A comprehensive monitoring intelligent management system and method for hatching eggs

Abstract

The present invention discloses a fully monitorable intelligent management system and method for hatching breeding eggs, relating to the field of poultry breeding technology. The method comprises inputting the weights of multiple breeding hens at hatching and the total number of eggs laid by the corresponding breeding hens during their egg-laying cycle into the input and output layers of an egg-laying prediction model and training the model until convergence; obtaining the hatchability of first-generation breeding eggs and the weight distribution interval of the first-generation breeding hens at hatching; obtaining the average number of three generations of breeding eggs corresponding to each breeding egg based on the hatchability, weight distribution interval of the first-generation breeding hens at hatching, and the egg-laying prediction model, as the hatching quality index of the first-generation breeding eggs; and adjusting the parameter combination of the incubation conditions for each type of breeding egg to maximize the hatching quality index of the first-generation breeding eggs, thereby obtaining the optimal parameters for the incubation conditions for each type of breeding egg. The present invention can quickly test the optimal incubation state for breeding eggs.

Description

Technical Field

[0001] The present invention belongs to the technical field of poultry breeding, and in particular relates to an intelligent management system and method for hatching breeding eggs capable of comprehensive monitoring. Background Art

[0002] Hatching eggs are eggs used for incubation. These eggs typically come from various poultry, such as chickens, ducks, geese, and turkeys. In agricultural production, hatching eggs are placed in incubators. The resulting chicks (such as chicks and ducklings) can be used for meat production or raised to become the next generation of breeder hens and ducks.

[0003] Traditional methods of hatching eggs usually rely on manual management and control, including the management of temperature, humidity, ventilation, egg turning and other incubation links. This management method is labor-intensive and prone to inadequate management, resulting in unsatisfactory hatching results.

[0004] Not only that, the breeding quality of the eggs can only be fully reflected in the subsequent breeding process, which makes it difficult to adjust the incubation environment of the eggs in a timely manner and effectively prevent damage during the incubation process. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent management system and method for hatching eggs that can be fully monitored. By establishing a quantitative influence relationship between the status of chicks after hatching and their future breeding capacity, the iterative cycle of the hatching status test of the eggs is shortened, and the optimal hatching status suitable for the eggs can be tested in a short time.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention provides an intelligent management method for hatching eggs that can be fully monitored, comprising:

[0008] Obtain the weight of multiple breeder hens at the time of hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle;

[0009] The weights of multiple breeder hens at hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle are input into the input layer and output layer of the egg production prediction model respectively, and the model is trained until convergence.

[0010] Classify the first generation of hatching eggs and obtain the category of each hatching egg;

[0011] For each category of first-generation breeding eggs, incubate the first-generation breeding eggs under each type of incubation condition parameter combination state to obtain the hatchability of the first-generation breeding eggs and the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching;

[0012] The hatching quality index of the first-generation eggs is obtained based on the hatching rate of the first-generation eggs, the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching, and the egg production prediction model; the average number of three-generation breeding eggs corresponding to each breeding egg is obtained as the hatching quality index of the first-generation breeding eggs;

[0013] The parameter combination of each type of incubation condition is adjusted separately to maximize the hatching quality index of the first generation of hatching eggs, and the optimal parameters of the incubation conditions of each type of hatching eggs are obtained.

[0014] The present invention also discloses a fully monitored intelligent management method for hatching eggs, comprising:

[0015] Obtain the optimal parameters for each type of incubation conditions for each type of egg;

[0016] Group eggs from multiple categories that share the same optimal parameters for incubation conditions within each category into one category.

[0017] The present invention also discloses an intelligent management method for hatching eggs that can be fully monitored, comprising:

[0018] Place eggs of the same type in the same egg incubator;

[0019] Obtaining the type of hatching eggs in the hatching egg incubator;

[0020] Obtain the optimal parameters for each type of hatching egg under each type of incubation conditions;

[0021] Set the incubation conditions for each type of egg incubator according to the optimal parameters.

