An intelligent hatching prediction method, device and computer readable medium

Abstract

The application provides an intelligent hatching prediction method, device and computer readable medium, the method comprises the following steps: in response to an intelligent hatching prediction request, obtaining a feature sample of a target animal; inputting the feature sample of the target animal into a preset feature information analysis model to obtain feature data of the target animal; inputting the feature data of the target animal into a preset intelligent hatching prediction model to obtain an intelligent hatching prediction result of the target animal.

Description

Technical Field

[0001] This application relates to the field of poultry farming technology, and in particular to an intelligent hatching prediction method, device and computer-readable medium. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this application as set forth in the claims. The description herein is not to be construed as prior art simply because it is included in this section.

[0003] With the development of aquaculture technology, the variety of animal species is increasing. To better plan animal growth, it is necessary to predict hatching success rates and adjust breeding strategies accordingly. Currently, most animal hatching rates are predicted based on human experience.

[0004] In recent years, modern scientific breeding techniques have been continuously introduced and applied to the breeding industry, which has greatly promoted the healthy development of poultry breeding and met people's consumption needs for poultry. At the same time, the structure of poultry breeding has gradually shifted from family-run operations to large-scale, intensive, and professional operations. Relying on traditional manual forecasting methods can no longer meet the current needs of poultry breeding.

[0005] Therefore, there is an urgent need to propose an intelligent incubation prediction method that is highly integrated, accurate, and real-time. Summary of the Invention

[0006] This application provides a method, apparatus, and computer-readable storage medium for predicting the hatching outcome of animals.

[0007] One aspect of this application provides an intelligent incubation prediction method, comprising:

[0008] In response to the intelligent hatching prediction request, characteristic samples of the target animal are obtained;

[0009] The feature samples of the target animal are input into a preset feature information analysis model to obtain the feature data of the target animal;

[0010] The characteristic data of the target animal are input into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal.

[0011] Furthermore, in response to the intelligent hatching prediction request, obtaining feature samples of the target animal includes: in response to the intelligent hatching prediction request, obtaining sample data of the target animal and its mother; associating the sample data of the target animal and the sample data of the target animal's mother to generate an association information table; naming the association information table based on the identity code of the target animal or the identity code of the target animal's mother to obtain feature samples of the target animal.

[0012] Furthermore, in response to the intelligent hatching prediction request, obtaining feature samples of the target animal also includes:

[0013] In response to the intelligent hatching prediction request, the sample data of the target animal is acquired and cleaned to obtain the cleaned sample data;

[0014] The cleaned sample data is standardized and normalized to obtain processed sample data;

[0015] Based on the hot coding rule, the non-numerical sample data of the target animal is converted into binary sample data;

[0016] Based on the binary sample data and the processed sample data, characteristic samples of the target animal are obtained.

[0017] Furthermore, in response to the intelligent hatching prediction request, sample data of the target animal is acquired and cleaned to obtain cleaned sample data, including:

[0018] The integrity of the sample data of the target animal is checked. If there is missing sample data, the sample data is filled in based on the following formula:

[0019]

[0020] Where: x i Let y represent the i-th time in the time series. i Indicates the corresponding x i Data;

[0021] The abnormality of the sample data of the target animal is detected. If abnormal sample data is found, it is processed according to the abnormal data processing rules.

[0022] Furthermore, the cleaned sample data is standardized and normalized, including: standardizing the cleaned sample data based on the following formula:

[0023]

[0024] Where: x represents the sample data before standardization, mean(x) represents the mean of the sample data x; std(x) represents the standard deviation of the sample data x; the cleaned sample data is normalized based on the following formula:

[0025]

[0026] Where: x represents the sample data before normalization, min(x) represents the minimum value of sample data x, and max(x) represents the maximum value of sample data x.

[0027] Furthermore, the method further includes: generating a preset feature information analysis model; generating the preset feature information analysis model includes: responding to an intelligent hatching prediction request, the intelligent hatching prediction request including an intelligent hatching prediction target; based on the intelligent hatching prediction target, determining the correlation between sample data and the intelligent hatching prediction target according to a correlation analysis model to obtain a first analysis result; based on the intelligent hatching prediction target, determining the correlation between sample data and the intelligent hatching prediction target according to a random forest model to obtain a second analysis result; and based on the first analysis result and the second analysis result, acquiring feature data of the target animal.

