluation value set is an empty set; When the yak population evaluation value is not greater than the population evaluation threshold, confirming the yak population evaluation value as eliminated population data, updating the yak population evaluation value set using the yak population combination set, and returning to the step of sequentially extracting yak population evaluation values from the yak population evaluation value set using the updated yak population evaluation value set until the yak population evaluation value set is an empty set; Collect excellent population data to obtain an excellent yak population dataset, and collect eliminated population data to obtain an eliminated yak population dataset.

3. The yak breeding quantity optimization method based on the analytic hierarchy process according to claim 2, characterized in that, Constructing a yak evaluation function based on the population reproduction rate, population survival rate, yak weight value, and yak height value includes: Construct a yak evaluation function based on a preset saturation adjustment parameter, a preset evaluation adjustment parameter, the population reproduction rate, the population survival rate, the yak weight value, and the yak height value. Among them, the yak evaluation function is as follows: Among them, Q refers to the yak evaluation function, w1 refers to the population reproduction rate, w2 refers to the population survival rate, e refers to the natural constant, β refers to the saturation adjustment parameter, α refers to the evaluation adjustment parameter, r1 refers to the yak weight value, t1 refers to the preset weight influence parameter, r2 refers to the yak height value, t2 refers to the preset height influence parameter, and γ refers to the preset combined adjustment parameter.

4. The optimization method for yak reproduction quantity based on the analytic hierarchy process according to claim 3, wherein Performing nutritional sampling on the excellent yak population to obtain excellent nutritional data, including: Obtain the forage feeding area, randomly sample within the forage feeding area based on the preset number of samples to obtain sampled forage, and perform a collection operation on the sampled forage to obtain a sampled forage set; Obtain excellent yak feed, and perform nutritional analysis on the excellent yak feed and the sampled forage set respectively based on the preset number of detections to obtain a feed nutritional component set and a forage nutritional component set; Calculate the mean value based on the feed nutritional component set and the forage nutritional component set to obtain excellent nutritional data.

5. The yak breeding quantity optimization method based on the analytic hierarchy process according to claim 4, characterized in that Before transmitting the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to the pre-constructed data processing unit, it also includes: Calculate the processing busy degree of the data processing unit; Compare the processing busy degree with the preset busy degree threshold; If the processing busy degree is greater than the busy degree threshold, store the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data in the pre-constructed data temporary storage unit, and return to the above step of calculating the processing busy degree of the data processing unit until the processing busy degree is not greater than the busy degree threshold, then transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data in the data temporary storage unit to the data processing unit; If the processing busy degree is not greater than the busy degree threshold, transmit the excellent population density, excellent nutritional data, eliminated population density, and eliminated nutritional data to the data processing unit.

6. The optimization method for yak reproduction quantity based on the analytic hierarchy process according to claim 5, wherein Calculating the processing busy degree of the data processing unit includes: Obtain the received data volume, data processing density, data calculation volume, system available resources, transmission delay, and storage access performance of the data processing unit, and calculate the processing busy degree based on the received data volume, data processing density, data calculation volume, system available resources, transmission delay, and storage access performance: Among them, W refers to the processing busy degree, R refers to the received data volume, T refers to the data processing density, Y refers to the data calculation volume, E refers to the system available resources, δ refers to the preset transmission parameter, I refers to the transmission delay, and U refers to the storage access performance.

7. The optimization method for yak reproduction quantity based on the analytic hierarchy process according to claim 6, characterized in that Using the analysis criterion layer to construct an analysis matrix includes: Obtain the relative weights according to the analysis criterion layer, aggregate the relative weights to obtain a relative weight set, where the relative weight set includes: grassland population weight, population grassland weight, grassland nutrition weight, nutrition grassland weight, grassland climate weight, climate grassland weight, population nutrition weight, nutrition population weight, population climate weight, climate population weight, nutrition climate weight, and climate nutrition weight; Construct an analysis matrix based on the grassland population weight, population grassland weight, grassland nutrition weight, nutrition grassland weight, grassland climate weight, climate grassland weight, population nutrition weight, nutrition population weight, population climate weight, climate population weight, nutrition climate weight, and climate nutrition weight; Among them, G refers to the analysis matrix, ρ1 refers to the grassland proportion of the population, ρ2 refers to the climate proportion of the population, ρ3 refers to the nutrition proportion of the population, ρ4 refers to the grassland population proportion, ρ5 refers to the grassland climate proportion, ρ6 refers to the grassland nutrition proportion, ρ7 refers to the climate population proportion, ρ8 refers to the climate grassland proportion, ρ9 refers to the climate nutrition proportion, ρ 10 refers to the nutrition population proportion, ρ 11 refers to the nutrition grassland proportion, ρ 12 refers to the nutrition climate proportion.

