A method, system, device and terminal for correcting sow growth performance measurement data

Abstract

The present application belongs to the technical field of automatic growth performance determination of pigs, and discloses a kind of sow growth performance determination data correction method, system, equipment and terminal, obtain determination data and are summarized to the determination data table with every pig unit, the determination data table of every pig is matched with the birth information of sow population, obtain the day age, sex, variety information corresponding to every pig;The most suitable intersection between the set formed by calculating each pig during the measurement period (MP) is determined, to determine the most individuals in the whole sow population Correction measurement period;For the missing value of daily weight data and daily feed intake data of each pig in the correction measurement period, fill in the correction, calculate the average daily gain (ADG) and feed conversion ratio (FCR) of each pig in the correction measurement period, calculate the change of body weight of each pig with increasing age in the correction measurement period, and draw the growth curve graph of each pig in the correction measurement period.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automatic growth performance determination of pigs, and particularly relates to a method, system, device and terminal for correcting growth performance determination data of breeding pigs. BACKGROUND

[0002] The state attaches great importance to the revitalization of the seed industry, especially in the pig breeding field. The growth performance of breeding pigs is crucial to the offspring commodity group. Therefore, it is urgent to cultivate breeds with excellent traits, and first of all, reliable and accurate phenotype data are needed to cultivate excellent breeds that meet the production needs of our country. Therefore, it is particularly important to determine the growth performance of breeding pigs to obtain a large amount of phenotype data. This determination is carried out under specific standardized feeding conditions and management levels. By recording the daily feed intake and body weight change data of each pig, the change of growth performance indicators of breeding pigs within a certain period of time is obtained. This provides necessary growth trait phenotype data support for individual livestock genetic evaluation and population genetic parameter estimation, helps pig farmers improve feeding management level, improves genetic improvement efficiency, and fully develops the genetic potential of high-quality breeding pigs. In addition, for consumers, these data also provide useful reference information for purchasing breeding pigs.

[0003] However, the growth performance determination of breeding pigs is a long-term and continuous recording process, especially when determining a large-scale group, the workload will increase significantly. The traditional manual determination method is not only time-consuming and labor-intensive, but also difficult to ensure accuracy. Therefore, the introduction of an automatic growth performance determination system can significantly reduce the cost of manpower, material resources and time required for determination. The measurer only needs to log in to the determination system terminal to view the feed intake and body weight data of each pig under group feeding conditions, thereby greatly improving the accuracy and efficiency of performance determination.

[0004] In general, piglets enter the determination stage after the end of the nursery period, and the body weight of the pig to be determined is required to be between 20kg-30kg, and the starting age is between 60-80 days. However, in actual production practice, it is difficult to ensure that the pigs to be determined are in the same growth stage, and the body weight, starting age and ending age of the pigs to be determined in the group to be determined are inconsistent, so it is impossible to compare the average daily gain, feed intake and other indicators at the same growth stage. To solve this problem, it is crucial to correct the determination data. The traditional correction method uses a correction formula for correction, but the effect of the correction formula is not ideal, and the error is large. In addition, the parameters of these correction formulas were estimated decades ago, and there is a large difference with the modern performance of breeding pigs. Therefore, directly applying the automatic growth performance determination data without correction will directly affect the accuracy of the evaluation of the determined traits, and then affect the efficiency of breeding and reduce the speed of breeding pig improvement.

[0005] Through the above analysis, the problems and defects of the prior art are:

[0006] (1)There is no effective correction method for automatic growth performance measurement data, and the measurement date and the measurement date are not corrected, which leads to the fact that the growth performance data of each pig cannot be effectively compared. This limits the overall performance evaluation and comparison of the breeding pig population, and affects the accuracy and efficiency of breeding work.

[0007] (2)At present, the growth performance measurement data is corrected by relying on traditional correction formula, however, the effect of these formulas is not good, which leads to large error in the analysis result. Such error will affect the accuracy of selecting excellent breeding pigs, and hinder the selection and breeding of truly excellent performance of reserve breeding pigs for breeding work. SUMMARY

[0008] In view of the problems existing in the prior art, the present application provides a breeding pig growth performance measurement data correction method, system, device and terminal.