[0022] The present invention also discloses an intelligent management system for hatching eggs that can be fully monitored, comprising:

[0023] The model training unit is used to obtain the weight of multiple breeder chickens at the time of hatching and the total number of eggs laid by the corresponding breeder chickens during the laying cycle;

[0024] The weights of multiple breeder hens at hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle are input into the input layer and output layer of the egg production prediction model respectively, and the model is trained until convergence.

[0025] The hatching egg classification unit is used to classify the first generation of hatching eggs and obtain the category of each hatching egg;

[0026] The hatching monitoring and management unit is used to, for each category of first-generation breeding eggs, incubate the first-generation breeding eggs under each type of parameter combination of incubation conditions, and obtain the hatching rate of the first-generation breeding eggs and the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching;

[0027] The hatching quality index of the first-generation eggs is obtained based on the hatching rate of the first-generation eggs, the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching, and the egg production prediction model; the average number of three-generation breeding eggs corresponding to each breeding egg is obtained as the hatching quality index of the first-generation breeding eggs;

[0028] Adjust the parameter combination of each type of incubation condition to maximize the hatching quality index of the first generation of hatching eggs, and obtain the optimal parameters for each type of hatching condition for each category of hatching eggs;

[0029] A hatching egg incubator, used to place hatching eggs of the same type in the same hatching egg incubator;

[0030] Obtaining the type of hatching eggs in the hatching egg incubator;

[0031] Obtain the optimal parameters for each type of hatching egg under each type of incubation conditions;

[0032] Set the incubation conditions for each type of egg incubator according to the optimal parameters.

[0033] The present invention associates the weight of the chick state of the breeder chicken with the total number of eggs laid during the egg-laying cycle and trains an egg-laying prediction model for predicting the total number of eggs laid. Through this model, the total egg production can be predicted when the breeder eggs are hatched into chicks, thereby shortening the cycle for judging the future breeding capacity of the breeder eggs. Specifically, the parameter combination of the incubation conditions of each type is adjusted, a generation of breeder eggs is hatched under the incubation conditions of these parameter combinations, and after the first generation of breeder eggs is hatched, the hatching quality index of the first generation of breeder eggs is calculated through the egg-laying prediction model. By continuously adjusting the parameter combination of the incubation conditions of each type, the optimal parameters of each type of incubation conditions of each type of breeder eggs are finally tested, so that the optimal incubation state suitable for the breeder eggs can be tested in a short time.

[0034] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a schematic diagram of an embodiment of an intelligent management system for hatching eggs capable of comprehensive monitoring according to the present invention;

[0037] Figure 2This is a schematic diagram of the steps of the model training unit, the egg classification unit, and the hatching monitoring and management unit in one embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the steps of the egg classification unit according to one embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of the steps of the egg incubation device according to one embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of the process flow of step S3 in one embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of the process flow of step S6 in one embodiment of the present invention;

[0042] Figure 7 This is a schematic diagram of the process flow of step S66 in one embodiment of the present invention;

[0043] Figure 8 This is a schematic diagram of the process flow of step S661 in one embodiment of the present invention;

[0044] Figure 9 This is a schematic diagram of the process flow of step S663 in one embodiment of the present invention;

[0045] Figure 10 This is a schematic diagram of a flow chart of step S665 in one embodiment of the present invention;

[0046] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0047] 1-Model training unit, 2-Breed egg classification unit, 3-Incubation monitoring and management unit, 4-Breed egg incubation device. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0049] It should be noted that the terms "first," "second," and the like in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application as detailed in the appended claims.

[0050] Breeding eggs are eggs from a specific breed of poultry or other animal used for breeding. Breeding eggs are typically collected from breeding sires and hens with superior genetic traits. These breeders are selected and bred to ensure optimal production performance, health, and adaptability. Of paramount importance is the egg production of the breeding eggs and their offspring.

