Large-scale chicken flock breeding leukemia purification detection starting opportunity analysis method

Abstract

The invention discloses a large-scale chicken flock breeding leukemia purification detection starting opportunity analysis method, and relates to the technical field of poultry breeding, and the key points of the technical scheme are as follows: determining a basic time interval of leukemia detection on a subsystem chicken flock next time according to a first detection result of leukemia detection on the subsystem chicken flock last time; determining a first correction coefficient according to a second detection result obtained by performing leukemia detection on the maternal chicken flocks last time; determining a second correction coefficient according to a detection distribution result obtained by performing leukemia detection on the breeding area to which the subsystem chicken flock belongs last time; and combining the basic time interval, the first correction coefficient and the second correction coefficient to determine a final time interval for performing leukemia detection on the subline chicken flocks next time. According to the method, a linkage correction mechanism of maternal line and child line detection results is constructed, and dynamic coupling calculation of basic time intervals and double correction coefficients is combined, so that the purification effect is guaranteed, and meanwhile, the detection resource consumption is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of poultry farming, and more specifically, it relates to a method for analyzing the starting time of leukemia purification detection in large-scale chicken flock farming. Background Art

[0002] Avian leukemia is an immunosuppressive disease of poultry caused by avian leukemia virus (ALV), which can lead to a decline in the production performance of chicken flocks and neoplastic death, causing significant economic losses to the global poultry industry every year. This virus can spread rapidly among chicken flocks through vertical and horizontal transmission. At present, there is no effective vaccine and specific therapeutic drug, and source purification has become the core means of preventing and controlling this disease.

[0003] Currently, large-scale farms generally adopt a purification strategy of regular sampling inspection combined with positive elimination. However, the spatio-temporal heterogeneity of dynamic infection risks makes it difficult to balance detection efficiency and resource consumption with a fixed detection cycle. For this reason, some existing technologies record a detection mechanism triggered based on a virus load threshold, but do not consider the continuous impact of maternal vertical transmission on the infection risk of offspring; in addition, some existing technologies also record the use of a fixed time window combined with the infection rate to adjust the detection frequency, but it ignores the dynamic blocking effect of the horizontal transmission path among different chicken flocks in the same breeding area; moreover, the above methods do not consider the associated impact of the detection results of the maternal and offspring lines, resulting in a lack of quantitative support for the vertical transmission link in the adjustment of the detection cycle, and there are problems of detection lag or over-detection.

[0004] Therefore, how to research and design a method for analyzing the starting time of leukemia purification detection in large-scale chicken flock farming that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a method for analyzing the starting time of leukemia purification detection in large-scale chicken flock farming. By constructing a linkage correction mechanism for the detection results of the maternal and offspring lines and combining the dynamic coupling calculation of the basic time interval and the double correction coefficient, the risk self-adaptive adjustment of the detection cycle is realized; compared with the fixed-cycle detection, the present invention can significantly increase the detection frequency of high-risk subgroups, while significantly reducing the ineffective detection of low-risk subgroups, and significantly reducing the consumption of detection resources while ensuring the purification effect.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions: A method for analyzing the starting time of leukemia purification detection in large-scale chicken flock farming, the chicken flock is divided into multiple breeding areas according to the hatching time, each of the breeding areas is equipped with multiple independent chicken coops, and the independent chicken coops in different breeding areas are marked as maternal chicken flocks and offspring chicken flocks according to the hatching relationship chain of breeding eggs. The method includes the following steps:

[0007] Determine the basic time interval for the next leukemia detection of the sub-line chicken flock according to the first detection result of the previous leukemia detection of the sub-line chicken flock;

[0008] Determine the first correction coefficient according to the second detection result of the previous leukemia detection of the maternal-line chicken flock;

[0009] Determine the second correction coefficient according to the detection distribution result of the previous leukemia detection of the breeding area to which the sub-line chicken flock belongs;

[0010] Combine the basic time interval, the first correction coefficient and the first correction coefficient to determine the final time interval for the next leukemia detection of the sub-line chicken flock.