[0009] The present application is implemented in the following way, a breeding pig growth performance measurement data correction method comprises:

[0010] Obtain measurement data and summarize it into a measurement data table in units of each pig, match the measurement data table of each pig with the birth information of the breeding pig population to obtain the corresponding age, gender, breed and other information of each pig; determine the correction measurement period covering the most individuals in the whole breeding pig population by calculating the optimal intersection between the sets formed by the measurement period (MP) of each pig; fill in and correct the missing values of the daily weight data and daily feed intake data of each pig in the correction measurement period, calculate the average daily gain (ADG) and feed conversion ratio (FCR) of each pig in the correction measurement period, calculate the change of the body weight of each pig with age in the correction measurement period, and draw the growth curve graph of each pig in the correction measurement period.

[0011] Further, the specific steps of the breeding pig growth performance measurement data correction method comprise:

[0012] Step one, download all continuous measurement data in the measurement period from the breeding pig growth performance measurement system;

[0013] Step two, summarize all measurement records of each pig on different measurement dates into an electronic table, and take the individual number of the pig as the electronic table name; for data without individual number, directly delete it;

[0014] Step three, match the measurement data table of each pig with the birth information, and add breed, gender, birth date and age column in the table;

[0015] Step four, average the multiple weight records of each pig at each measured age to obtain the weight of the pig at the measured age, and sum the multiple feed records of each pig at each measured age to obtain the daily feed intake of the pig at the measured age;

[0016] Step five, perform the correction during the measured age, and statistically analyze the entry measured age and the exit measured age of the entire pig measured group and the measured period, and screen and retain the individuals that meet the condition: Mean MP -SD MP ≤MP≤Mean MP +SD MP , and then use the method of loop iteration to calculate the measured interval covering the largest number of individuals in the entire group;

[0017] Step six, use the Logistic regression model to perform model fitting of the weight records of each pig with respect to age, and predict the weight at each age; and fill in the missing values of the weight records of each pig in the correction measured period with the corresponding predicted weight at the age obtained by the Logistic regression model;

[0018] Step seven, sum all the feed records of each pig at each age in the correction measured period, calculate the total feed intake and the average daily feed intake of the pig with measured records in the correction measured period, and use the average daily feed intake to fill in the missing values of the daily feed intake records in the correction measured period;

[0019] Step eight, calculate the multiple growth data of each pig in the correction measured period obtained in step seven, calculate the average daily gain and the feed conversion ratio of each pig in the correction measured period, and record them in the electronic table;

[0020] Step nine, plot the correction growth data of each pig obtained in step six, with the correction measured period as the horizontal coordinate and the weight value corresponding to each age as the vertical coordinate, to draw the growth curve of the pig.

[0021] Further, in the step one, all the csv format data generated in the measured period is downloaded, and the csv format data contains all the growth performance data records of each pig per day in the measured period;

[0022] In the step two, the records of the same pig distributed in different measured date tables are summarized to obtain all the measured records of each pig in its entire measured period;

[0023] In the step three, the measured records of each pig are matched with the birth information of the pig, and then the growth rule of each pig at different ages is observed for comparison between breeds or within a breed.

[0024] In the fourth step, all the test records of the whole test period are unified from the aspects of body weight and total daily feed intake of different ages, for correction later.

[0025] Further, in the fifth step, the test age interval of the whole test population is corrected, the corrected test period covering the largest number of individuals is calculated, the minimum value of the corrected test period is taken as the test entry age, and the maximum value is taken as the test exit age; comparison of growth performance of pigs at different growth stages in the same corrected test period is realized.

[0026] In the sixth step, the daily age and body weight of each pig are modeled by Logistic method to calculate the predicted body weight of each pig at the corrected test period; the body weight corresponding to the age is filled in the missing body weight at the corresponding age in the corrected test period by Logistic method.