8. The optimization method for yak reproduction quantity based on the analytic hierarchy process according to claim 7, characterized in that, The calculation of the criterion weights according to the analysis matrix includes: Calculate the analysis vector based on the analysis matrix, calculate the maximum eigenvalue of the analysis matrix, and calculate the analysis weight according to the analysis vector and the maximum eigenvalue; Obtain the matrix order of the analysis matrix, and calculate the consistency ratio based on the maximum eigenvalue and the matrix order: Where, F refers to the consistency ratio, l refers to the maximum eigenvalue, z refers to the matrix order, and c refers to the preset consistency index value; Compare the consistency ratio with the preset consistency threshold; If the consistency ratio is greater than or equal to the consistency threshold, construct an updated matrix, update the analysis matrix using the updated matrix, and return to the step of calculating the analysis vector based on the analysis matrix using the updated analysis matrix; If the consistency ratio is less than the consistency threshold, confirm the analysis weight as the criterion weight.

9. The yak breeding quantity optimization method based on the analytic hierarchy process according to claim 8, characterized in that, The calculation of the excellent evaluation value according to the criterion weight and the excellent yak population includes: Construct a yak population scoring scale, and calculate the population density score, soil organic matter content score, soil acidity and alkalinity score, protein content score, fat content score, temperature score, humidity score, and precipitation score of the excellent yak population based on the yak population scoring scale; Perform mean calculation according to the soil organic matter content score and the soil acidity and alkalinity score to obtain the grassland environment score, perform mean calculation according to the protein content score and the fat content score to obtain the nutrient component score, and perform mean calculation according to the temperature score, humidity score, and precipitation score to obtain the climate environment score; Calculate the excellent evaluation value based on the criterion weight, population density score, grassland environment score, nutrient component score, and climate environment score.

10. A yak breeding quantity optimization system based on the analytic hierarchy process, characterized in that, The system includes: A population density calculation module for obtaining a reference yak population data set, and identifying an excellent yak population data set and a culled yak population data set according to the reference yak population data set; Match the corresponding excellent yak population based on the excellent yak population data set, calculate the excellent population density according to the excellent yak population, and obtain the hierarchical factors, where the hierarchical factors are composed of grassland environment, population density, nutrient components, and climate environment; An analysis criterion construction module for performing nutrient sampling on the excellent yak population to obtain excellent nutrient data, matching the corresponding culled yak population according to the culled yak population data set, calculating the culled population density based on the culled yak population, and performing nutrient sampling on the culled yak population to obtain culled nutrient data; Transmit the excellent population density, excellent nutrition data, eliminated population density, and eliminated nutrition data to a pre-constructed data processing unit, and establish an analysis criterion layer based on the grassland environment, population density, nutritional components, and climate environment; An evaluation value calculation module, configured to construct an analysis matrix by using the analysis criterion layer and calculate criterion weights according to the analysis matrix; Calculate an excellent evaluation value according to the criterion weights and the excellent yak population, and calculate an eliminated evaluation value according to the criterion weights and the eliminated yak population; A breeding plan formulation module, configured to construct a breeding surface based on the excellent evaluation value, the eliminated evaluation value, the excellent population density, the excellent nutrition data, the eliminated population density, the eliminated nutrition data received by the data processing unit, and the data processing unit, obtain optimized breeding parameters according to the breeding surface, and formulate an optimized breeding plan based on the optimized breeding parameters.

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