[0051] The various incubation conditions during the incubation process have a profound impact on the subsequent growth and reproduction of the eggs. Hatching eggs involves placing the collected eggs in an appropriate incubation environment to promote embryonic development and hatching of chicks. The incubation process can be carried out through the following steps.

[0052] Incubator Preparation: Ensure the incubator is at the appropriate temperature and humidity conditions. Set the appropriate temperature and humidity parameters according to the requirements of different poultry species. Check the incubator for proper operation and ensure the accuracy of the thermometer and hygrometer. Egg Selection: Carefully inspect the eggs, eliminating any broken, deformed, or surface-damaged eggs. Select only eggs that appear intact and are free of odor. This helps increase the chances of successful hatching. Incubator Preheating: Preheat the incubator to the appropriate incubation temperature. Depending on the requirements of the poultry species, the temperature is typically set between 33.5 and 39 degrees Celsius, but this range is too wide. Egg Positioning: Gently place the selected eggs in the incubator trays or cells, ensuring they are evenly distributed and avoid crowding. Incubation Environment Management: Adjust the humidity and ventilation of the incubator according to the requirements of the incubator. Controlling humidity helps prevent the eggshell from drying out, while ventilation provides fresh oxygen and removes carbon dioxide. Egg Turning: During incubation, turn the eggs regularly (usually 2-3 times a day) to prevent the embryo from adhering to the shell and promote uniform embryonic development. However, this frequency is too wide. Incubation observation: During the incubation process, regularly observe the eggs in the incubator, pay attention to the stability of the incubator temperature and humidity, and pay attention to whether the eggs have any abnormal conditions, such as cracks or abnormal development.

[0053] The aforementioned egg incubation process reveals that some conventional incubation conditions are too broad, making their impact on subsequent egg growth and reproduction difficult to accurately quantify. Furthermore, the subsequent reproductive capacity of eggs requires multiple generations of continuous breeding, making the testing cycle too long to accurately measure. To improve the efficiency of testing the incubation status of eggs, the present invention provides the following solution.

[0054] See also Figures 1 to 4As shown, the present invention provides an intelligent management system for hatching of breeding eggs that can be fully monitored, which is divided into a model training unit 1, a breeding egg classification unit 2, an incubation monitoring and management unit 3, and a breeding egg incubation device 4 in terms of functional modules. The model training unit 1 in this solution is a virtual functional unit, which only needs to complete the modeling of the egg production prediction model. The breeding egg classification unit 2 is also a virtual functional unit, and its purpose is also to classify the breeding eggs so that they can be placed in different breeding egg incubation devices 4. The incubation monitoring and management unit 3 is the core of this solution, which can be specifically embodied as a computing unit with a data reading function. The breeding egg incubation device 4 can be an egg incubator, which usually refers to a device used for artificial incubation of eggs, also known as an incubator. It provides a controlled environment to simulate the conditions for hens to incubate eggs, promote the development of the embryo in the egg and hatch the chicks.

[0055] In the specific operation process, the model training unit 1 first executes step S1 to obtain the weight of multiple breeder chickens at the time of hatching and the total number of eggs laid by the corresponding breeder chickens during the egg-laying cycle. This process can be carried out simultaneously and is not limited to the offspring of the breeder chickens produced by the hatching of a specific breeder egg. Next, step S2 can be executed to input the weight of multiple breeder chickens at the time of hatching and the total number of eggs laid by the corresponding breeder chickens during the egg-laying cycle into the input layer and output layer of the egg-laying prediction model and train until convergence. Since in actual operation, it can be multiple breeder chickens without blood relationship, the sample data for training the egg-laying prediction model can be free from the restrictions of the growth and breeding cycle of the breeder chickens.