[0011] Furthermore, the determination process of the basic time interval includes:

[0012] Based on the first detection result, dynamically map the product of the viral load and the infection ratio to the first interval with a value range of (0, 1) through the Sigmoid function;

[0013] Convert the mapped value of the first interval into the basic time interval by using a non-linear response function;

[0014] Among them, the minimum value of the basic time interval is the minimum detection interval, and the maximum value of the basic time interval is the maximum detection interval.

[0015] Furthermore, the first detection result includes the viral load, the number of positive chickens and the total number of chickens in the flock, and the infection ratio is the ratio of the number of positive chickens to the total number of chickens in the flock.

[0016] Furthermore, the process of converting the mapped value of the first interval into the basic time interval by using a non-linear response function includes:

[0017] If the product is greater than the risk threshold, the non-linear response function will make the basic time interval approach the minimum detection interval;

[0018] If the product is less than the risk threshold, the non-linear response function will make the basic time interval approach the maximum detection interval.

[0019] Furthermore, the determination process of the first correction coefficient includes:

[0020] Calculate the first correction coefficient through the maternal-line pure vertical positive value and the time decay parameter.

[0021] Furthermore, the determination process of the maternal-line pure vertical positive value is:

[0022] Calculate the horizontal transmission contribution value of other maternal chicken flocks in the same breeding area to the maternal chicken flock based on the distance decay model;

[0023] The horizontal transmission contribution value is normalized, and the normalized horizontal transmission value is removed from the total viral load of the maternal flock to obtain the maternal pure vertical positive value.

[0024] Furthermore, the determination process of the time decay parameter is as follows:

[0025] The time decay parameter was obtained by simulating the natural decay of the activity of leukemia virus transmitted vertically through eggs.

[0026] Furthermore, the process of determining the second correction coefficient includes:

[0027] The second correction coefficient is calculated based on the positive gradient, normalized distance and path validity between each sub-lineage chicken flock in the same breeding area.

[0028] Furthermore, the positive gradient is: only considering neighboring chicken flocks with higher viral loads than the sub-lineage chicken flock, so as to simulate the natural spread direction of the virus from the high load area to the low load area.

[0029] Furthermore, the process of determining the final time interval includes:

[0030] Using a hyperbolic tangent function to transform the first correction coefficient and the second correction coefficient into a second interval having a value range of [-1, 1];

[0031] The basic time interval is multiplied by the transformed value of the second interval to obtain the final time interval.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention provides a method for analyzing the timing of starting leukemia purification detection in large-scale chicken farming. By constructing a linkage correction mechanism for the detection results of the maternal and pedigree lines, combined with the dynamic coupling calculation of the basic time interval and the double correction coefficient, the risk adaptive adjustment of the detection cycle is realized. Compared with fixed-cycle detection, the present invention can significantly increase the detection frequency of high-risk subgroups and significantly reduce invalid detection of low-risk subgroups, thereby significantly reducing the consumption of detection resources while ensuring the purification effect.

[0034] 2. The present invention uses Sigmoid function and nonlinear response function to dynamically map the product of viral load and infection ratio, establishes an exponential response relationship between detection interval and biosafety risk, and can significantly improve the sensitivity of high-risk identification by times. When the viral load mutates, emergency detection can be automatically triggered, and potential virus spread can be discovered in advance.

[0035] 3. The present invention separates the maternal pure vertical positive values through a distance attenuation model, combines the time attenuation parameter to quantify the law of virus activity attenuation, effectively eliminates the misjudgment interference caused by horizontal transmission in the same batch, and can effectively improve the accuracy of maternal transmission risk assessment;

[0036] 4. The present invention constructs a second correction coefficient based on the positive gradient, normalized distance and path effectiveness, accurately characterizes the virus diffusion direction and intensity in the breeding area, and can successfully identify most of the hidden transmission paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0038] Figure 1 is a flowchart in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the embodiments and the drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not constitute a limitation to the present invention.

[0040] Embodiment: A method for analyzing the start-up timing of leukemia purification detection in large-scale chicken flocks. The chicken flocks are divided into multiple breeding areas according to the hatching time. Each breeding area is equipped with multiple independent chicken coops, and the independent chicken coops in different breeding areas are marked as maternal chicken flocks and offspring chicken flocks according to the egg hatching relationship chain.