[0028] Furthermore, the method also includes: generating a preset intelligent incubation prediction model; generating the preset intelligent incubation prediction model includes: acquiring multiple sets of training sample sets; dividing each set of training sample sets into two subsets of training samples based on preset values; and determining the information entropy of any subset of training samples in each set of training sample sets based on the following formula:

[0029]

[0030] Where i represents any training subset, p i This represents the proportion of sample data in any training subset; the information gain of each training sample set is determined based on the following formula:

[0031]

[0032] Where |D| represents the total number of sample data, |D i | represents the number of samples in any subset of training samples in any set of training samples; based on the information entropy of each set of training samples, a preset intelligent incubation prediction model is generated.

[0033] Another aspect of this application provides an intelligent incubation prediction device, the device comprising:

[0034] A response unit is used to respond to intelligent hatching prediction requests and obtain feature samples of the target animal;

[0035] The first acquisition unit is used to input the feature sample of the target animal into a preset feature information analysis model to acquire the feature data of the target animal.

[0036] The second acquisition unit is used to input the characteristic data of the target animal into a preset intelligent incubation prediction model to obtain the intelligent incubation prediction result of the target animal.

[0037] In another aspect, this application provides an electronic device, the device comprising:

[0038] At least one processor; and

[0039] A memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the intelligent incubation prediction method as described above.

[0041] In another aspect, this application provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor to implement the intelligent hatching prediction method.

[0042] In the solution provided in this application embodiment, a feature sample of the target animal is obtained in response to an intelligent hatching prediction request; the feature sample of the target animal is input into a preset feature information analysis model to obtain feature data of the target animal; the feature data of the target animal is input into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal. Based on the above intelligent hatching prediction method, through big data models, it can help breeders better control the hatching process of animals and improve the survival rate of animals. It can also help detect potential health problems in animals, such as diseases and developmental abnormalities, early, so that breeders can take timely measures to reduce unknown risks. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 A flowchart illustrating an embodiment of the intelligent incubation prediction method provided in this application;

[0046] Figure 2 This is a schematic diagram of the structure of an intelligent incubation prediction device provided in another embodiment of this application;

[0047] Figure 3 A schematic diagram of the structure of an electronic device suitable for implementing the solutions in the embodiments of this application.

[0048] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] In a typical configuration of this application, the terminal and the service network devices each include one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0051] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0052] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer program instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0053] This application provides an intelligent incubation prediction method, including:

[0054] In response to the intelligent hatching prediction request, characteristic samples of the target animal are obtained;

[0055] The feature samples of the target animal are input into a preset feature information analysis model to obtain the feature data of the target animal;

[0056] The characteristic data of the target animal are input into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal.

[0057] In practical scenarios, the execution entity of this method can be a user device, or a device composed of a user device and a network device integrated through a network, or an application running on the aforementioned device. The user device includes, but is not limited to, various terminal devices such as computers, mobile phones, tablets, smartwatches, and wristbands. The network device includes, but is not limited to, network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets. Here, the cloud consists of a large number of hosts or network servers based on cloud computing. Cloud computing is a type of distributed computing, consisting of a virtual computer composed of a group of loosely coupled computer sets.

[0058] Example 1

[0059] This application provides a model generation method to generate a feature information analysis model for acquiring feature data of a target animal. The method includes:

[0060] In response to a smart hatching prediction request, the smart hatching prediction request includes a smart hatching prediction target; wherein, the smart hatching prediction type includes, but is not limited to, the hatching rate, survival rate, growth rate, and health status of animals;

[0061] Based on the intelligent incubation prediction target, the correlation between the sample data and the intelligent incubation prediction target is determined according to the correlation analysis model, and the first analysis result is obtained.

[0062] Based on the intelligent incubation prediction target, the correlation between the sample data and the intelligent incubation prediction target is determined according to the random forest model to obtain the second analysis result;

[0063] Based on the results of the first and second analyses, characteristic data of the target animal were obtained.

[0064] In one embodiment, the determination of animal characteristic data related to hatchability is illustrated as an example. The raw animal characteristic data includes: Hen data: Weight: 2000 g; Body Size: Medium; Age: 24 months; Feed Intake: 100 g; Genotype: AA; SNP (Single Nucleotide Polymorphism): rs1234567A / G. Egg data: Egg Size: 5.5 cm; Egg Weight: 50 g; Egg Shape: Oval; Egg Color: White; Protein Content: 12 g / 100 g egg; Fat Content: 10 g / 100 g egg; Carbohydrate Content: 5 g / 100 g egg.

[0065] Using a correlation model, the first analytical result regarding the relationship between the above sample data and the hatching rate is obtained:

[0066] Correlation coefficient between body weight and hatchability: 0.8;

[0067] Correlation coefficient between age and hatching rate: 0.7;

[0068] Correlation coefficient between egg size and hatchability: 0.6;

[0069] The correlation coefficient between egg weight and hatchability is 0.75.