[0056] Next, the egg classification unit 2 can execute step S3 to classify the first-generation eggs to obtain the category of each egg. This is because different categories of eggs need to be tested and detected in a targeted manner. During the specific testing process, the hatching monitoring management unit 3 first executes step S4 for each category of first-generation eggs, incubating the first-generation eggs under the parameter combination of each type of incubation conditions, and obtaining the hatchability of the first-generation eggs and the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching. Next, step S5 can be executed to obtain the average number of three-generation eggs corresponding to each egg based on the hatchability of the first-generation eggs, the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching and the egg production prediction model as the hatching quality index of the first-generation eggs. Finally, step S6 can be executed to adjust the parameter combination of each type of incubation conditions respectively so that the hatching quality index of the first-generation eggs is maximized, and the optimal parameters for each category of eggs under each type of incubation conditions are obtained. In this solution, the first-generation eggs become first-generation breeder chickens after hatching, and the first-generation breeder chickens grow and produce second-generation eggs. The second-generation eggs become second-generation breeder chickens after hatching and growing, and the second-generation breeder chickens produce third-generation eggs.

[0057] The egg classification unit 2 can further classify the eggs. First, step S011 can be executed to obtain the optimal incubation parameters for each type of egg. Then, step S012 can be executed to group eggs from multiple categories that share the same optimal incubation parameters into the same category. This allows for combined incubation and reduces the use of the egg incubator 4.

[0058] The hatching monitoring and management unit 3 only performs small-batch test incubations. After obtaining the optimal parameters for the incubation conditions for each type of hatching egg, the hatching egg incubator 4 can then perform large-scale industrial incubation. Specifically, during the hatching process, the hatching egg incubator 4 first executes step S21 to place hatching eggs of the same type within the same hatching egg incubator. Next, step S22 can be executed to obtain the type of hatching eggs within the hatching egg incubator. Next, step S23 can be executed to obtain the optimal parameters for the incubation conditions for each type of hatching egg. Finally, step S24 can be executed to set the incubation conditions for each type of hatching egg in the hatching egg incubator according to the optimal parameters until the hatching of the hatching eggs is completed and the first generation of hatching chicks are obtained.

[0059] See also Figure 5 As shown, the hatching eggs are not completely the same, and different hatching eggs require different incubation conditions, so it is necessary to classify the hatching eggs. Due to factors such as the physiological state of the parent breeder chickens, the weight and size of the hatching eggs will be different, which directly affects the hatching and breeding status of the hatching eggs. Therefore, the types can be classified according to the weight and size of the hatching eggs. In practical applications, step S31 can be first executed to obtain the weight and aspect ratio of multiple first-generation hatching eggs. The aspect ratio in this solution is the ratio of the long diameter to the short diameter. Next, step S32 can be executed to obtain several weight intervals where the weight of the first-generation hatching eggs is concentrated according to the weight of the first-generation hatching eggs. Next, step S33 can be executed to obtain several aspect ratio intervals where the aspect ratio of the first-generation hatching eggs is concentrated according to the aspect ratio of the first-generation hatching eggs. Next, step S34 can be executed to combine several weight intervals and several aspect ratios to obtain the weight interval and aspect ratio interval of the hatching eggs under each category. Finally, step S35 may be executed to classify each egg according to the weight interval and the aspect ratio interval of the eggs in each category to obtain the category of each egg.

[0060] To supplement the implementation of steps S31 to S35, the source code for some functional modules is provided, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution is desensitized. The same applies below.

[0061]

[0062]

[0063]

[0064] This code classifies first-generation eggs and assigns each egg a category. First, it retrieves egg weight and aspect ratio data from a database or file. It then defines weight and aspect ratio intervals, which should be adjusted based on the actual data. Finally, it iterates over each egg, checking which interval its weight and aspect ratio fall within. The index of these two intervals is then combined to assign the egg a category.