[0041] Specifically, the large-scale chicken flocks are divided into multiple independent breeding areas according to the egg hatching time (such as batches, time periods). Each area represents a chicken flock born within the same hatching cycle. For example, Area A: the hatching batch in January 2023; Area B: the hatching batch in February 2023.

[0042] And multiple physically isolated independent chicken coops are set in each breeding area to separate chicken flocks from different sources. For example, Area A contains chicken coops A1, A2, and A3, which respectively raise offspring chicken flocks with different maternal sources.

[0043] The maternal chicken flock is the group traced back to the source hens through the egg hatching relationship chain. For example, the maternal of chicken coop A1 in Area A is the previous generation maternal chicken flock M1. The offspring chicken flock is the offspring group hatched from the eggs of the maternal chicken flock. For example, the eggs of the maternal chicken flock M1 hatch to form the offspring chicken flock A1.

[0044] The present invention uses maternal and offspring markers to clarify the chain of vertical transmission of the virus through hatching eggs. By raising chicken flocks with different hatching times and maternal origins in different regions and cages, it is possible to dynamically initiate the purification detection of leukemia in large-scale chicken flocks. Compared with the traditional centralized detection method, it can effectively balance the work intensity, improve the coverage of leukemia purification detection, and make the leukemia purification response more accurate and reliable.

[0045] The method for analyzing the initiation timing of leukemia purification detection based on the above-mentioned large-scale chicken flock breeding method is as Figure 1 shown and includes the following steps:

[0046] S1: Determine the basic time interval for the next leukemia detection of the offspring chicken flock according to the first detection result of the previous leukemia detection of the offspring chicken flock;

[0047] S2: Determine the first correction coefficient according to the second detection result of the previous leukemia detection of the maternal chicken flock;

[0048] S3: Determine the second correction coefficient according to the detection distribution result of the previous leukemia detection of the breeding area to which the offspring chicken flock belongs;

[0049] S4: Combine the basic time interval, the first correction coefficient, and the first correction coefficient to determine the final time interval for the next leukemia detection of the offspring chicken flock.

[0050] In step S1, since the traditional fixed detection cycle cannot adapt to the dynamic infection risks of different subgroups, the present invention dynamically compresses the positive values into high- and low-risk intervals through the Sigmoid function. For high-risk groups, the detection cycle can be adaptively shortened, while for low-risk groups, the detection cycle can be adaptively extended, avoiding resource waste or missed detections caused by fixed cycles.

[0051] The determination process of the basic time interval includes: based on the first detection result, dynamically mapping the product of the virus load and the infection ratio to a first interval with a value range of (0, 1) through the Sigmoid function; using a non-linear response function to convert the mapped value of the first interval into the basic time interval; where the minimum value of the basic time interval is the minimum detection interval, and the maximum value of the basic time interval is the maximum detection interval.

[0052] It should be noted that the first detection result includes the virus load, the number of positive chickens, and the total number of chickens in the flock, and the infection ratio is the ratio of the number of positive chickens to the total number of chickens in the flock.

[0053] The adoption of a non-linear response function to convert the mapping value in the first interval into a basic time interval includes: when the product is greater than the risk threshold, the non-linear response function makes the basic time interval approach the minimum detection interval; when the product is less than the risk threshold, the non-linear response function makes the basic time interval approach the maximum detection interval.

[0054] For example, the calculation formula for the basic time interval is as follows:

[0055] ;

[0056] Among them, represents the basic time interval; represents the maximum detection interval, such as taking a value of 30 days; represents the minimum detection interval, such as taking a value of 7 days; represents the steepness parameter of the Sigmoid curve, mainly used to control the risk response sensitivity, generally taking a value of 0.5; represents the viral load; represents the reference load, which can adopt the average value of the viral loads in multiple consecutive times in the same breeding area; represents the number of positive chickens; represents the total number of the chicken flock; represents the risk threshold, such as taking a value to divide the high and low risk intervals.