[0070] Correlation coefficient between protein content and hatchability: 0.65;

[0071] Correlation coefficient between fat content and hatchability: 0.55;

[0072] The correlation coefficient between carbohydrate content and hatching rate is 0.45.

[0073] Using the random forest model, a second analytical result was obtained regarding the relationship between the above sample data and the hatching rate:

[0074] Importance score for weight: 0.35;

[0075] Importance score for age: 0.28;

[0076] Importance score of egg size: 0.25;

[0077] Importance score of egg weight: 0.22;

[0078] Importance score for protein content: 0.18;

[0079] Importance score for fat content: 0.15;

[0080] Importance score for Carbohydrate Content: 0.1.

[0081] Specifically, weight has a high correlation with hatching rate (0.8) and also has a high importance score in the random forest model (0.35).

[0082] Age also showed a high correlation with hatching rate (0.7), with an importance score of (0.28) in the random forest model.

[0083] Egg size had a high correlation with hatchability (0.6) and an importance score of (0.25) in the random forest model.

[0084] Based on the results of the first and second analyses, the characteristic data related to hatchability were determined to include: hen weight, hen age, and egg size.

[0085] Similarly, based on the above methods, the characteristic data related to growth rate are determined to include: hen weight, hen age, and hen feed intake; the characteristic data related to survival rate are determined to include: egg weight, egg protein content, and hen gene data; and the important characteristics related to the health attributes of the target animal are determined to include: hen weight, egg protein content, and hen gene data.

[0086] Example 2

[0087] This application provides a model generation method to generate an intelligent hatching prediction model for obtaining intelligent hatching prediction results of a target animal. The intelligent hatching prediction results include, but are not limited to, the animal's hatching rate, survival rate, growth rate, and health status. The method includes:

[0088] Acquire feature data of the target animal and generate multiple training sample sets;

[0089] Each training sample set is divided into two training sample subsets based on preset values;

[0090] The information entropy of any subset of training samples in each training sample set is determined based on the following formula:

[0091]

[0092] Where i represents any training subset, p i This represents the proportion of sample data in any training subset;

[0093] The information gain for each training sample set is determined based on the following formula:

[0094]

[0095] Where |D| represents the total number of sample data, |D i | represents the number of samples in any subset of training samples in any set of training samples;

[0096] Based on the information entropy of each training sample set, a pre-defined intelligent incubation prediction model is generated.

[0097] In one implementation, using hatchability-related feature data as an example, the training sample set includes three sets: hen weight, hen age, and egg size. Each training sample set includes a high hatchability subset and a low hatchability subset; wherein, high hatchability (80 samples) and low hatchability (20 samples) are defined. The information entropy of any training sample subset within each training sample set is determined based on the following formula:

[0098] Entroopy(D) = -(p high i log2(p high i )+p 1ow i log2(p low i ))

[0099] Where i represents any training subset, p high i p represents the proportion of samples with the label "high" in any training subset. low iThis represents the proportion of sample data with the label "low" in any training subset; the information gain of each training sample set is determined based on the following formula:

[0100] Gain (Weight)

[0101] =Entropy(D)-(p high log2(p high )+p low log2(p low Entropy(D)

[0102] Where, p high p represents the proportion of samples with the label "high" in the training sample set. low This indicates the proportion of sample data with the label "low" in the training sample set.

[0103] Based on the hen's weight, age, and egg size, the following calculations were performed:

[0104]

[0105] Based on the hen's weight, age, and egg size, the information entropy is calculated as follows:

[0106]

[0107] Calculate the information gain for each feature:

[0108] Training sample set 1: weight of hens; total samples: 100.

[0109] Divide into two subsets based on weight:

[0110] Training sample subset 1: Weight greater than or equal to 1800 grams. The corresponding hatching rate data is as follows:

[0111] Number of samples with a "high" hatching rate: 60;

[0112] Number of samples with a "low" hatching rate: 10.

[0113] Training sample subset 2: Weight less than 1800 grams. The corresponding hatching rate data is as follows:

[0114] Number of samples with a "high" hatching rate: 20;

[0115] Number of samples with a "low" hatching rate: 10.

[0116] The information gain of training sample 1 was calculated to be approximately 0.2566.

[0117] Similarly, the information gain for the hen's age is 0.2065, and the information gain for the egg size is 0.

[0118] Calculate the information entropy of training sample subset 1:

[0119]

[0120] Calculate the information entropy of training sample subset 2:

[0121]

[0122] Calculate the information gain of weight characteristics:

[0123]

[0124] Similarly, the information gain for the age feature (Age) is calculated to be approximately 0.2065, and the information gain for the egg size feature (Egg Size) is approximately 0.0000.