[0065] See also Figure 6 As shown, temperature, humidity, and egg turning frequency are the three most important incubation conditions during hatching. However, since the suitable parameter range for each type of hatching condition is relatively wide, for example, a temperature setting between 33.5 and 39 degrees Celsius, this does not provide much reference value for guiding specific incubation work. To determine the optimal parameters for producing as many three-generation hatching eggs as possible, for each type of hatching egg, step S6 can be performed in the specific implementation process. First, step S61 can be performed to obtain the suitable parameter range for each type of hatching condition during hatching. Next, step S62 can be performed to divide the suitable parameter range for each type of hatching condition into multiple parameter subranges. Next, step S63 can be performed to combine the parameter subranges for each type of hatching condition to obtain several groups of parameter subrange combinations. Next, step S64 can be performed to obtain the hatching quality index of the first-generation hatching eggs corresponding to each combination of parameter subranges. Next, step S65 can be performed to select the combination of parameter subranges for the hatching condition that corresponds to the highest hatching quality index for the first-generation hatching eggs as the target parameter subrange combination for the hatching condition. Next, step S66 can be executed to adjust the parameter combination of each type of incubation condition within the combination of target parameter subranges of the incubation condition to obtain the optimal parameters for the incubation condition of each type. Finally, step S67 is executed to summarize and obtain the optimal parameters for the incubation condition of each type of hatching egg.

[0066] See also Figure 7As shown, the process of steps S61 to S66 may not yield the optimal parameters simply by dividing the parameter subintervals once. To approximate the optimal parameters for each type of incubation condition as closely as possible, step S661 can be performed to first divide the target parameter subinterval for each type of incubation condition into updated parameter subintervals for each type of incubation condition. Next, step S662 can be performed to combine the updated parameter subintervals for each type of incubation condition to obtain a plurality of updated parameter subinterval combinations for each type of incubation condition. Next, step S663 can be performed to obtain the hatching quality index of the first-generation hatching eggs corresponding to each combination of the updated parameter subintervals. Next, step S664 can be performed to select the parameter subinterval combination corresponding to the highest hatching quality index for the first-generation hatching eggs as the updated target parameter subinterval combination. Finally, step S665 can be performed to continuously update the target parameter subinterval combination for each type of incubation condition until the optimal parameters for each type of incubation condition are obtained. The optimal parameters are obtained through continuous iteration using a successive approximation approach.

[0067] To supplement the implementation of steps S661 to S665, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution is desensitized. The same applies below.

[0068]

[0069]

[0070]

[0071] This code primarily adjusts the incubation parameters for each egg type within the target parameter subranges to obtain the optimal parameters for each egg type. First, the incubation parameter ranges are defined, which should be adjusted based on actual data. Then, for each egg type, multiple parameter combinations are generated within these ranges and the corresponding hatch quality index is calculated for each combination. Finally, the parameter combinations are sorted by hatch quality index, and the combination with the highest hatch quality index is returned as the optimal parameter combination.

[0072] See also Figure 8As shown, since each update of the target parameter subinterval for each type of incubation condition requires the completion of one hatching egg, excessive iterations can result in an excessively long total test duration. If the target parameter subinterval is divided into a small number of equal parts, the total test duration will be longer. If the target parameter subinterval is divided into a large number of equal parts, the number of combinations of target parameter subintervals for each type of incubation condition will be excessive. To achieve this balance, the theoretically optimal number of equal parts for dividing the target parameter subinterval is the natural logarithm e. To approximate the natural logarithm e, during each continuous update of the target parameter subinterval combination for each type of incubation condition, step S661 can first be performed in step S6611 to evenly divide the target parameter subinterval for each type of incubation condition into two equal parts, each of which will be used as the updated parameter subinterval for each type of incubation condition. Next, step S6612 can be performed to evenly divide the target parameter subinterval for each type of incubation condition into three equal parts, each of which will be used as the updated parameter subinterval for each type of incubation condition. Finally, step S6613 can be executed to determine whether the continuous update of the combination of target parameter sub-intervals for each type of incubation condition has been completed. If not, the process returns to steps S6611 and S6612 to alternately divide the target parameter sub-interval for each type of incubation condition into three equal parts and then into two equal parts until the continuous update of the combination of target parameter sub-intervals for each type of incubation condition is completed. If so, the process ends. In the process, by alternating between bisection and trisection, the average number of equal parts of the target parameter sub-interval is 2.5, which is closer to the natural logarithm e, taking into account the total test time and test complexity.