[0027] In the seventh step, the total feed intake and average daily feed intake of each pig in the corrected test period are calculated based on the test record data of each pig obtained after step six, and the average daily feed intake is used to fill in the missing feed intake at the age in the corrected test period.

[0028] Further, in the eighth step, the test record data of each pig in the corrected test period after step seven is calculated in units of each pig to obtain average daily gain, feed conversion ratio and growth data information, which are recorded in an electronic table.

[0029] In the ninth step, after step eight, the corrected growth information data of each pig in the corrected test period are plotted into a growth curve, taking the corrected test age interval as the horizontal coordinate of the growth curve, and taking the body weight data of each pig at different ages as the vertical coordinate to plot the growth curve.

[0030] Another object of the present application is to provide a kind of pig growth performance test data correction system, the pig growth performance test data correction system includes:

[0031] Test data acquisition module, for downloading all continuous test data from pig growth performance test system;

[0032] Data summary module, for all test records of each pig are summarized into an electronic table;

[0033] Information matching module, for matching the test record table of each pig with birth information;

[0034] The determination period correction module is used for correcting the determination period of the whole pig population by using the iterative age finding maximum intersection method, calculating the correction determination period covering the most number of the population, taking the minimum age in the correction determination period as the corrected entry determination age and the maximum age as the corrected exit determination age.

[0035] The weight and feed calculation and filling module uses the Logistic method to predict and calculate the weight of each pig at the age, and uses the predicted weight to fill in the missing weight at the corresponding age, uses the feed intake of the real data record in the correction determination period to calculate the total feed intake and average daily feed intake of each pig, and uses the average daily feed intake to fill in the missing feed intake at the age in the correction determination period.

[0036] The growth curve drawing module takes the correction determination period as the horizontal coordinate of the curve graph, and takes the corrected weight per day as the vertical coordinate, to draw the complete growth curve in the correction determination period.

[0037] In combination with the technical scheme and the solved technical problem, the technical scheme to be protected by the present application has the following advantages and positive effects:

[0038] Firstly, the pig growth performance determination data correction method provided by the present application can achieve the purpose of correction from the source by downloading all the csv format data generated in the determination period, and matches the determination record of each pig with the birth information of each pig, so as to observe the growth rule of each pig at different ages, and to compare between different breeds or within the same breed.

[0039] The growth data correction of the present application involves two categories of determination period correction and missing value filling of determination records, and can achieve the purpose of correcting the daily weight gain, total weight gain, daily feed intake, total feed intake, ADG and FCR; the multiple growth data of each pig in the same correction determination period are corrected, the accurate comparison of the growth performance between different individuals is realized, and thus it is helpful to select individuals with good growth performance in breeding work.

[0040] The present application takes the average value of multiple normal weight records of each pig at the same determination age as the weight of each pig at the determination age, which avoids the interference of abnormal values; the correction determination period covering the most number of individuals in the pig population is calculated, the minimum value in the correction determination period is taken as the corrected entry determination age, and the maximum value is taken as the corrected exit determination age; the determination pigs at different growth stages are compared with each other in the correction determination period.

[0041] The missing weight of the measured record corresponding to the predicted age is filled by calculating the predicted weight of the age, all weight data in the measured period is corrected, and the missing daily weight gain record can be avoided; the average daily feed intake is calculated by multiple measured dates, the missing feed intake of the measured age is filled, all feed data in the measured correction interval is corrected, and the missing daily feed record can be avoided. The final corrected result can realize the comparison between the measured breeding pigs in different growth stages. The actual situation of the breeding process of the breeding pig is combined, and the problem that the performance difference between individuals cannot be effectively compared when the growth performance of pigs in different growth stages is measured is effectively solved.

[0042] Secondly, the method adopted by the present application can correct abnormal growth performance measurement data, improve the scientificity of growth performance measurement data, and improve the improvement speed of growth speed and feed efficiency traits of Chinese breeding pigs. The problem of large analysis result error and the problem of affecting the accuracy of selecting excellent breeding pigs caused by inconsistent weight, age and age of the measured pigs are effectively solved.

