Risk assessment method of laying hen breeding system based on risk control engine

Abstract

The invention provides a risk assessment method of a laying hen breeding system based on a risk control engine, and relates to the technical field of laying hen breeding risk assessment. The method comprises the following steps: collecting historical breeding data of laying hens, and carrying out risk factor analysis of output quality based on breeding cost to form a breeding output risk control model; market breeding price data of laying hen breeding is collected, price trend analysis of breeding output is carried out, and laying hen output price trend change information is formed; acquiring breeding output requirement information, acquiring real-time breeding data, performing risk identification of breeding output, and forming breeding output risk identification data; and according to the breeding output risk identification data, carrying out breeding risk disposal to form breeding risk disposal result data. According to the method, a reasonable risk simulation evaluation mode is established based on risk factors influencing laying hen breeding, so that reasonable prediction of laying hen breeding return is realized, and the loss risk of laying hen breeding is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment for laying hen farming, and in particular to a risk assessment method for a laying hen farming system based on a risk control engine. Background Art

[0002] Egg-laying chicken farming is an industry that requires investment before returns. Therefore, there are subjective and objective factors in the initial investment process, which causes certain risks in egg-laying chicken farming. In order to ensure that egg-laying chicken farming can achieve a reasonable return, a reasonable investment return assessment should be conducted during the initial investment to avoid losses caused by blind investment.

[0003] The risk control engine mainly collects, identifies and processes risk factors of risk systems with risk factors, which has realized the risk control of risk systems and effectively reduced the damage caused by risks to the system. Reasonable risk control of laying hen farming and the establishment of a risk engine can effectively guarantee the investment return benefits of laying hen farming. At present, the risk assessment of laying hen farming based on the risk engine is still immature, and how to reasonably assess and handle the risks of laying hen farming is worth considering.

[0004] Therefore, designing a risk assessment method for laying hen farming systems based on a risk control engine, and establishing a reasonable risk simulation assessment method based on the risk factors affecting laying hen farming to achieve a reasonable prediction of the returns of laying hen farming, effectively reducing the risk of losses in laying hen farming, is a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a risk assessment method for a laying hen breeding system based on a risk control engine, by analyzing the risk factors that affect the output quality and output yield of the laying hen historical breeding data, and establishing a risk impact model of the risk factors relative to the output quality and output yield. At the same time, based on the breeding output requirements, the risk impact model is used to identify the risks that affect the output cost and the sales price, ensuring that the possible risk size of the return profit can be reasonably identified before the breeding investment is made, and then timely taking reasonable risk treatment for the breeding to be invested, effectively avoiding the loss of breeding.

[0006] In a first aspect, the present invention provides a risk assessment method for an egg-laying chicken breeding system based on a risk control engine, comprising collecting historical breeding data of egg-laying chickens, and conducting a risk factor analysis of output quality based on breeding costs to form a breeding output risk control model; collecting market breeding price data of egg-laying chickens, and conducting a price trend analysis of breeding outputs in combination with the breeding output risk control model to form information on price trend changes in egg-laying chicken outputs; obtaining breeding output requirement information, and collecting real-time breeding data, and identifying breeding output risks in combination with the breeding output risk control model and the price trend change information on egg-laying chicken outputs to form breeding output risk identification data; and conducting breeding risk disposal based on the breeding output risk identification data to form breeding risk disposal result data.

[0007] In the present invention, the method analyzes the risk factors that affect the output quality and output yield of laying hens historical breeding data, and establishes a risk impact model of risk factors relative to the output quality and output yield. At the same time, based on the breeding output requirements, the risk impact model is used to identify the risks that affect the output cost and sales price, ensuring that the possible risk size of the return profit can be reasonably identified before the breeding investment is made, and then timely adopt reasonable risk treatment for the breeding to be invested, effectively avoiding the loss of breeding.

[0008] As a possible implementation method, historical breeding data of laying hens are collected, and risk factor analysis of output quality based on breeding costs is conducted to form a breeding output risk control model, including: taking the output output of a single item as the breeding analysis cycle, extracting breeding quality control factors that affect the quality grade of eggs in the historical breeding data of laying hens, and forming output quality control factor data; taking the output output of a single item as the breeding analysis cycle, extracting breeding production control factors that affect the number of eggs in the historical breeding data of laying hens, and forming output production control factor data; conducting risk control analysis on the output quality control factor data and the output production control factor data based on the impact of the egg grade quantity, and forming a breeding output risk control model.

[0009] In the present invention, a breeding output risk control model is established based on the historical breeding data of laying hens. The fundamental consideration of risk control is to determine whether the return of breeding is stable and acceptable. Therefore, the establishment of the control model considers two aspects that affect the return of breeding. On the one hand, there are risk control factors that affect the quality of egg output, and on the other hand, there are risk control factors that affect the output of eggs. After all, the quality of eggs determines the unit price of eggs, and the output of eggs determines the sales volume of eggs. The unit price of eggs and the sales volume of eggs jointly affect the return of breeding output, that is, the profit of breeding output. Combined with the risk factor information affecting the quality of egg output and the risk factor information affecting the output of eggs, a risk control model that affects the return of egg breeding output is established, which provides an important analytical basis for subsequent risk identification and risk treatment.

[0010] As a possible implementation method, a risk control analysis based on the impact of egg grade quantity is performed on the output quality control factor data and the output yield control factor data to form a breeding output risk control model, including: according to the output quality control factor data, all output quality risk factors that affect the egg output quality are determined, and the impact quantity analysis of different output quality risk factors on egg grade is performed to form output quality risk factor impact relationship information; according to the output yield control factor data, a multi-factor change analysis of the output yield risk factors that affect the change in egg quantity is performed to form output yield risk factor impact relationship information; the output quality risk factor impact relationship information and the output yield risk factor impact relationship information are combined to form a breeding output risk control model.