[0057] In step S2, generally, the viral load of the maternal chicken flock has a positive correlation with the viral infection situation of the offspring chicken flock. Therefore, the first correction coefficient can be determined according to the second test result of the previous leukemia test on the maternal chicken flock. Combining the first correction coefficient with the basic time interval can achieve vertical risk transmission correction.

[0058] In some examples, the first correction coefficient can be directly determined based on the viral load of the maternal chicken flock. For example, the greater the viral load of the maternal chicken flock, the smaller the first correction coefficient.

[0059] In some examples, considering that the self-positivity of the maternal line may be contaminated by same-level infections, directly applying the viral load of the maternal chicken flock to determine the first correction coefficient is likely to lead to a large vertical correction error. Therefore, by separating the same-level horizontal transmission interference in the maternal positive value and only retaining the vertical transmission part, the purification targeting can be improved.

[0060] Specifically, the determination process of the first correction coefficient includes: calculating the first correction coefficient through the pure vertical positive value of the maternal line and the time decay parameter.

[0061] The determination process of the maternal pure vertical positive value is as follows: Based on the distance decay model, calculate the horizontal transmission contribution value of other maternal chicken flocks to the maternal chicken flock in the same breeding area; normalize the horizontal transmission contribution value, and subtract the normalized horizontal transmission value from the total virus load of the maternal chicken flock to obtain the maternal pure vertical positive value.

[0062] The determination process of the time decay parameter is as follows: By simulating the natural decay characteristics of the activity of leukemia virus vertically transmitted through hatching eggs over time, the time decay parameter is obtained.

[0063] For example, the calculation formula of the first correction coefficient is as follows:

[0064] ;

[0065] where represents the first correction coefficient; represents the vertical transmission intensity factor, which characterizes the inherent risk ratio of the maternal virus transmitted through hatching eggs; represents the pure vertical virus load of the maternal chicken flock; represents the maximum threshold of the virus load; represents the time decay factor, which characterizes the rate of decline of virus activity over time; represents the detection time difference between the maternal chicken flock and the offspring chicken flock; represents the original virus load of the maternal chicken flock; represents the total number of maternal chicken flocks in the same breeding area; represents other maternal chicken flocks in the same breeding area of the virus load; represents the Euclidean distance between the independent chicken coops corresponding to the maternal chicken flock m and other maternal chicken flocks ; represents the reference distance, such as taking a value of 1 meter; represents the distance decay exponent, which controls the rate of decline of the transmission risk with distance; represents the smoothing factor, taking a relatively small positive number, such as 10 to the power of negative 6.

[0066] In step S3, generally, the virus load of other offspring chicken flocks in the same breeding area has a positive correlation with the virus infection situation of the offspring chicken flock. Therefore, the second correction coefficient can be determined according to the detection distribution result of the previous leukemia detection of the breeding area to which the offspring chicken flock belongs.

[0067] In some examples, the second correction coefficient can be directly determined by the ratio of the virus load of other offspring chicken flocks in the same breeding area to the virus load of the offspring chicken flock, combined with the distance between independent chicken flocks.

[0068] In some examples, considering that the above method ignores the propagation direction and path blockage, it is easy to misjudge sublines with high positivity but no propagation risk. Therefore, considering only high-positive sublines driving the spread and negative barriers blocking, the second correction coefficient is calculated based on the positive gradient, normalized distance, and path effectiveness among sublines of chicken flocks in the same breeding area.

[0069] The positive gradient is: only considering neighboring chicken flocks with a higher viral load than the subline of chicken flock to simulate the natural diffusion direction of the virus from the high-load area to the low-load area.

[0070] For example, the calculation formula of the second correction coefficient is as follows:

[0071] ;

[0072] Where, represents the second correction coefficient; represents the number of other sublines of chicken flocks in the same breeding area; represents other sublines of chicken flocks 's viral load; represents the subline of chicken flock 's viral load; represents the maximum threshold of the viral load; represents the reference distance, such as taking a value of 1 meter; represents other sublines of chicken flocks and the subline of chicken flock the Euclidean distance between the corresponding independent chicken coops; represents the distance attenuation exponent, controlling the rate at which the transmission risk decreases with distance; represents the path effectiveness factor, characterizing the blocking effect of negative chicken coops on the virus on the transmission path, with a value range of 0 - 1; represents other sublines of chicken flocks 's time decay weight, reflecting the timeliness impact of the detection time of other sublines of chicken flocks on the current risk; represents the blocking intensity factor, controlling the attenuation rate of the path effectiveness by the number of negative barriers; represents the subline of chicken flock and other sublines of chicken flocks the number of negative chicken coops on the straight-line path.