[0043] Thirdly, the technical solution of the present application can provide a reference for selecting high-quality breeding pigs for breeding farms after transformation, and fully play the value of growth performance measurement.

[0044] The technical solution of the present application fills the defect that the automatic growth performance measurement system of breeding pigs does not have a complete quality control process.

[0045] Fourthly, each step has made significant technical progress in the process of implementing the whole breeding pig growth performance measurement data correction method. The specific technical progress of each step is as follows:

[0046] Step one: The process of downloading all continuous measurement data improves the efficiency and accuracy of data acquisition. Through automatic data collection, the possibility of human error and missing data is avoided, and the integrity and reliability of the data are improved.

[0047] Step two: By summarizing all measurement records of each pig, subsequent processing and analysis are facilitated. At the same time, the individual number of the pig is used as the name of the electronic form, which facilitates the differentiation and identification of each pig and improves the efficiency of data management.

[0048] Step three: By matching with the birth information, more rich background information (such as breed, gender, birth date, etc.) is obtained, which is very important for subsequent data analysis and interpretation.

[0049] Step four: By calculating the average weight of each measured age and daily feed intake, the multiple measurement results are considered comprehensively, and the stability and reliability of the data are improved.

[0050] Step five: By counting the entry and exit ages of the entire pig test group, the correction test period covering the most individuals is determined, which improves the representativeness and scientificity of the data.

[0051] Step six: The weight of each age is predicted by the Logistic regression model, the missing values are filled, the incomplete data problem is solved, and the availability of the data is improved.

[0052] Step seven: The total feed intake and average daily feed intake of each pig in the correction test period are calculated, the missing values of the daily feed intake test records are filled, and the growth state of the pigs is more comprehensively reflected.

[0053] Step eight: The average daily gain and feed conversion ratio of each pig in the correction test period are calculated, more accurate growth performance data are obtained, and the basis for optimizing the feeding strategy is provided.

[0054] Step nine: By drawing the growth curve of the pig, the growth trend of the pig is intuitively displayed, a more intuitive and easier to understand data presentation form is provided, and it is convenient for the breeder or researcher to understand and apply. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a flowchart of the pig growth performance test data correction method provided by the embodiment of the present application;

[0056] Figure 2 is a distribution diagram of the individual test age range provided by the embodiment of the present application;

[0057] Figure 3 is a distribution diagram of the average daily gain before and after correction provided by the embodiment of the present application;

[0058] Figure 4 is a distribution diagram of the feed conversion ratio before and after correction provided by the embodiment of the present application;

[0059] Figure 5 is a comparison diagram of the body weight before correction, the body weight after correction and the model predicted body weight of part of the individuals using the present application provided by the embodiment of the present application;

[0060] Figure 6 is a structure framework diagram of the pig growth performance test data correction system provided by the embodiment of the present application;

[0061] In the figure: 1, test data acquisition module; 2, data summary module; 3, information matching module; 4, test period correction module; 5, body weight and feed calculation and filling module; 6, growth curve drawing module. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0063] As shown in Figure 1 The pig growth performance measurement data correction method provided by the embodiment of the present application comprises the following steps:

[0064] S101, obtaining measurement data and summarizing to obtain a measurement record table, and matching the measurement record table with birth information;

[0065] S102, using an iterative maximum intersection method for calculating age to correct the measurement period of the entire pig population, calculating a correction measurement period covering the most number of the population, and taking the minimum age in the correction measurement period as the corrected measurement age and the maximum age as the corrected measurement age.

[0066] S103, calculating the weight gain of each pig in the corrected measurement age, and filling in the missing weight of the corresponding age with the calculated predicted age weight, calculating the average daily feed intake of each pig in the corrected measurement age, and filling in the daily feed intake missing in the corrected measurement age with the average daily feed intake.