[0011] In the present invention, the establishment of the aquaculture output risk control model is achieved by respectively analyzing the relationship between the risk factors affecting the output quality and the relationship between the risk factors affecting the output yield. Only by accurately determining the risk control factors affecting the output quality and the relationship between these risk control factors and the output quality, as well as the risk control factors affecting the output yield and the relationship between these risk control factors and the output yield, can a reasonable aquaculture output risk control model be established.

[0012] As a possible implementation method, all output quality risk factors that affect egg output quality are determined based on output quality control factor data, and the impact of different output quality risk factors on egg grade is analyzed to form output quality risk factor impact relationship information, including: taking the breeding analysis cycle as the basis for data extraction, establishing the high-grade quality impact relationship between the percentage of high-grade eggs in each breeding analysis cycle and the high-grade quality impact relationship between the parameter quantities of different output quality risk factors ,in: , n represents the number of different output quality risk factors affecting high-grade egg production, Indicates high-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered n in , Indicates high-level quality impact relationship The constant term in , Indicates the percentage of high-grade eggs; based on the breeding analysis cycle as the data extraction basis, establish the relationship between the percentage of medium-grade eggs in each breeding analysis cycle and the medium-grade quality impact of different output quality risk factor parameters ,in: , m represents the number of different output quality risk factors affecting the output of medium-grade eggs, Indicates medium-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered m in the equation is: Indicates medium-level quality impact relationship The constant term in the analysis cycle is used as the basis for data extraction, and the relationship between the percentage of low-grade eggs and the low-grade quality impact of different output quality risk factor parameters in each analysis cycle is established. ,in: , k represents the number of different output quality risk factors affecting low-grade egg production, Indicates low-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered k in is: Indicates low-level quality impact relationship The constant term in ; where for low-grade quality the relationship , Medium-level quality influence relationship And high-level quality affects the relationship , in the same breeding analysis cycle .

[0013] In the present invention, the analysis of the risk control factors affecting the output quality to obtain the influence relationship information of these risk control factors on the output quality is achieved by targeting the probability of egg production that affects different grades of quality. It can be understood that for eggs of different grades, from low grade to high grade, the proportion of low-grade eggs in each production cycle is achieved by controlling the basic breeding parameters, while for medium-grade and high-grade eggs, other independent risk control factors are added on the basis of the risk control factors corresponding to the low-grade eggs to achieve the expansion of the single output ratio. Therefore, for the risk control factors corresponding to low-grade quality, the risk control factors corresponding to medium-grade quality, and the risk control factors corresponding to high-grade quality, these risk control factors are relatively independent and only affect the corresponding egg grades. Of course, for the risk control factors that affect the quality of eggs of the same grade, since the quality of eggs is the basis of multiple effects, when determining the grade influence relationship, a reasonable influence relationship is established by using big data to conduct multi-factor influence analysis. In addition, it should be noted that the risk control factors that affect the quality of low-grade eggs are also necessary factors for the subsequent production of medium-grade and high-grade quality eggs, but the control and adjustment of these necessary factors cannot cause an increase in the output ratio of medium-grade quality eggs or high-grade quality eggs. That is, the adjustment of these necessary factors is only the basis for determining whether or not eggs can be laid or low-grade eggs can be produced. On the basis of laying eggs or producing low-grade eggs, a more reasonable influence relationship is formed by analyzing and determining the risk control factors that affect the output of medium-grade eggs or high-grade eggs.

[0014] As a possible implementation method, based on the output production control factor data, a multi-factor change analysis of the output production risk factors that affect the change in the number of eggs is conducted to form the output production risk factor impact relationship information, including: taking the breeding analysis cycle as the basis for data extraction, establishing the output impact relationship between the number of eggs and different output production risk factor parameters in each breeding analysis cycle ,in: , i represents the number of different output risk factors, Indicates the relationship between production and output The impact coefficient corresponding to the output risk factor numbered i in the equation is: Indicates the relationship between production and output The constant term in the equation is Q, which represents the total number of eggs. , determine the yield impact relationship All influencing coefficients and constant terms in .

[0015] In the present invention, the determination of the relationship between the risk control factors affecting the output and the output is based on a multi-factor analysis of the risk control factors affecting the output based on historical big data. It should be noted that, for both the relationship between the output quality and the corresponding risk control factors and the relationship between the output and the corresponding risk control factors, since the amount of big data is huge, when determining the corresponding influence coefficients and constant terms, there must be redundant data, so the corresponding influence coefficients and constant terms can be determined by grouping to establish a relationship and then averaged and fitted, or the data can be reasonably screened to establish a relationship to determine the corresponding influence coefficient and constant term.

[0016] As a possible implementation method, the output quality risk factor impact relationship information and the output yield risk factor impact relationship information are combined to form a breeding output risk control model, including: The relationship between production and , determine the risk control model for low-level aquaculture output : ,in, Indicates the total output of low-level breeding; according to the relationship between the middle-level quality The relationship between production and , determine the mid-level breeding output risk control model : ,in, Indicates the total output of medium-level breeding; affects the relationship according to high-level quality The relationship between production and , determine the high-level breeding output risk control model : ,in, Indicates the total output of high-level breeding.

[0017] In the present invention, the breeding output risk control model comprehensively considers the influence relationship affecting the output quality and the influence relationship affecting the output yield. At the same time, the influence relationship of the output yield is established based on the information of different grades of eggs. Therefore, when establishing the breeding risk control model, it is also established based on different grades to ensure the rationality of the model establishment, and also provides more reasonable basic data for the subsequent risk analysis of the demand for eggs of different grades.

