Analysis method for starting time of purification detection of leukemia in large-scale chicken flock breeding

Abstract

The present invention discloses an analysis method for the starting time of leukemia purification detection in large-scale chicken flock breeding, which relates to the technical field of poultry breeding. The key points of its technical solution are as follows: determining 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; determining the first correction coefficient according to the second detection result of the previous leukemia detection of the maternal chicken flock; determining 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; and 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 offspring chicken flock. 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 coefficients, the present invention significantly reduces the consumption of detection resources while ensuring the purification effect.

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 deaths, causing significant economic losses to the global poultry industry every year. This virus can spread rapidly among chicken flocks through vertical and horizontal transmission. Currently, there is no effective vaccine or 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 mechanism for triggering detection based on virus load thresholds, 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 horizontal transmission paths between different chicken flocks in the same breeding area. In addition, the above methods do not consider the associated impact of maternal and offspring detection results, 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 maternal and offspring detection results 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 fixed-cycle detection, the present invention can significantly increase the detection frequency of high-risk subgroups and significantly reduce the ineffective detection of low-risk subgroups, while ensuring the purification effect and significantly reducing the consumption of detection resources.

[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 conversion of 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 makes the basic time interval approach the minimum detection interval;

[0018] If the product is less than the risk threshold, the non-linear response function makes 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 process of determining the time decay parameter is as follows:

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

[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 attenuation law of virus activity, effectively eliminates the misjudgment interference caused by horizontal transmission in the same batch, and can effectively improve the accuracy of the 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 within 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 limit 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 combination with embodiments and drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention.

[0040] Embodiment: A method for analyzing the start time of leukemia purification detection in large-scale chicken farming. The chicken flock is divided into multiple breeding areas according to the hatching time, and each breeding area is equipped with multiple independent chicken coops. 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 flock is 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 from different maternal sources.

[0043] The maternal chicken flock is a 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 produced by 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, the dynamic initiation of leukemia purification detection in large-scale chicken flocks can be achieved. 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 analysis method for the initiation timing of leukemia purification detection based on the above 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 mapping 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] And using a non - linear response function to convert the mapping value in the first interval into a basic time interval, including: 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 of the basic time interval is as follows:

[0055] ;

[0056] Wherein, 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, and the average value of the viral loads in multiple consecutive times in the same breeding area can be adopted; 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 - risk and low - risk intervals.

[0057] In step S2, generally, the viral load of the maternal chicken flock has a positive - correlation impact on the viral infection situation of the filial chicken flock. Therefore, the first correction coefficient can be determined according to the second detection result of the previous leukemia detection of 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 according to 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 the same - level infection, 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 characteristic of the natural decay 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] Wherein, 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 progeny 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 progeny chicken flocks in the same breeding area has a positive correlation with the virus infection situation of the progeny 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 where the progeny chicken flock belongs.

[0067] In some examples, the second correction coefficient can be directly determined by the ratio of the virus load of other progeny chicken flocks in the same breeding area to the virus load of the progeny 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 the sub-lines with high positivity but no transmission risk. Therefore, considering only the high-positive sub-line-driven transmission and negative barrier blockage, the second correction coefficient is calculated based on the positive gradient, normalized distance, and path effectiveness among the sub-line chicken flocks in the same breeding area.

[0069] The positive gradient is: only considering the neighboring chicken flocks with a higher viral load than the sub-line 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 sub-line chicken flocks in the same breeding area; represents other sub-line chicken flocks 's viral load; represents the sub-line 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 sub-line chicken flocks and the sub-line chicken flock the Euclidean distance between the corresponding independent chicken coops; represents the distance decay exponent, controlling the rate at which the transmission risk decreases with distance; represents the path effectiveness factor, characterizing the blocking effect of the negative chicken coops on the virus on the transmission path, with a value range of 0-1; represents other sub-line chicken flocks 's time decay weight, reflecting the timeliness impact of the detection time of other sub-line 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 sub-line chicken flock and other sub-line 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 sub-line 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], preventing a single correction coefficient from dominating the time interval and avoiding the generation of 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 basic 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, equipped with environmental monitoring sensors.

[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. It can be seen from Table 1 that 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 path 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 assessment data are shown in Table 2. As can be seen from Table 2, the accuracy of the maternal vertical transmission assessment 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 economic losses by approximately 1.27 million yuan per 10,000 birds.

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

[0090] Index The method of the present invention Traditional method Positive prediction accuracy of vertical transmission 91.7% 68.4% Interference elimination 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 period 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-period detection, the present invention can significantly increase the detection frequency of high-risk subgroups while significantly reducing the ineffective detections of low-risk subgroups, significantly reducing the consumption of detection resources while ensuring the purification effect.

[0092] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. 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, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes 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 realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple 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 realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple 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 executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0096] The specific embodiments described above have further elaborated on the object, 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 shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing the starting time of purification detection of leukemia in large-scale chicken flock breeding, characterized in that, 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 the maternal chicken flock and the filial chicken flock according to the genetic relationship chain of the hatching eggs. The method includes the following steps: Determine the basic time interval for the next leukemia detection of the filial chicken flock according to the first detection result of the previous leukemia detection of the filial chicken flock; Determine the first correction coefficient according to the second detection result of the previous leukemia detection of the maternal chicken flock; Determine the second correction coefficient according to the detection distribution result of the previous leukemia detection of the breeding area to which the filial chicken flock belongs; Combine the basic time interval, the first correction coefficient and the second correction coefficient to determine the final time interval for the next leukemia detection of the filial chicken flock; The determination process of the basic time interval includes: Based on the first detection result, dynamically map the product of the virus load and the infection ratio to the first interval with a value range of (0,1) through the Sigmoid function; Use a non-linear response function to convert the mapped value of the first interval into the basic time interval; 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; The calculation formula of the first correction coefficient is as follows: ； Among them, 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 viral load of the maternal chicken flock; represents the maximum threshold of the viral load; represents the time decay factor, which characterizes the rate of decline in virus activity over time; represents the detection time difference between the maternal chicken flock and the offspring chicken flock; represents the original viral 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 's viral 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; represents the distance decay exponent, which controls the rate of decline of the transmission risk with distance; represents the smoothing factor; The determination process of the second correction coefficient includes: Calculate the second correction coefficient based on the positive gradient, normalized distance and path effectiveness among the filial chicken flocks in the same breeding area; The positive gradient is: only consider the neighboring chicken flocks with a higher virus load than that of the filial chicken flock to simulate the natural diffusion direction of the virus from the high-load area to the low-load area.

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 first detection result includes the virus load, the number of positive chickens and the total number of the chicken flock, and the infection ratio is the ratio of the number of positive chickens to the total number of the chicken flock.

3. The analysis method for the starting time of purification detection of leukemia in large-scale chicken flock breeding according to claim 1, wherein, The step of using a non-linear response function to convert the mapped value of the first interval into the basic time interval includes: 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; 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.

4. 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 determination process of the time decay parameter is: Obtain the time decay parameter by simulating the characteristic of the natural decay of the activity of the leukemia virus vertically transmitted through the hatching eggs over time.

5. 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 determination process of the final time interval includes: Use the hyperbolic tangent function to transform the first correction coefficient and the second correction coefficient into the second interval with a value range of [-1,1]; Multiply the basic time interval by the transformed value of the second interval to obtain the final time interval.

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