[0073] In step S4, combining the basic time interval, the first correction coefficient, and the first correction coefficient to determine the final time interval for the next leukemia detection of the subline of chicken flock can be achieved by the method of linear superposition, and the calculation formula is as follows:

[0074] ;

[0075] Where, represents the final time interval; represents the weight coefficient of the second correction coefficient; represents the weight coefficient of the first correction coefficient.

[0076] In some examples, considering that linear superposition may lead to excessive periodic fluctuations or insufficient sensitivity, the hyperbolic tangent function can also be used to compress the correction coefficient to [-1, 1] to prevent a single correction coefficient from dominating the time interval and avoid generating invalid time intervals under extreme risks.

[0077] Specifically, the determination process of the final time interval includes: using the hyperbolic tangent function to transform the first correction coefficient and the second correction coefficient into a second interval with a value range of [-1, 1]; multiplying the base time interval by the transformed value of the second interval to obtain the final time interval.

[0078] For example, the calculation formula of the final time interval is as follows:

[0079] ;

[0080] where, represents the hyperbolic tangent function.

[0081] It should be noted that the weight coefficients of the first correction coefficient and the second correction coefficient can be obtained by statistically analyzing historical data using statistical methods, or the weights can be optimized by gradient descent based on historical infection data and regularization constraints.

[0082] Experimental verification

[0083] Experimental group: 3 large-scale laying hen farms (A1 - A3) applying the method of the present invention, each farm includes: 6 breeding areas (divided by hatching batches); 12 groups of maternal chicken flocks and 72 groups of offspring chicken flocks; the spacing between independent chicken coops is 1.5 - 3 meters, and environmental monitoring sensors are equipped.

[0084] Control group: 3 farms of the same scale (B1 - B3) using traditional fixed-cycle detection

[0085] The statistical results of indicators such as detection frequency, detection sensitivity, and resource consumption are shown in Table 1. As can be seen from Table 1, the dynamic detection mechanism increases the detection frequency of high-risk groups by 65% and reduces the invalid detections of low-risk groups by 37.5%; in addition, the Sigmoid risk mapping can identify virus spread 63 hours earlier than the traditional threshold method.

[0086] Table 1 Statistical results of indicators

[0087] Index Experimental group (A1 - A3) Control group (B1 - B3) Improvement ratio Detection frequency Number of detections of high - risk subgroups 38 times / group 23 times / group +65% Number of detections of low - risk subgroups 15 times / group 24 times / group -37.5% Detection sensitivity Detection time limit for virus load mutation 41.2 hours 113.5 hours 63% earlier Recognition rate of latent transmission paths 82.3% 34.7% +137% Resource consumption Consumption of detection reagents (L) 127.5 214.8 -40.6% False positive rate 6.2% 23.8% -73.9%

[0088] The statistical results of the maternal transmission evaluation data are shown in Table 2. As can be seen from Table 2, the accuracy of maternal vertical transmission evaluation has increased by 34.3%, and the accuracy of breeder culling has reached 93.5%. In addition, the path effectiveness analysis has successfully blocked 82.3% of the latent transmissions, reducing the economic loss by approximately 1.27 million yuan per 10,000 birds.

[0089] Table 2 Statistical Results of Maternal Transmission Evaluation Data

[0090] Index Method of the present invention Traditional method Positive prediction accuracy of vertical transmission 91.7% 68.4% Interference rejection rate of the same batch 89.2% Not implemented Accuracy of breeder culling 93.5% 71.2% Fitting degree of virus activity decay (R²) 0.872 0.512

[0091] Working principle: The present invention realizes the risk adaptive adjustment of the detection cycle by constructing a linkage correction mechanism for the detection results of the maternal and offspring lines and combining the dynamic coupling calculation of the basic time interval and the double correction coefficient. Compared with the fixed-cycle detection, the present invention can significantly increase the detection frequency of high-risk subgroups and significantly reduce the ineffective detections of low-risk subgroups, while ensuring the purification effect and significantly reducing the consumption of detection resources.