[0018] As a possible implementation method, the market breeding price data of laying hen breeding is collected, and the price trend of breeding output is analyzed in combination with the breeding output risk control model to form the information on the change trend of laying hen output price, including: according to the market breeding price data, the cost volume of different output quality risk factors of different levels is determined in the time dimension, and periodic fitting is performed to form the periodic change trend of the unit cost of output quality risk factors. , where e is n, m, k; according to the market breeding price data, determine the change trend of the cost of different output risk factors in the time dimension, and perform periodic fitting to form the periodic change trend of the unit cost of output risk factors ; According to the market breeding price data, determine the price change trend of eggs of different grades in the time dimension, and perform periodic fitting to form the unit price periodic change trend of output grade , where g can be low, middle, or high.

[0019] In the present invention, the risk control model is only to establish the relationship between the risk control factors affecting the output quality and output yield, and for laying hen farming, the risk will eventually be reflected in the return on investment, so to more intuitively carry out the risk assessment of the return on investment, it is necessary to carry out an economic analysis of the factors affecting the return on investment. Here, for all risk control factors in the breeding process, including all risk control factors affecting the output quality and output yield, the change trend based on the time dimension is determined. Of course, it is also necessary to analyze the change trend of the price of eggs of different grades in the time dimension, which determines the overall sales volume and is an important parameter affecting the return profit. It should be noted that whether it is the cost of the risk control factors affecting the output quality and output yield and the price of eggs of different grades, they are all part of the market behavior of breeding, so their change trend in the time dimension is also marketable. Based on the supply and demand characteristics of the market, the changes in these cost prices are regular, and the fitting of the change trend of the same factors under different cycles based on the breeding cycle can reasonably and accurately form a regular prediction of the change of the factor cost of periodic breeding.

[0020] As a possible implementation method, the information on breeding output requirements is obtained, and the risk identification of breeding output is carried out in combination with the breeding output risk control model and the information on the price trend of laying hens output to form breeding output risk identification data, including: determining the demand quantity of high-grade eggs according to the breeding output requirement information And the corresponding high-level output demand ratio , the number of medium-grade eggs required And the corresponding ratio of medium-level output demand , the number of low-grade eggs required And the corresponding low-level output demand ratio And the demand deadline; the demand ratio of high-level output Ratio of output demand to medium level For reference, the corresponding output quality risk factor parameter quantity control adjustment is carried out, and the following analysis and judgment are carried out: If the parameter quantity control adjustment of different output quality risk factors can achieve the proportion of high-level output demand Ratio of output demand to medium level , then according to the demand deadline and different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of output quality of the combination with the lowest cost among the combinations of values ​​of different output quality risk factors , and according to the demand for high-grade eggs , the number of medium-grade eggs required And the number of low-grade eggs required And the relationship between production and , combined with different output risk factors, unit cost cycle change trend and price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate total cost based on output quality , output quantity estimated total cost and output estimated total sales , determine the estimated profit of output ,in: ; If the parameter quantity control adjustment of different output quality risk factors cannot achieve the required proportion of high-level output Ratio of output demand to medium level , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit ; Set profit acceptance rate threshold , based on the estimated profit of the output obtained , output quality estimated total cost and output quantity estimated total cost , conduct the following risk identification analysis: , then the profit control risk identification information is formed; when , then the uncontrollable profit risk identification information is formed; when , then loss risk identification information is formed.

[0021] In the present invention, the requirements of laying hen farming are the conditions that need to be met for output. Based on these conditions, combined with the risk control model and the price trend change information of laying hens, risk identification analysis can be performed to accurately determine whether there is a return risk for the return on investment required for farming. It can be understood that when performing risk identification, the best state is to meet the output quality and output quantity requirements by adjusting the risk control factors. In this case, the number of eggs just meets the demand without excess. After all, although the excess eggs have a market price, the impact on the cost of risk control factors is extensive and large. For situations where it is impossible to meet the output quality and output requirements by adjusting the risk control factors, a reasonable estimate is made considering the principle of minimum cost consumption, and risk identification for this situation can be achieved. Of course, after obtaining the corresponding estimated profit, it is not that there is no risk as long as there is profit. It is necessary to consider that there is a certain uncertainty in the assessment risk and the situation that may actually affect the output. Therefore, when risk identification is performed, a certain threshold height boundary is made based on the estimated profit to make a reasonable judgment, and the profit acceptance rate threshold can be set according to the actual situation, or it can be determined based on big data analysis.

[0022] As a possible implementation method, if the parameter quantity control adjustment of different output quality risk factors cannot achieve the high-level output demand ratio Ratio of output demand to medium level , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit , including: the proportion of medium-level output demand As a reference, determine the proportion of mid-level output demand The corresponding maximum allowable high-level output demand ratio , and according to the demand for high-grade eggs , combined with the mid-level breeding output risk control model , High-level aquaculture output risk control model , the proportion of medium-level output demand And the maximum allowable high-level output demand ratio , determine the quantity of eggs that meet the demand for high-grade eggs The required number of medium-grade eggs and the required number of low-grade eggs; according to the ratio of medium-grade output demand and the maximum allowable high-level output demand ratio , combined with the demand deadline and different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of output quality of the combination with the lowest cost among the combinations of values ​​of different output quality risk factors , and according to the demand for high-grade eggs , the necessary number of medium-grade eggs and the necessary number of low-grade eggs and the relationship between production and output , combined with different output risk factors, unit cost cycle change trend and price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate total cost based on output quality , output quantity estimated total cost and output estimated total sales , determine the estimated profit of output ,in: .