[0067] S104, drawing a growth curve of each pig in the corrected measurement period.

[0068] As a preferred embodiment, the pig growth performance measurement data correction method based on iterative calculation of the correction measurement period and Logistic regression analysis provided by the embodiment of the present application specifically comprises the following steps:

[0069] Step one, downloading all continuous measurement data in the measurement period from the pig growth performance measurement system;

[0070] Step two, summarizing all measurement records of each pig on different measurement dates into an electronic table, and taking the individual number of the pig as the electronic table name; for data without individual number, directly deleting;

[0071] Step three, matching the measurement data table of each pig with the birth information, and adding breed, gender, birth date and age column in the table;

[0072] Step four, averaging multiple weight records of each measurement age of each pig executed in step three as the weight of the measurement age, and summing multiple feed records of each measurement age as the daily feed intake of the measurement age;

[0073] Step five, the correction of the measurement age during the measurement is carried out, the entry measurement age and the end measurement age of the whole pig measurement group and the measurement during the measurement are statistically calculated, the average value (Mean MP ) and the standard deviation (SD MP ) of the whole group during the measurement are calculated, the individuals meeting the condition: Mean MP -SD MP ≤MP≤Mean MP +SD MP during the measurement (MP) are reserved, and the individuals not meeting the condition are not included in the calculation of the correction measurement period. The measurement period covering the largest number of individuals in the whole group is calculated by using the method of loop iteration to calculate the set of measurement periods of all individuals after screening, as the correction measurement period of the pig group.

[0074] Step six, the body weight record of each pig obtained by executing step four is respectively fitted with the model of age about weight by using the Logistic regression model, and the body weight of each age is predicted; the missing values of the weight measurement records of each pig during the correction measurement period obtained in step four are filled with the corresponding age body weight predicted by the Logistic regression model;

[0075] Step seven, the total feed intake and the average daily feed intake of the pigs with measurement records during the correction measurement period are calculated by summing up all the feed records of each pig at each age during the correction measurement period, and the missing values of the daily feed intake measurement records during the correction measurement period are filled with the average daily feed intake;

[0076] Step eight, the multiple growth data of each pig during the correction measurement period obtained by executing step seven are calculated, the average daily gain and the feed conversion ratio of each pig during the correction measurement period are calculated, and recorded into the electronic table;

[0077] Step nine, the corrected growth data of each pig obtained by executing step six are plotted, the correction measurement period is taken as the horizontal coordinate, and the body weight value corresponding to each age is taken as the vertical coordinate, and the growth curve graph of the pig is plotted.

[0078] Preferably, the generation of the whole csv format data of the measurement period in step one includes all the records generated during the measurement period, so as to achieve the purpose of correction from the source.

[0079] Preferably, the records of the same pig distributed in different measurement date tables in step two are summarized to obtain all the measurement records of each pig during its whole measurement period.

[0080] Preferably, the measurement records of each pig are matched with the birth information of the pig in step three, and then the growth rule of each pig at different ages is observed, so as to carry out the comparison between different breeds or within the same breed.

[0081] Preferably, the step four averages the plurality of body weight records of each pig at each test age to obtain the body weight at the test age, and sums the plurality of feed intake records at each test age to obtain the total daily feed intake at the test age, so as to unify the test records of the whole test period from the aspects of body weight and total daily feed intake, to facilitate the subsequent correction.

[0082] Preferably, the step five corrects the test age interval of the whole test population, calculates the correction test period covering the largest number of individuals, takes the minimum value of the correction test period as the test-in age, and takes the maximum value of the correction test period as the test-out age, so as to realize the comparison of growth performance of pigs at different growth stages within the same correction test period.

[0083] Preferably, the step six models the age and body weight records of each pig by using the Logistic method to find the body weight of each pig at the age within the correction test period, and fills in the missing body weight at the corresponding age within the correction test period by using the Logistic method to predict the body weight corresponding to the age.