[0092] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0094] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps of the functions specified in one block or a plurality of blocks.

[0096] The specific embodiments described above have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the starting time of leukemia purification detection in large-scale chicken flock breeding, characterized in that, The chicken flock is divided into a plurality of breeding areas according to the hatching time, each breeding area is equipped with a plurality of independent chicken cages, and the independent chicken cages in different breeding areas are marked as a maternal chicken flock and a daughter chicken flock according to the hatching relationship chain of the breeding eggs. The method comprises the following steps: Determine the basic time interval for performing leukemia detection on the sub-lineage chicken group next time according to the first detection result of the leukemia detection on the sub-lineage chicken group last time; Determining a first correction coefficient according to a second test result of a last leukemia test on the maternal chicken flock; Determine a second correction coefficient based on the distribution result of the last leukemia test performed on the breeding area to which the sub-lineage chickens belong; The basic time interval, the first correction factor and the first correction factor are combined to determine the final time interval for the next leukemia detection of the sub-line chicken group.

2. The analysis method for the start-up timing of leukemia purification detection in large-scale chicken flock breeding according to claim 1, wherein The process of determining the basic time interval includes: Based on the first detection result, dynamically mapping the product of the viral load and the infection ratio to a first interval with a value range of (0,1) through a Sigmoid function; Using a nonlinear response function to convert the mapping value of the first interval into the basic time interval; The minimum value of the basic time interval is the minimum detection interval, and the maximum value of the basic time interval is the maximum detection interval.

3. The analysis method for the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 2, characterized in that, The first test result includes the viral load, the number of positive chickens and the total number of chickens in the flock, and the infection ratio is the ratio of the number of positive chickens to the total number of chickens in the flock.

4. A method for analyzing the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 2, characterized in that The adopting a nonlinear response function to convert the mapping value of the first interval into the basic time interval includes: If the product is greater than the risk threshold, the nonlinear response function makes the basic time interval approach the minimum detection interval; If the product is less than the risk threshold, the nonlinear response function makes the basic time interval approach the maximum detection interval.

5. The analysis method for the initiation time of leukemia purification detection in large-scale chicken flock breeding according to claim 1, wherein, The process of determining the first correction coefficient includes: The first correction factor is calculated by the parent pure vertical positive value and the time decay parameter.

6. The analysis method for the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 5, characterized in that, The determination process of the maternal pure vertical positive value is as follows: Calculate the horizontal transmission contribution value of other maternal chicken flocks in the same breeding area to the maternal chicken flock based on the distance decay model; The horizontal transmission contribution value is normalized, and the normalized horizontal transmission value is removed from the total viral load of the maternal flock to obtain the maternal pure vertical positive value.

7. A method for analyzing the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 5, characterized in that The determination process of the time decay parameter is: The time decay parameter was obtained by simulating the natural decay of the activity of leukemia virus transmitted vertically through eggs.

8. A method for analyzing the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 1, characterized in that, The process of determining the second correction coefficient includes: The second correction coefficient is calculated based on the positive gradient, normalized distance and path validity between each sub-lineage chicken flock in the same breeding area.

9. The analysis method for the starting time of leukemia purification detection in large-scale chicken flock breeding according to claim 8, characterized in that, The positive gradient is: only neighboring chicken flocks with higher viral loads than the sub-lineage chicken flock are considered to simulate the natural spread direction of the virus from high load areas to low load areas.

10. The analysis method for the initiation timing of leukemia purification detection in large-scale chicken flock breeding according to claim 1, characterized in that, The process of determining the final time interval includes: Using a hyperbolic tangent function to transform the first correction coefficient and the second correction coefficient into a second interval having a value range of [-1, 1]; Multiply the base time interval by the transformation value of the second interval to obtain the final time interval.

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