[0023] In the present invention, when there is a situation where the output quality and output requirements cannot be met by adjusting the risk control factors, the risk identification estimate is made by considering the adjustment of the output yield and ensuring that the number of high-grade eggs just reaches the required number for estimation and analysis. After all, the cost increase caused by the increase in the number of high-grade eggs is greater than the cost increase caused by the increase in the number of medium-grade eggs. The higher the grade of the eggs, the higher the cost consumed to affect its output. The gap between this cost and the corresponding market price is smaller than that of low-grade eggs, so the profit will be relatively reduced. Therefore, the adjustment is made with reference to just meeting the requirement of the number of high-grade eggs.

[0024] As a possible implementation method, aquaculture risk treatment is carried out according to the aquaculture output risk identification data to form aquaculture risk treatment result data, including: when the risk identification result shows controllable profit risk identification information, then the non-treatment information is output; when the risk identification result shows uncontrollable profit risk identification information, then the profit risk warning information and warning profit rate are output. ; When the risk identification result shows loss risk identification information, the production loss warning information and loss rate are output .

[0025] In the present invention, after completing risk identification, it is necessary to process and warn the identified risks. For controllable profit risk identification information, the profit is controllable and there are stable return conditions, so it is a positive signal for breeding investment. For uncontrollable profit risk identification information, although there are conditions for obtaining profits, the actual possible profit acquisition is greatly affected and needs to be treated with caution. For loss risk identification information, it can be confirmed that the investment that meets the breeding requirements this time will suffer losses.

[0026] The risk assessment method for laying hen farming system based on risk control engine provided by the present invention has the following beneficial effects: This method analyzes the risk factors that affect the output quality and output of laying hens through historical breeding data, and establishes a risk impact model of risk factors relative to output quality and output. At the same time, based on the breeding output requirements, the risk impact model is used to identify the risks that affect the output cost and sales price, ensuring that the possible risk of return profit can be reasonably identified before investing in breeding, and then timely taking reasonable risk treatment for the breeding to be invested, effectively avoiding breeding losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A step diagram of a breeding risk simulation and assessment method for a laying hen breeding system based on a risk control engine provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] Egg-laying chicken farming is an industry that requires investment before returns. Therefore, there are subjective and objective factors in the initial investment process, which causes certain risks in egg-laying chicken farming. In order to ensure that egg-laying chicken farming can achieve a reasonable return, a reasonable investment return assessment should be conducted during the initial investment to avoid losses caused by blind investment.

[0033] The risk control engine mainly collects, identifies and processes risk factors of risk systems with risk factors, which has realized the risk control of risk systems and effectively reduced the damage caused by risks to the system. Reasonable risk control of laying hen farming and the establishment of a risk engine can effectively guarantee the investment return benefits of laying hen farming. At present, the risk assessment of laying hen farming based on the risk engine is still immature, and how to reasonably assess and handle the risks of laying hen farming is worth considering.

[0034] refer to Figure 1The embodiment of the present invention provides a risk assessment method for a laying hen breeding system based on a risk control engine. The method analyzes the risk factors that affect the output quality and output yield of the laying hen's historical breeding data, and establishes a risk impact model of the risk factors relative to the output quality and output yield. At the same time, based on the breeding output requirements, the risk impact model is used to identify the risks that affect the output cost and sales price, ensuring that the possible risk of return profit can be reasonably identified before investing in breeding, and then timely taking reasonable risk treatment for the breeding to be invested, effectively avoiding the loss of breeding.

[0035] The breeding risk simulation assessment method of laying hen breeding system based on risk control engine specifically includes the following steps: S1: Collect historical laying hen breeding data, and conduct risk factor analysis on output quality based on breeding costs to form a breeding output risk control model.

[0036] Historical laying hen breeding data is collected, and risk factor analysis of output quality based on breeding costs is conducted to form a breeding output risk control model, including: taking the output of a single item as the breeding analysis cycle, extracting breeding quality control factors that affect the quality grade of eggs from the historical laying hen breeding data to form output quality control factor data; taking the output of a single item as the breeding analysis cycle, extracting breeding production control factors that affect the number of eggs from the historical laying hen breeding data to form output production control factor data; conducting risk control analysis on the output quality control factor data and the output production control factor data based on the impact of egg grade quantity to form a breeding output risk control model.

[0037] Based on the historical breeding data of laying hens, a breeding output risk control model is established. The fundamental consideration of risk control is to determine whether the return of breeding is stable and acceptable. Therefore, the establishment of the control model takes into account two aspects that affect the return of breeding. On the one hand, there are risk control factors that affect the quality of egg output, and on the other hand, there are risk control factors that affect the output of eggs. After all, the quality of eggs determines the unit price of eggs, and the output of eggs determines the sales volume of eggs. The unit price of eggs and the sales volume of eggs jointly affect the return of breeding output, that is, the profit of breeding output. Combining the risk factor information that affects the quality of egg output and the risk factor information that affects the output of eggs, a risk control model that affects the return of egg breeding output is established, which provides an important analytical basis for subsequent risk identification and risk treatment.

[0038] A risk control analysis based on the impact of egg grade quantity is performed on the output quality control factor data and the output yield control factor data to form a breeding output risk control model, including: determining all output quality risk factors that affect egg output quality based on the output quality control factor data, and analyzing the impact quantity of different output quality risk factors on egg grade to form output quality risk factor impact relationship information; performing a multi-factor change analysis on output yield risk factors that affect changes in egg quantity based on the output yield control factor data to form output yield risk factor impact relationship information; and combining the output quality risk factor impact relationship information and the output yield risk factor impact relationship information to form a breeding output risk control model.