[0125] Compare the information gain of the three training sample sets:

[0126] The information gain for the weight characteristic is approximately 0.2566;

[0127] The information gain for the age characteristic (Age) is approximately 0.2065;

[0128] The information gain for the egg size feature is approximately 0.0000.

[0129] Based on the information gain of the three sets of training samples, a preset intelligent hatching prediction model is generated to predict the hatching rate of the target animal. In one embodiment, the preset intelligent hatching prediction model is a decision tree. The nodes of the decision tree are generated based on the information gain of the three sets of training samples, with the weight feature set as the parent node of the age feature, and the age feature set as the parent node of the egg size feature.

[0130] Example 3

[0131] Figure 1 This paper illustrates the processing flow of an intelligent hatching prediction method provided in an embodiment of this application. Based on the feature information analysis model generated in Embodiment 1, and the intelligent hatching prediction model generated in Embodiment 2, the method predicts the intelligent hatching results of a target animal. The method includes at least the following processing steps:

[0132] Step S101: In response to the intelligent hatching prediction request, obtain feature samples of the target animal;

[0133] Step S102: Input the feature sample of the target animal into a preset feature information analysis model to obtain the feature data of the target animal;

[0134] Step S103: Input the feature data of the target animal into the preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal.

[0135] The intelligent hatching prediction method described in this embodiment integrates and correlates scattered data, and selects a suitable prediction model based on the characteristics of different indicators. The trained intelligent hatching prediction model is used to predict hatching rates, growth rates, survival rates, and health conditions, and to monitor the status of target animals in real time, providing early warnings and suggestions to breeders.

[0136] It should be understood that this embodiment uses a chick as an example to illustrate the intelligent hatching prediction method described in this application.

[0137] In one embodiment, step S101 of obtaining the feature samples of the target animal includes: obtaining sample data of eggs and sample data of hens (i.e., the mother of the eggs); generating information codes corresponding to the eggs and hens respectively; associating the sample data of eggs and sample data of hens to generate an association information table; naming the association information table based on the information code of the eggs, or the identity code of the hens, to generate the feature samples of the eggs.

[0138] Specifically, wearable devices can be used to collect sample data from hens, including weight, body size, age, and feed intake. In practical applications, a hen's weight can be measured in grams (g); for example, a hen weighing 2000 grams. A hen's body size can be categorized, such as small, medium, or large; for example, a hen of medium size. A hen's age can be measured in months; for example, a hen 24 months old. The amount of feed a hen consumes daily can be measured in grams (g); for example, a hen consuming 100 grams of feed per day.

[0139] The sample data for hens also includes their genetic data, obtained through gene sequencing, which describes the hen's genomic composition. Genetic data is typically presented as a DNA sequence, consisting of a series of base pairs (A, T, C, G). The base pair sequence in the genetic data determines the hen's genetic characteristics, such as color, body size, and disease resistance. For example, genotype describes different variants of a gene; for instance, a gene may have two common genotypes, represented by letters (e.g., AA and Aa). Gene frequency describes the frequency of a particular genotype in the entire population. SNP (Single Nucleotide Polymorphism) describes variations in a single base pair within a gene. rs1234567A / G indicates that there are two possible base pairs (A and G) at this locus. Gene function describes the function of the protein encoded by the gene, or the association between the gene and a specific trait.

[0140] Egg sample data includes collecting phenotypic data (such as size, weight, shape, color, etc.) and compositional data. Egg size includes length, typically the distance from the longest end to the other (e.g., 5.5 cm, 6.0 cm, 6.5 cm, etc.). Egg shape includes oval, round, etc. Eggshell color includes white, brown, etc.

[0141] Specifically, the composition data of eggs includes protein content, fat content, carbohydrate content, etc. The composition data of eggs mentioned in this application refers to the percentage of each component per 100g unit.

[0142] In one implementation, information codes are generated for eggs and hens respectively, and sample data of eggs and sample data of hens are associated to generate an association information table, including: naming the association information table based on the information code of the eggs, or the identity code of the hens, and generating feature samples of eggs.

[0143] Specifically, the information encoding corresponding to eggs and hens includes: after the hen lays an egg, the egg is marked by spraying an invisible agent, and a unique ID is obtained by irradiating the egg with ultraviolet light. For example: ID-001, ID-002, ID-003, etc.