[0073] See also Figure 9 As shown, after each update of the combination of parameter sub-intervals, since the parameter sub-interval is a range value, in order to calculate the hatching quality index of the corresponding first-generation breeding eggs, a representative value can be selected within the parameter sub-interval as the parameter value of the hatching conditions during the test hatching process. Specifically, step S6631 can be first executed for each group of updated parameter sub-intervals to obtain the median value of the parameter sub-interval of each type of hatching condition in the combination of updated parameter sub-intervals. Next, step S6632 can be executed to incubate the first-generation breeding eggs under the median value state of the parameter sub-interval of each type of hatching condition to obtain the hatching rate of the first-generation breeding eggs and the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching. Finally, step S6633 can be executed based on the hatching rate of the first-generation breeding eggs, the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching and the egg production prediction model to obtain the hatching quality index of the first-generation breeding eggs corresponding to each group of updated parameter sub-intervals.

[0074] To supplement the implementation of steps S6631 to S6633, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution has been desensitized. The same applies below.

[0075]

[0076]

[0077] This code snippet primarily calculates the hatch quality index for first-generation eggs based on each updated parameter subrange combination. First, the parameter ranges for the incubation conditions are defined, which should be adjusted based on actual data. Then, the incubation model is used to incubate first-generation eggs to determine hatchability and weight distribution. Finally, the prediction model is used to calculate the hatch quality index for the first-generation eggs based on the hatchability and weight distribution.

[0078] This process can gradually find the optimal parameter range for each type of incubation conditions within a large parameter range, and evaluate the impact of these parameter ranges on the hatching quality of the first generation of breeding eggs through a predictive model.

[0079] See also Figure 10 As shown, although continuously updating the target parameter sub-intervals of each type of incubation condition will continuously approach the optimal parameters, too many update iterations will prolong the test time, and with the marginal diminishing effect, although too many iterations continuously approach the optimal parameters, they no longer have real economic value. In order to terminate the update iteration in time, step S6651 can be executed to continuously determine whether the hatching quality index of the corresponding generation of breeding eggs has decreased compared to the hatching quality index of the first generation of breeding eggs generated in the previous refinement process during each continuous update of the target parameter sub-intervals of each type of incubation condition. If so, step S6652 can be executed next to use the median value of the target parameter sub-interval of each type of incubation condition in the previous update process as the optimal parameter for the incubation condition corresponding to each type. If not, continue to update.

[0080] To supplement the implementation of steps S6651 to S6652, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution has been desensitized. The same applies below.

[0081]

[0082]

[0083]

[0084]

[0085] This code snippet primarily retrieves the hatch quality index for first-generation eggs based on each updated set of parameter subranges. First, the parameter ranges for the incubation conditions are defined, which should be adjusted based on actual data. Then, the incubation model is used to incubate first-generation eggs to determine hatchability and weight distribution. Finally, the prediction model is used to determine the hatch quality index for the first-generation eggs based on the hatchability and weight distribution.

[0086] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and the part for the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be performed substantially in parallel, and they can sometimes also be performed in the opposite order, depending on the function involved.