[0039] The establishment of the aquaculture output risk control model is achieved by analyzing the relationship between the risk factors that affect the output quality and the relationship between the risk factors that affect the output yield. Only by accurately determining the risk control factors that affect the output quality and the relationship between these risk control factors and the output quality, as well as the risk control factors that affect the output yield and the relationship between these risk control factors and the output yield, can a reasonable aquaculture output risk control model be established.

[0040] According to the output quality control factor data, all output quality risk factors that affect the egg output quality are determined, and the impact of different output quality risk factors on the egg grade is analyzed to form the output quality risk factor impact relationship information, including: taking the breeding analysis cycle as the basis for data extraction, establishing the high-grade quality impact relationship between the percentage of high-grade eggs in each breeding analysis cycle and the high-grade quality impact relationship between the parameter quantities of different output quality risk factors ,in: , n represents the number of different output quality risk factors affecting high-grade egg production, Indicates high-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered n in , Indicates high-level quality impact relationship The constant term in , Indicates the percentage of high-grade eggs; based on the breeding analysis cycle as the data extraction basis, establish the relationship between the percentage of medium-grade eggs in each breeding analysis cycle and the medium-grade quality impact of different output quality risk factor parameters ,in: , m represents the number of different output quality risk factors affecting the output of medium-grade eggs, Indicates medium-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered m in the equation is: Indicates medium-level quality impact relationship The constant term in the analysis cycle is used as the basis for data extraction, and the relationship between the percentage of low-grade eggs and the low-grade quality impact of different output quality risk factor parameters in each analysis cycle is established. ,in: , k represents the number of different output quality risk factors affecting low-grade egg production, Indicates low-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered k in is: Indicates low-level quality impact relationship The constant term in ; where for low-grade quality the relationship , Medium-level quality influence relationship And high-level quality affects the relationship , in the same breeding analysis cycle .

[0041] The analysis of the risk control factors affecting the output quality obtains the information on the influence relationship of these risk control factors on the output quality by targeting the probability of egg production of different quality grades. It can be understood that for eggs of different grades, from low grade to high grade, the proportion of low-grade eggs in each production cycle is achieved by controlling the basic breeding parameters, while for medium-grade and high-grade eggs, other independent risk control factors are added on the basis of the risk control factors corresponding to low-grade eggs to achieve the expansion of the single output ratio. Therefore, for the risk control factors corresponding to low-grade quality, the risk control factors corresponding to medium-grade quality, and the risk control factors corresponding to high-grade quality, these risk control factors are relatively independent and only affect the corresponding egg grades. Of course, for the risk control factors affecting the quality of eggs of the same grade, since the quality of eggs is the basis of multiple effects, when determining the grade influence relationship, a reasonable influence relationship is established by using big data to conduct multi-factor influence analysis. In addition, it should be noted that the risk control factors that affect the quality of low-grade eggs are also necessary factors for the subsequent production of medium-grade and high-grade quality eggs, but the control and adjustment of these necessary factors cannot cause an increase in the output ratio of medium-grade quality eggs or high-grade quality eggs. That is, the adjustment of these necessary factors is only the basis for determining whether or not eggs can be laid or low-grade eggs can be produced. On the basis of laying eggs or producing low-grade eggs, a more reasonable influence relationship is formed by analyzing and determining the risk control factors that affect the output of medium-grade eggs or high-grade eggs.

[0042] Based on the output control factor data, a multi-factor change analysis of the output risk factors that affect the change in egg quantity is conducted to form the output risk factor impact relationship information, including: taking the breeding analysis cycle as the basis for data extraction, establishing the output impact relationship between the number of eggs and the parameters of different output risk factors in each breeding analysis cycle ,in: , i represents the number of different output risk factors, Indicates the relationship between production and output The impact coefficient corresponding to the output risk factor numbered i in the equation is: Indicates the relationship between production and output The constant term in the equation is Q, which represents the total number of eggs. , determine the yield impact relationship All influencing coefficients and constant terms in .

[0043] The determination of the relationship between the risk control factors affecting the output and the output is based on a multi-factor analysis of the risk control factors affecting the output based on historical big data. It should be noted that, for both the relationship between the output quality and the corresponding risk control factors and the relationship between the output and the corresponding risk control factors, since the amount of big data is huge, when determining the corresponding influence coefficients and constant terms, there must be redundant data. Therefore, the corresponding influence coefficients and constant terms can be determined by grouping and establishing a relationship, and then averaged and fitted. The data can also be reasonably screened to establish a relationship to determine the corresponding influence coefficients and constant terms.

[0044] As a possible implementation method, the output quality risk factor impact relationship information and the output yield risk factor impact relationship information are combined to form a breeding output risk control model, including: The relationship between production and , determine the risk control model for low-level aquaculture output : ,in, Indicates the total output of low-level breeding; according to the relationship between the middle-level quality The relationship between production and , determine the mid-level breeding output risk control model : ,in, Indicates the total output of medium-level breeding; affects the relationship according to high-level quality The relationship between production and , determine the high-level breeding output risk control model : ,in, Indicates the total output of high-level breeding.

[0045] The breeding output risk control model comprehensively considers the influencing relationships that affect output quality and the influencing relationships that affect output yield. At the same time, the influencing relationships of output yield are established based on the information of different grades of eggs. Therefore, when establishing the breeding risk control model, it is also established based on different grades to ensure the rationality of the model establishment. It also provides more reasonable basic data for subsequent risk analysis of the demand for different grades of eggs.

[0046] S2: Collect market price data of laying hen farming, and analyze the price trend of farming output in combination with the farming output risk control model to form information on price trend changes of laying hen output.