[0144] Specifically, the sample data of eggs and the sample data of hens are linked to generate a correlation information table; this includes: creating a hen table (Chickens): storing phenotypic data of hens, including weight, body size, age, feed intake, etc.; creating a gene table (Genes): storing gene data, including genotype, gene frequency, SNP, etc.; an egg table (Eggs); creating an egg phenotypic data table: storing phenotypic data of eggs, including size, weight, shape, color, etc.; creating an egg composition table (Nutrients): storing egg composition data, including protein content, fat content, carbohydrate content, etc.; establishing the correlation: in the database, each table has a unique identifier as a primary key, such as hen ID, egg ID, etc. In the Genes and Eggs tables, the hen ID can be used as a foreign key to link these two tables with the hen table. Similarly, in the Nutrients table, the egg ID can be used as a foreign key to link the nutrient table with the egg table, creating a related information table. SQL queries can then be used to join these related information tables.

[0145] In one implementation, the associated information table is named based on the information code of the egg, or the identity code of the hen, to generate characteristic samples of the egg; so as to realize the connection query between the various associated information tables through the unique ID (the information code of the egg, or the identity code of the hen) to obtain complete information including the phenotypic data of the hen, genetic data, phenotypic data of the egg, and composition data.

[0146] In one embodiment, step S101, in response to the intelligent hatching prediction request, further includes: cleaning the sample data of the eggs to obtain cleaned sample data; standardizing and normalizing the cleaned sample data to obtain processed sample data; converting the non-numerical sample data into binary sample data based on hot coding rules; and obtaining the characteristic data of the target animal based on the binary sample data and the processed sample data.

[0147] In one implementation, data cleaning of the egg sample data includes: detecting and processing missing values; missing values ​​can affect the training and prediction of the model, and can be processed by deleting missing values, interpolation, etc.

[0148] Handling missing values ​​through deletion includes: if the number of missing values ​​is small and they do not affect the overall data distribution and pattern, the rows or columns containing the missing values ​​can be directly deleted. For example, given a dataset containing hen weights, egg size, and egg weights, if some egg weights are missing, the rows containing these missing weights can be deleted.

[0149] Filling missing values ​​using interpolation methods includes using interpolation techniques based on patterns in existing data, such as linear interpolation, polynomial interpolation, and KNN interpolation. For time series or ordered data, linear interpolation can be used to fill missing values.

[0150] Specifically, filling in missing values ​​using interpolation methods includes: checking the completeness of the egg sample data; if missing sample data exists, filling in the missing sample data based on the following formula:

[0151]

[0152] Where: x i Let y represent the i-th time in the time series. i Indicates the corresponding x i Data;

[0153] The abnormality of the egg sample data is detected. If abnormal sample data exists, the abnormal sample data is processed according to the abnormal data processing rules.

[0154] In one embodiment, the sample data of the eggs is standardized and normalized, including: standardizing the sample data of the eggs based on the following formula:

[0155]

[0156] Where: x represents the sample data before standardization, mean(x) represents the mean of the sample data x, and std(x) represents the standard deviation of the sample data x;

[0157] The sample data of the eggs are normalized based on the following formula:

[0158]

[0159] Where: x represents the sample data before normalization, min(x) represents the minimum value of sample data x, and max(x) represents the maximum value of sample data x.

[0160] Standardization produces processed sample data that represents the degree to which each sample deviates from the mean of each feature. A positive standardized value indicates that the sample's value for that feature is higher than the feature's mean; a negative value indicates that the sample's value for that feature is lower than the feature's mean.

[0161] Taking an egg weighing 150g (i.e., x = 150) as an example, normalizing the weight of the egg to the range [0, 1], the minimum value min(x) in the original dataset is 100g, and the maximum value max(x) is 200g. Then, the calculated values ​​are...

[0162]

[0163] That is, the normalized value of an egg weighing 150g is 0.5.

[0164] Taking egg composition data as an example (protein content: 20g; fat content: 10g; carbohydrate content: 5g; protein content: 15g; fat content: 8g), after standardizing the above data, the following calculations are obtained:

[0165]

[0166] in,

[0167]

[0168] std(carbohydrate) = 0g.

[0169] Taking protein content as an example, the standardized value of 20 grams of protein content is 1, indicating that the protein content of this sample is one standard deviation higher than the mean; the standardized value of 15 grams of protein content is -1, indicating that the protein content of this sample is one standard deviation lower than the mean. Standardizing and normalizing the sample data of the target animal helps to compare data with different features and makes it easier to observe the relative relationships between different features, which is more beneficial to the convergence speed and learning performance of algorithms such as machine learning.