[0087] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0088] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0089] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A fully monitored intelligent management method for hatching eggs, characterized in that: include, Obtain the weight of multiple breeder hens at the time of hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle; The weights of multiple breeder hens at hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle are input into the input layer and output layer of the egg production prediction model respectively, and the model is trained until convergence. Classify the first generation of hatching eggs and obtain the category of each hatching egg; For each category of first-generation breeding eggs, incubate the first-generation breeding eggs under each type of incubation condition parameter combination state to obtain the hatchability of the first-generation breeding eggs and the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching; The hatching quality index of the first-generation eggs is obtained based on the hatching rate of the first-generation eggs, the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching, and the egg production prediction model; the average number of three-generation breeding eggs corresponding to each breeding egg is obtained as the hatching quality index of the first-generation breeding eggs; For each type of egg, Obtaining the appropriate parameter range for each type of incubation condition during the incubation of hatching eggs, wherein the types of incubation conditions include temperature, humidity, and egg turning frequency, The suitable parameter range of each type of incubation condition is divided into multiple parameter sub-ranges. The parameter sub-intervals of each type of incubation condition are combined to obtain several groups of parameter sub-intervals of incubation conditions. Get the hatching quality index of the first generation of eggs corresponding to the combination of parameter sub-intervals of each set of incubation conditions, The combination of the parameter sub-intervals of the incubation conditions corresponding to the highest value of the hatching quality index of the first generation of hatching eggs is selected as the combination of the target parameter sub-intervals of the incubation conditions. adjusting the parameter combination of the incubation condition for each species within the combination of the target parameter subranges of the incubation condition to obtain the optimal parameters of the incubation condition for each species; The optimal parameters for each type of hatching egg under each type of incubation condition were summarized.

2. The method according to claim 1, characterized in that The step of classifying the first generation of hatching eggs to obtain the category of each hatching egg includes: Obtaining the weight and aspect ratio of multiple first-generation breeding eggs, wherein the aspect ratio is the ratio of the long diameter to the short diameter; According to the weight of the first generation of breeding eggs, several weight intervals where the weight of the first generation of breeding eggs is concentrated are obtained; According to the aspect ratio of the first generation of breeding eggs, several aspect ratio intervals where the aspect ratio of the first generation of breeding eggs is concentrated are obtained; Combining several weight intervals and several length-to-diameter ratios to obtain the weight interval and length-to-diameter ratio interval for each category of eggs; Each egg is classified according to the weight range and length-to-short-diameter ratio range of each category to obtain the category of each egg.

3. The method according to claim 1, characterized in that The step of adjusting the parameter combination of each type of incubation condition within the combination of target parameter subranges of the incubation condition to obtain the optimal parameters of each type of incubation condition, include, dividing the target parameter subinterval of each type of hatching condition into updated parameter subintervals of each type of hatching condition; Combining the updated parameter sub-intervals of each type of incubation condition to obtain a combination of several groups of updated parameter sub-intervals of each type of incubation condition; Obtaining the hatching quality index of the first generation of hatching eggs corresponding to each combination of updated parameter sub-intervals; Selecting the combination of parameter sub-intervals corresponding to the highest value of the hatching quality index of the first generation of hatching eggs as the combination of target parameter sub-intervals after update; The combination of the target parameter subranges for each type of hatching condition is continuously updated until the optimal parameters for each type of hatching condition are obtained.

4. The method according to claim 3, characterized in that The step of dividing the target parameter sub-interval of each type of incubation condition into the updated parameter sub-interval of each type of incubation condition, include, In the process of continuously updating the combination of target parameter sub-ranges for each type of incubation condition, The target parameter subinterval of the hatching condition of each species is evenly divided into two equal parts and the two subintervals are used as the updated parameter subintervals corresponding to the hatching condition of each species; In the next process of updating the combination of target parameter sub-intervals of each type of incubation condition, the target parameter sub-interval of each type of incubation condition is evenly divided into three equal parts and the three parts are used as the updated parameter sub-intervals corresponding to each type of incubation condition; The target parameter subintervals for each type of hatching condition are alternately divided into three equal parts and two equal parts until the continuous updating of the combination of the target parameter subintervals for each type of hatching condition is completed.

5. The method according to claim 3, characterized in that The step of obtaining the hatching quality index of the first generation of hatching eggs corresponding to the combination of each group of updated parameter sub-intervals includes: For each set of updated parameter subintervals, obtaining the median value of the parameter subinterval for each species of incubation condition in the combination of updated parameter subintervals; The first generation breeding eggs are incubated under the intermediate value state of the parameter sub-interval of each type of incubation condition to obtain the hatchability of the first generation breeding eggs and the weight distribution interval distribution of the first generation breeding chickens at the time of hatching; According to the hatching rate of first-generation breeding eggs, the weight distribution interval distribution of first-generation breeding chickens at the time of hatching and the egg production prediction model, the hatching quality index of first-generation breeding eggs corresponding to each group of updated parameter sub-intervals is obtained.