[0047] Collect market breeding price data of laying hens, and analyze the price trend of breeding output in combination with the breeding output risk control model to form information on the change of laying hens output price trend, including: according to the market breeding price data, determine the change trend of the cost of different output quality risk factors of different levels in the time dimension, and perform periodic fitting to form the periodic change trend of the unit cost of output quality risk factors. , where e is n, m, k; according to the market breeding price data, determine the change trend of the cost of different output risk factors in the time dimension, and perform periodic fitting to form the periodic change trend of the unit cost of output risk factors ; According to the market breeding price data, determine the price change trend of eggs of different grades in the time dimension, and perform periodic fitting to form the unit price periodic change trend of output grade , where g can be low, middle, or high.

[0048] The risk control model only establishes the relationship between the risk control factors and the output quality and output volume. For egg-laying chicken farming, the risk will eventually be reflected in the return on investment. Therefore, in order to conduct a more intuitive risk assessment of the return on investment, it is necessary to conduct an economic analysis of the factors affecting the return on investment. Here, for all risk control factors in the breeding process, including all risk control factors that affect the output quality and output volume, the change trend based on the time dimension is determined. Of course, it is also necessary to analyze the change trend of the price of eggs of different grades in the time dimension, which determines the overall sales and is an important parameter affecting the return profit. It should be noted that both the cost of risk control factors that affect the output quality and output volume and the price of eggs of different grades are part of the market behavior of breeding, so their change trend in the time dimension is also market-oriented. Based on the supply and demand characteristics of the market, the changes in these cost prices are regular. Fitting the change trend of the same factors in different cycles based on the breeding cycle can reasonably and accurately form a prediction of the change law of the cost of factors in cyclical breeding.

[0049] S3: Obtain breeding output requirement information and collect real-time breeding data. Combined with the breeding output risk control model and the information on the price trend change of laying hen output, identify the risks of breeding output and form breeding output risk identification data.

[0050] Obtain the breeding output requirement information, combine the breeding output risk control model and the information on the price trend of laying hens, identify the risk of breeding output, and form breeding output risk identification data, including: Determine the demand quantity of high-grade eggs according to the breeding output requirement information And the corresponding high-level output demand ratio , the number of medium-grade eggs required And the corresponding ratio of medium-level output demand , the number of low-grade eggs required And the corresponding low-level output demand ratio And the demand deadline; the demand ratio of high-level output Ratio of output demand to medium level For reference, the corresponding output quality risk factor parameter quantity control adjustment is carried out, and the following analysis and judgment are carried out: If the parameter quantity control adjustment of different output quality risk factors can achieve the proportion of high-level output demand Ratio of output demand to medium level , then according to the demand deadline and different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of output quality of the combination with the lowest cost among the combinations of values ​​of different output quality risk factors , and according to the demand for high-grade eggs , the number of medium-grade eggs required And the number of low-grade eggs required And the relationship between production and , combined with different output risk factors, unit cost cycle change trend and price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate total cost based on output quality , output quantity estimated total cost and output estimated total sales , determine the estimated profit of output ,in: ; If the parameter quantity control adjustment of different output quality risk factors cannot achieve the required proportion of high-level output Ratio of output demand to medium level , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit ; Set profit acceptance rate threshold , based on the estimated profit of the output obtained , output quality estimated total cost and output quantity estimated total cost , conduct the following risk identification analysis: , then the profit control risk identification information is formed; when , then the uncontrollable profit risk identification information is formed; when , then loss risk identification information is formed.

[0051] The requirements of laying hen farming are the conditions that need to be met for output. Based on these conditions, combined with the risk control model and the price trend change information of laying hens, risk identification and analysis can accurately determine whether there is a return risk for the investment return required for farming. It is understandable that when conducting risk identification, the best state is to meet the output quality and output quantity requirements by adjusting the risk control factors. In this case, the number of eggs just meets the demand without excess. After all, although the excess eggs have a market price, the impact on the cost of risk control factors is extensive and large. For situations where it is impossible to meet the output quality and output requirements by adjusting the risk control factors, a reasonable estimate is made considering the principle of minimum cost consumption, which can achieve risk identification for this situation. Of course, after obtaining the corresponding estimated profit, it is not that there is no risk as long as there is profit. It is necessary to consider the uncertainty of the assessment risk and the actual possible sudden impact on the output. Therefore, when identifying risks, a reasonable judgment is made based on the estimated profit and a certain threshold height boundary. For the profit acceptance rate threshold, it can be set according to the actual situation or determined based on big data analysis.

[0052] If the parameter quantity control adjustment of different output quality risk factors cannot achieve the required proportion of high-level output Ratio of output demand to medium level , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit , including: the proportion of medium-level output demand As a reference, determine the proportion of mid-level output demand The corresponding maximum allowable high-level output demand ratio , and according to the demand for high-grade eggs , combined with the mid-level breeding output risk control model , High-level aquaculture output risk control model , the proportion of medium-level output demand And the maximum allowable high-level output demand ratio , determine the quantity of eggs that meet the demand for high-grade eggs The required number of medium-grade eggs and the required number of low-grade eggs; according to the ratio of medium-grade output demand and the maximum allowable high-level output demand ratio , combined with the demand deadline and different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of output quality of the combination with the lowest cost among the combinations of values ​​of different output quality risk factors , and according to the demand for high-grade eggs , the necessary number of medium-grade eggs and the necessary number of low-grade eggs and the relationship between production and output , combined with different output risk factors, unit cost cycle change trend and price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate total cost based on output quality , output quantity estimated total cost and output estimated total sales , determine the estimated profit of output ,in: .