[0170] In one implementation, non-numerical sample data of the egg is converted into binary sample data based on a one-hot encoding rule. The non-numerical sample data includes the egg's size, color, shape, hen's size, etc. This embodiment uses egg color as an example to illustrate the conversion of non-numerical sample data into binary sample data; assuming the egg's color characteristic has possible values ​​such as "white" and "brown," we can use one-hot encoding to convert this characteristic into multiple binary features.

[0171] Specifically, each different color is first mapped to a unique integer code, such as: white: 0, brown: 1; then one-hot encoding is applied to each integer code to create a new binary feature; that is, the binary feature corresponding to the white color of the egg is [1,0,0], and the binary feature corresponding to the brown color of the egg is [0,1,0].

[0172] In one embodiment, in step S102, the feature sample of the target animal is input into a preset feature information analysis model to obtain the feature data of the target animal: based on the intelligent hatching prediction request, an intelligent hatching prediction target is obtained; based on the type of the intelligent hatching request, the feature data of the target animal is obtained.

[0173] Specifically, the intelligent hatching prediction targets include, but are not limited to, hatching rate, growth rate, survival rate, and health status. Specifically, when the intelligent hatching prediction target is hatching rate, the characteristic data of the target animal includes the hen's weight, age, and egg size; when the intelligent hatching prediction target is growth rate, the characteristic data of the target animal includes the hen's weight, age, and feed intake; when the intelligent hatching prediction target is survival rate, the characteristic data of the target animal includes egg weight, egg protein content, and hen genetic data; when the intelligent hatching prediction target is health status, the characteristic data of the target animal includes the hen's weight, egg protein content, and hen genetic data.

[0174] In one embodiment, step S103 inputs the feature data of the target animal into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal, which includes: obtaining the intelligent hatching prediction result of the target animal based on the feature data of the target animal, wherein the intelligent hatching prediction result corresponds to the intelligent hatching prediction target, including but not limited to hatching rate, survival rate, growth rate and health status.

[0175] In one embodiment, the characteristic data of the target animal is input into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal.

[0176] Using the following data as an example, obtain the predicted hatching rate of chicks:

[0177] The characteristics of the hen and eggs include: hen weight: 1800 grams; hen age: 20 months; egg size: 5 centimeters.

[0178] The above feature data is input into a preset feature information analysis model. The first feature is the hen's weight. If the weight is less than or equal to 2000 grams, proceed to the next feature. If the weight is greater than 2000 grams, the hatchability is directly judged as "high". The second feature is the hen's age. If the age is less than or equal to 24 months, proceed to the next feature. If the age is greater than 24 months, the hatchability is directly judged as "low". The third feature is egg size. If the egg size is less than or equal to 5.5 cm, the hatchability is judged as "high". If the egg size is greater than 5.5 cm, the hatchability is judged as "low". Specifically, the above hatchability prediction process includes: the hen's weight is 1800 grams, less than 2000 grams, proceeding to the next feature; the hen's age is 20 months, less than 24 months, proceeding to the next feature; the egg size is 5 cm, less than or equal to 5.5 cm, so the final predicted hatchability for this set of data is "high".

[0179] In one implementation, a set of sample data from hens and eggs is used to predict the survival rate of chicks. Specifically, the egg weight is 45 grams; the egg protein content is 11 grams per 100 grams of egg; and the hen's gene data is AA. Based on a preset feature information analysis model, the target animal's feature data is obtained, including egg weight, egg protein content, and hen gene data as important features related to survival rate. After obtaining the target animal's feature data, based on a preset intelligent hatching prediction model, the survival rate prediction result of the target animal is obtained. The specific steps include: the first feature is egg weight. If the egg weight is less than or equal to 50 grams, proceed to the next feature. If the egg weight is greater than 50 grams, the survival rate is directly judged as "high". Proceed to the second feature, egg protein content: if the protein content is less than or equal to 10 grams per 100 grams of egg, the survival rate is judged as "low". If the protein content is greater than 10 grams per 100 grams of egg, proceed to the next characteristic. The third characteristic is the hen's genetic data: if the genetic data is AA, the survival rate is judged as "high". If the genetic data is other, the survival rate is judged as "low".

[0180] In one implementation, a sample of hen data is used to predict the growth rate of chicks. The data is as follows: hen weight: 2000 grams; hen age: 28 months; hen feed intake: 120 grams. Based on a preset feature information analysis model, the target animal's feature data, including hen weight, hen age, and hen feed intake, are obtained. According to a preset intelligent hatching prediction model, the growth rate prediction result of the target animal is obtained. The specific prediction process includes: the first feature is the hen's weight. If the weight is less than or equal to 2000 grams, proceed to the next feature. If the weight is greater than 2000 grams, the growth rate is directly judged as "fast". Proceed to the second feature, hen age: if the age is less than or equal to 24 months, proceed to the next feature. If the age is greater than 24 months, the growth rate is directly judged as "slow". The third characteristic is the hen's feed intake: if the feed intake is less than or equal to 100 grams, the growth rate is judged as "fast". If the feed intake is greater than 100 grams, the growth rate is judged as "slow".