6. The method according to claim 5, characterized in that The step of continuously updating the combination of target parameter sub-intervals for each type of incubation condition until the optimal parameters for each type of incubation condition are obtained, include, During each continuous updating of the combination of target parameter sub-intervals for each type of incubation condition, it is continuously determined whether the hatching quality index of the corresponding first generation of hatching eggs has decreased compared to the hatching quality index of the first generation of hatching eggs generated in the previous refinement process; If so, the middle value of the target parameter subinterval of the hatching condition for each species in the last updating process is used as the optimal parameter of the hatching condition for each species; If not, continue to update.

7. A fully monitored intelligent management method for hatching eggs, characterized in that: include, Obtaining the optimal parameters for each type of hatching conditions for each category of hatching eggs in the intelligent hatching management method capable of comprehensive monitoring according to any one of claims 1 to 6; Group eggs from multiple categories that share the same optimal parameters for incubation conditions within each category into one category.

8. A fully monitored intelligent management method for hatching eggs, characterized in that: include, Place eggs of the same type in the same egg incubator; Obtaining the type of hatching eggs in the hatching egg incubator; Obtaining the optimal parameters for each type of hatching conditions for each category of hatching eggs in the intelligent hatching management method capable of comprehensive monitoring according to any one of claims 1 to 6; Set the incubation conditions for each type of egg incubator according to the optimal parameters.

9. An intelligent management system for hatching eggs capable of comprehensive monitoring, characterized in that: include, The model training unit is used to obtain the weight of multiple breeder chickens at the time of hatching and the total number of eggs laid by the corresponding breeder chickens during the laying cycle; The weights of multiple breeder hens at hatching and the total number of eggs laid by the corresponding breeder hens during the laying cycle are input into the input layer and output layer of the egg production prediction model respectively, and the model is trained until convergence. The hatching egg classification unit is used to classify the first generation of hatching eggs and obtain the category of each hatching egg; The hatching monitoring and management unit is used to, for each category of first-generation breeding eggs, incubate the first-generation breeding eggs under each type of parameter combination of incubation conditions, and obtain the hatching rate of the first-generation breeding eggs and the weight distribution interval distribution of the first-generation breeding chickens at the time of hatching; The hatching quality index of the first-generation eggs is obtained based on the hatching rate of the first-generation eggs, the weight distribution interval distribution of the first-generation breeder chickens at the time of hatching, and the egg production prediction model; the average number of three-generation breeding eggs corresponding to each breeding egg is obtained as the hatching quality index of the first-generation breeding eggs; For each type of egg, Obtaining the appropriate parameter range for each type of incubation condition during the incubation of hatching eggs, wherein the types of incubation conditions include temperature, humidity, and egg turning frequency, The suitable parameter range of each type of incubation condition is divided into multiple parameter sub-ranges. The parameter sub-intervals of each type of incubation condition are combined to obtain several groups of parameter sub-intervals of incubation conditions. Get the hatching quality index of the first generation of eggs corresponding to the combination of parameter sub-intervals of each set of incubation conditions, The combination of the parameter sub-intervals of the incubation conditions corresponding to the highest value of the hatching quality index of the first generation of hatching eggs is selected as the combination of the target parameter sub-intervals of the incubation conditions. adjusting the parameter combination of the incubation condition for each species within the combination of the target parameter subranges of the incubation condition to obtain the optimal parameters of the incubation condition for each species; Summarize and obtain the best parameters for each type of hatching eggs under each type of incubation conditions; A hatching egg incubator, used to place hatching eggs of the same type in the same hatching egg incubator; Obtaining the type of hatching eggs in the hatching egg incubator; Obtain the optimal parameters for each type of hatching egg under each type of incubation conditions; Set the incubation conditions for each type of egg incubator according to the optimal parameters.

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