[0053] When there is a situation where the output quality and output requirements cannot be met by adjusting the risk control factors, the risk identification estimate is made by considering the adjustment of the output and ensuring that the number of high-grade eggs just reaches the required number. After all, the cost impact caused by the increase in the number of high-grade eggs is greater than the cost increase caused by the increase in the number of medium-grade eggs. The higher the grade of the eggs, the higher the cost of affecting their output. The gap between this cost and the corresponding market price is smaller than that of low-grade eggs, so the profit will be relatively reduced. Therefore, the adjustment is made with reference to just meeting the requirement of the number of high-grade eggs.

[0054] S4: Carry out aquaculture risk disposal based on aquaculture output risk identification data to form aquaculture risk disposal result data.

[0055] According to the aquaculture output risk identification data, aquaculture risk disposal is carried out to form aquaculture risk disposal result data, including: when the risk identification result shows controllable profit risk identification information, then the non-disposal information is output; when the risk identification result shows uncontrollable profit risk identification information, then the profit risk warning information and warning profit rate are output. ; When the risk identification result shows loss risk identification information, the production loss warning information and loss rate are output .

[0056] After completing risk identification, it is necessary to handle and warn the identified risks. For the controllable profit risk identification information, the profit is controllable and there are stable return conditions, so it is a positive signal for breeding investment. For the uncontrollable profit risk identification information, although there are conditions for obtaining profits, the actual possible profit is greatly affected and needs to be treated with caution. As for the loss risk identification information, it can be confirmed that the investment that meets the breeding requirements this time will suffer losses.

[0057] In summary, the beneficial effects of the breeding risk simulation assessment method for laying hen breeding system based on risk control engine provided by the embodiment of the present invention are as follows: This method analyzes the risk factors that affect the output quality and output of laying hens through historical breeding data, and establishes a risk impact model of risk factors relative to output quality and output. At the same time, based on the breeding output requirements, the risk impact model is used to identify the risks that affect the output cost and sales price, ensuring that the possible risk of return profit can be reasonably identified before investing in breeding, and then timely taking reasonable risk treatment for the breeding to be invested, effectively avoiding breeding losses.

[0058] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0059] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0060] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0061] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0062] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0063] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0064] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. These three situations, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0065] It should be understood that the processor in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0066] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0067] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0069] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the functions 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. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A risk assessment method for laying hen breeding system based on risk control engine, characterized in that: include: Collect historical laying hen breeding data, and conduct risk factor analysis on output quality based on breeding costs to form a breeding output risk control model; Collecting market breeding price data of laying hens, and analyzing the price trend of breeding output in combination with the breeding output risk control model, to form information on the price trend change of laying hens output; Acquire breeding output requirement information, collect real-time breeding data, and identify breeding output risks by combining the breeding output risk control model and the laying hen output price trend change information to form breeding output risk identification data; Based on the aquaculture output risk identification data, aquaculture risk disposal is carried out to form aquaculture risk disposal result data.

2. According to claim 1, a risk assessment method for a laying hen breeding system based on a risk control engine is characterized in that: The historical laying hen breeding data is collected, and the risk factor analysis of output quality based on breeding costs is performed to form a breeding output risk control model, including: Taking the output of a single item as the breeding analysis cycle, extracting the breeding quality control factors that affect the quality grade of eggs from the historical breeding data of laying hens, and forming output quality control factor data; Taking the output of a single item as the breeding analysis cycle, extracting the breeding production control factors that affect the number of eggs in the historical breeding data of laying hens, and forming output production control factor data; The output quality control factor data and the output yield control factor data are subjected to risk control analysis based on the impact of egg grade quantity to form the breeding output risk control model.

3. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 2, characterized in that: The risk control analysis based on the impact of egg grade quantity on the output quality control factor data and the output yield control factor data is performed to form the breeding output risk control model, including: According to the output quality control factor data, all output quality risk factors that affect the output quality of eggs are determined, and the impact of different output quality risk factors on the egg grade is analyzed to form output quality risk factor impact relationship information; Based on the output yield control factor data, a multi-factor change analysis of the output yield risk factors that affect the change in the number of eggs is performed to form output yield risk factor influence relationship information; The aquaculture output risk control model is formed by combining the output quality risk factor impact relationship information and the output yield risk factor impact relationship information.

4. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 3 is characterized in that: According to the output quality control factor data, all output quality risk factors that affect the output quality of eggs are determined, and the impact of different output quality risk factors on the egg grade is analyzed to form output quality risk factor impact relationship information, including: Based on the breeding analysis cycle as the data extraction basis, the high-grade quality impact relationship between the percentage of high-grade eggs and the parameters of different output quality risk factors in each breeding analysis cycle is established. ,in: , n represents the number of different output quality risk factors affecting high-grade egg production, Indicates the high-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered n in , Indicates the high-level quality impact relationship The constant term in , Indicates the percentage of high-grade eggs; Based on the breeding analysis cycle as the data extraction basis, the relationship between the percentage of medium-grade eggs and the medium-grade quality impact of different output quality risk factor parameters in each breeding analysis cycle is established. ,in: , m represents the number of different output quality risk factors affecting the output of medium-grade eggs, Indicates the medium-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered m in the equation, Indicates the medium-level quality impact relationship The constant term in ; Based on the breeding analysis cycle as the data extraction basis, the relationship between the percentage of low-grade eggs and the low-grade quality impact of different output quality risk factor parameters in each breeding analysis cycle is established. ,in: , k represents the number of different output quality risk factors affecting low-grade egg production, Indicates the low-level quality impact relationship The impact coefficient corresponding to the output quality risk factor numbered k in , Indicates the low-level quality impact relationship The constant term in ; Among them, for the low-level quality impact relationship , the intermediate quality influence relationship And the high-level quality affects the relationship , in the same breeding analysis cycle .

5. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 4 is characterized in that: The method of performing a multi-factor change analysis on the output and yield risk factors that affect the change in the number of eggs based on the output and yield control factor data to form output and yield risk factor influence relationship information includes: Based on the breeding analysis cycle as the data extraction basis, the relationship between the number of eggs and the production impact of different output production risk factor parameters in each breeding analysis cycle is established. ,in: , i represents the number of different output risk factors, Indicates the yield impact relationship The impact coefficient corresponding to the output risk factor numbered i in the output, Indicates the yield impact relationship The constant term in , Q represents the total number of eggs; According to different yield impact relationships , determine the yield impact relationship All influencing coefficients and constant terms in .

6. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 5, characterized in that: The combining of the output quality risk factor influence relationship information and the output yield risk factor influence relationship information to form the aquaculture output risk control model includes: Affect relationships based on low-level quality The relationship between the yield and the , determine the risk control model for low-level aquaculture output : ,in, It indicates the total output of low-level aquaculture; According to the medium-level quality impact relationship The relationship between the yield and the , determine the mid-level breeding output risk control model : ,in, Indicates the total output of medium-level farming; Influence relationships based on high-level quality The relationship between the yield and the , determine the high-level breeding output risk control model : ,in, Indicates the total output of high-level breeding.

7. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 6, characterized in that: The market breeding price data of laying hens is collected, and the price trend analysis of breeding output is performed in combination with the breeding output risk control model to form the information on the change of laying hens output price trend, including: According to the market breeding price data, determine the change trend of the cost of different output quality risk factors of different levels in the time dimension, and perform periodic fitting to form the periodic change trend of the unit cost of output quality risk factors , where e is n, m, k; According to the market breeding price data, determine the change trend of the cost of different output risk factors in the time dimension, and perform periodic fitting to form the periodic change trend of the unit cost of output risk factors ; According to the market breeding price data, the price change trend of eggs of different grades in the time dimension is determined, and periodic fitting is performed to form the periodic change trend of the unit price of the output grade. , where g can be low, middle, or high.

8. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 7, characterized in that: The obtaining of the breeding output requirement information, combining the breeding output risk control model and the laying hen output price trend change information, performing breeding output risk identification, and forming breeding output risk identification data, includes: According to the breeding output requirement information, determine the required quantity of high-grade eggs And the corresponding high-level output demand ratio , the number of medium-grade eggs required And the corresponding ratio of medium-level output demand , the number of low-grade eggs required And the corresponding low-level output demand ratio and the deadline for the requirement; Proportion of demand at the above high level output and the ratio of the intermediate output requirements For reference, the corresponding parameter quantity control adjustment of output quality risk factors is carried out, and the following analysis and judgment are carried out: If the parameter quantity control adjustment of different output quality risk factors is achieved to achieve the high-level output demand ratio and the ratio of the intermediate output requirements , then according to the demand deadline and the different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of the output quality of the value combination with the lowest cost among the value combinations of the different output quality risk factors , and according to the required number of high-grade eggs 、The number of medium-grade eggs required And the number of low-grade eggs required And the yield impact relationship , combined with the different output risk factors, unit cost cycle change trend and the price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate the total cost based on the output quality , the estimated total cost of the output quantity and the estimated total sales of said output , determine the estimated profit of output ,in: ； If the parameter quantity control adjustment of different output quality risk factors cannot achieve the required proportion of high-level output and the ratio of the intermediate output requirements , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit ; Setting profit acceptance rate threshold , based on the estimated profit obtained from the output , the estimated total cost of the output quality and the estimated total cost of the output quantity , conduct the following risk identification analysis: when , then the profit control risk identification information is formed; when , then the non-controllable profit risk identification information is formed; when , then loss risk identification information is formed.

9. The risk assessment method for a laying hen breeding system based on a risk control engine according to claim 8, characterized in that: If the parameter quantity control adjustment of different output quality risk factors cannot achieve the high-level output demand ratio and the ratio of the intermediate output requirements , then adjust the output quantity to ensure quality and quantity, and determine the output estimated profit ,include: The ratio of the required output at the intermediate level For reference, determine the proportion of the output requirements in meeting the middle level The corresponding maximum allowable high-level output demand ratio , and according to the required number of high-grade eggs , combined with the mid-level aquaculture output risk control model , the high-level breeding output risk control model , the proportion of the intermediate output demand And the maximum allowable high-level output demand ratio , determine the quantity of eggs that meet the demand for the high grade eggs The necessary number of medium-grade eggs and the necessary number of low-grade eggs; According to the mid-level output demand ratio and the maximum allowable high-level output requirement ratio , combined with the demand deadline and the different output quality risk factors, the unit cost cycle change trend , determine the estimated total cost of the output quality of the value combination with the lowest cost among the value combinations of the different output quality risk factors , and according to the required number of high-grade eggs , the necessary number of medium-grade eggs and the necessary number of low-grade eggs and the relationship between the production , combined with the different output risk factors, unit cost cycle change trend and the price cycle trends of different output levels , determine the output quantity and estimate the total cost and output estimated total sales , estimate the total cost based on the output quality described , the estimated total cost of the output quantity and the estimated total sales of said output , determine the estimated profit of output ,in: 。 10. The risk assessment method for laying hen farming system based on risk control engine according to claim 9, characterized in that: The aquaculture risk treatment is performed according to the aquaculture output risk identification data to form aquaculture risk treatment result data, including: When the risk identification result is displayed as the controllable profit risk identification information, outputting no disposal information; When the risk identification result shows the uncontrollable profit risk identification information, the profit risk warning information and warning profit rate are output. ; When the risk identification result is displayed as the loss risk identification information, the production loss warning information and loss rate are output .

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