[0181] In one implementation, a set of sample data from hens and eggs is used to predict the health status of chicks. The data is as follows: Hen weight: 1800 grams; Egg protein content: 11 grams / 100 grams of egg; Hen gene data: AG. Based on a preset intelligent hatching prediction model, the health status prediction result of the target animal is obtained. The specific prediction process includes: The first feature is the hen's weight. If the weight is less than or equal to 2000 grams, proceed to the next feature. If the weight is greater than 2000 grams, the health status is directly judged as "good". If the protein content is less than or equal to 10 grams / 100 grams of egg, the health status is judged as "fair". If the protein content is greater than 10 grams / 100 grams of egg, proceed to the next feature. If the gene data is AA, the health status is judged as "good". If the gene data is otherwise, the health status is judged as "fair".

[0182] Example 4

[0183] This application also provides an intelligent incubation prediction device, the structure of which is as follows: Figure 2 As shown, the device includes:

[0184] A response unit is used to respond to a smart hatching prediction request and obtain a feature sample of the target animal, wherein the smart hatching prediction request includes a smart hatching prediction target.

[0185] The first acquisition unit is used to input the feature sample of the target animal into a preset feature information analysis model to acquire the feature data of the target animal.

[0186] The second acquisition unit is used to input the characteristic data of the target animal into a preset intelligent incubation prediction model to obtain the intelligent incubation prediction result of the target animal.

[0187] Example 5

[0188] Based on the same inventive concept, this application also provides an electronic device. The method corresponding to the electronic device can be the intelligent incubation prediction method in the foregoing embodiments, and its problem-solving principle is similar to that method. The electronic device provided in this application includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the methods and / or technical solutions of the foregoing embodiments of this application.

[0189] The electronic device may be a user device, or a device formed by integrating user devices and network devices through a network, or it may be an application running on the aforementioned devices. The user device includes, but is not limited to, various terminal devices such as computers, mobile phones, tablets, smartwatches, and wristbands. The network device includes, but is not limited to, network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets. Here, the cloud consists of a large number of hosts or network servers based on cloud computing. Cloud computing is a type of distributed computing, consisting of a virtual computer composed of a group of loosely coupled computer sets.

[0190] Figure 3 The diagram illustrates the structure of an apparatus suitable for implementing the methods and / or technical solutions in the embodiments of this application. The apparatus 300 includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage portion 308 into a Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0191] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, touchscreen, microphone, infrared sensor, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), LED display, OLED display, etc., and speakers, etc.; a storage section 308 including one or more computer-readable media such as hard disk, optical disk, magnetic disk, semiconductor memory, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet.

[0192] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by the central processing unit (CPU) 301, it performs the functions defined in the methods of this application.

[0193] Example 6

[0194] Another embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application described above.

[0195] Specifically, this embodiment may employ any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0196] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0197] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0198] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0199] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or page components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0202] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0204] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0206] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. An intelligent incubation prediction method, wherein, The method includes: In response to a smart hatching prediction request, a feature sample of the target animal is obtained, the feature sample of the target animal including sample data of the egg and sample data of the mother of the egg, the egg being used to hatch the target animal; The feature samples are input into a preset feature information analysis model to obtain feature data of the target animal, and the feature data is related to the intelligent hatching prediction target. The acquired feature data is input into a preset intelligent incubation prediction model to obtain the intelligent incubation prediction result of the target animal; The process involves inputting the feature samples into a preset feature information analysis model to obtain feature data of the target animal. The correlation between this feature data and the intelligent hatching prediction target includes: determining the correlation between the feature samples and the intelligent hatching prediction target based on a correlation analysis model, obtaining a first analysis result; determining the importance score between the feature samples and the intelligent hatching prediction target based on a random forest model, obtaining a second analysis result; and determining feature data related to the intelligent hatching prediction target based on the first and second analysis results. This feature data related to the intelligent hatching prediction target is the feature data of the target animal obtained. The preset intelligent incubation prediction model is a decision tree. The node splitting order of the decision tree is determined based on the information gain of each training sample set, and the preset intelligent incubation prediction model is generated. The intelligent hatching prediction targets include the hatching rate, growth rate, survival rate, and health status of the target animal. The intelligent hatching prediction results correspond to the intelligent hatching prediction targets, including the hatching rate, survival rate, growth rate, and health status of the target animal.

2. The intelligent incubation prediction method according to claim 1, wherein, In response to the intelligent hatching prediction request, characteristic samples of the target animal are obtained, including: The sample data of the egg and the sample data of the mother egg are associated to generate an association information table; the association information table is named based on the information code of the egg or the identity code of the mother egg to generate a feature sample of the target animal.

3. The intelligent incubation prediction method according to claim 2, wherein, In response to the intelligent hatching prediction request, the acquisition of feature samples of the target animal also includes: In response to the intelligent hatching prediction request, the sample data of the target animal is acquired and cleaned to obtain the cleaned sample data; The cleaned sample data is standardized and normalized to obtain processed sample data; Based on the hot coding rule, the non-numerical sample data of the target animal is converted into binary sample data; Based on the binary sample data and the processed sample data, characteristic samples of the target animal are obtained.

4. The intelligent incubation prediction method according to claim 3, wherein, In response to the intelligent hatching prediction request, sample data of the target animal is acquired and cleaned to obtain the cleaned sample data, including: The integrity of the sample data of the target animal is checked. If there is missing sample data, the sample data is filled in based on the following formula: ； in: This represents the filled sample data, x. i-1 Let y represent the (i-1)th time in the time series. i-1 Indicates the corresponding x i-1 Data, x i Let y represent the i-th time in the time series. i Indicates the corresponding x i Data; The abnormality of the sample data of the target animal is detected. If abnormal sample data is found, it is processed according to the abnormal data processing rules.

5. The intelligent incubation prediction method according to claim 3, wherein, The cleaned sample data is then standardized and normalized, including: The cleaned sample data is standardized based on the following formula: ； Where: x represents the sample data before standardization, mean(x) represents the mean of the sample data x, x1 represents the sample data after standardization, and std(x) represents the standard deviation of the sample data x; The cleaned sample data is normalized based on the following formula: ； Where: x represents the sample data before normalization, min(x) represents the minimum value of sample data x, x2 represents the sample data after normalization, and max(x) represents the maximum value of sample data x.

6. The intelligent incubation prediction method according to claim 5, wherein, The method further includes: generating the preset intelligent incubation prediction model, including: Obtain multiple sets of training samples; Each training sample set is divided into two training sample subsets based on preset values; The information entropy of any subset of training samples in each training sample set is determined based on the following formula: ； Where i represents any subset of training samples. This represents the proportion of sample data in any training sample subset. Let represent the information entropy of any subset of training samples in each training sample set, and n represent the total number of training sample subsets. The information gain for each training sample set is determined based on the following formula: ； Where |D| represents the total number of sample data, and |Di| represents the number of samples in any subset of any training sample set. Let represent the information entropy of any subset of training samples in each training sample set, n represent the total number of training sample subsets, and i represent any subset of training samples; Based on the information gain of each training sample set, the preset intelligent incubation prediction model is generated.

7. An intelligent incubation prediction device, wherein, The device includes: A response unit is used to respond to an intelligent hatching prediction request and obtain a feature sample of the target animal, the feature sample of the target animal including sample data of the egg and sample data of the mother of the egg, the egg being used to hatch the target animal; The first acquisition unit is used to input the feature sample of the target animal into a preset feature information analysis model to acquire the feature data of the target animal, wherein the feature data is related to the intelligent hatching prediction target. The second acquisition unit is used to input the characteristic data of the target animal into a preset intelligent hatching prediction model to obtain the intelligent hatching prediction result of the target animal. The process of inputting feature samples of the target animal into a preset feature information analysis model to obtain feature data of the target animal includes: determining the correlation between the feature samples and the intelligent hatching prediction target based on a correlation analysis model, and obtaining a first analysis result; determining the importance score between the feature samples and the intelligent hatching prediction target based on a random forest model, and obtaining a second analysis result; and determining feature data related to the intelligent hatching prediction target based on the first and second analysis results, wherein the feature data is the feature data of the target animal obtained. The intelligent incubation prediction model is a decision tree. The node splitting order of the decision tree is determined based on the information gain of each group of training sample sets to generate the intelligent incubation prediction model. The intelligent hatching prediction targets include the hatching rate, growth rate, survival rate, and health status of the target animal. The intelligent hatching prediction results correspond to the intelligent hatching prediction targets, including the hatching rate, survival rate, growth rate, and health status of the target animal.

8. An electronic device, the electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A computer-readable medium having stored thereon computer program instructions that can be executed by a processor to implement the method as described in any one of claims 1 to 6.

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