[0084] Preferably, the step seven calculates the total feed intake and the average daily feed intake of each pig within the correction test period after the execution of the step six, and fills in the missing feed intake at the age within the correction test period by using the average daily feed intake.

[0085] Preferably, the step eight calculates the growth data information such as ADG and FCR of each pig within the correction test period based on the test records of each pig within the correction test period after the execution of the step seven, and records the growth data information into an electronic table.

[0086] Preferably, the step nine draws the growth curve of each pig within the correction test period based on the corrected growth information of each pig, takes the corrected test age interval as the horizontal coordinate of the growth curve, and takes the body weight data of each pig at different ages as the vertical coordinate of the growth curve after the execution of the step eight.

[0087] The individual test age range distribution diagram provided by the embodiment of the present application is shown in Figure 2 .

[0088] The horizontal coordinate of the distribution diagram represents the number of individuals in the test population, and the vertical coordinate represents the test age. Each vertical line represents the test interval of an individual, and there are two horizontal lines perpendicular to the vertical lines in the diagram. The lower horizontal line represents the test-in age of the correction test period of the test population, and the upper horizontal line represents the test-out age of the correction test period of the test population.

[0089] The average daily gain distribution before and after correction is shown in Figure 3 .

[0090] Before correction, the distribution is relatively discrete, and the number of individuals capable of calculating ADG is small; after correction, the distribution is concentrated, the number of individuals after correction is increased, and the amount of data is ensured.

[0091] The FCR distribution before and after correction is as shown in Figure 4

[0092] Before correction, the FCR is discrete, and a large number of individuals have low or high FCR values; after correction, the distribution is concentrated, and the FCR values of abnormal individuals are eliminated.

[0093] The comparison of the body weight of some individuals before correction, the body weight after correction, and the model predicted body weight is as shown in Figure 5

[0094] Through the correction of the growth data of these 6 individuals, the growth curve of each individual is more in line with the growth and development law of breeding pigs.

[0095] As shown in Figure 6 The breeding pig growth performance measurement data correction system provided by the embodiment of the present application comprises:

[0096] The measurement data acquisition module 1 is used to download all continuous measurement data from the breeding pig growth performance measurement system;

[0097] The data summary module 2 is used to summarize all measurement records of each pig into an electronic table;

[0098] The information matching module 3 is used to match the measurement record table of each pig with the birth information;

[0099] The measurement period correction module 4 is used to correct the measurement period of the entire breeding pig population by using the iterative maximum intersection method of age, calculate the correction measurement period covering the most number of the population, and take the minimum age in the correction measurement period as the correction entry measurement age and the maximum age as the correction exit measurement age;

[0100] The body weight and feed calculation and filling module 5 uses the Logistic method to predict and calculate the body weight of each pig at the age, and uses the predicted body weight to fill in the missing body weight at the corresponding age, uses the feed intake of the real data record in the correction measurement period to calculate the total feed intake and the average daily feed intake of each pig, and uses the average daily feed intake to fill in the missing feed intake at the age in the correction measurement period.

[0101] The growth curve drawing module 6 takes the correction measurement period as the horizontal coordinate of the curve graph, and takes the corrected body weight per day as the vertical coordinate, to draw the complete growth curve in the correction measurement period.

[0102] The technical solutions of the present application will be further described in combination with specific embodiments.

[0103] ​​The pig growth performance determination data quality control method provided by the embodiment of the application specifically comprises the following steps:

[0104] (1) Download all csv format data in the determination period from the pig growth performance determination system:

[0105] A total of 112 days of growth performance determination records including 831,835 records of 829 pigs were downloaded from a certain pig farm from November 10, 2022 to March 1, 2023.

[0106] (2) For each pig, all determination records of the pig on different determination dates are summarized into an excel table, data without individual number is directly deleted, and the individual number of the pig is used as the file name. A total of 829 different pig number named tables are obtained.

[0107] (3) The determination record table of each pig is matched with the birth information, and breed, gender, birth date and day-old column are added to the table.

[0108] (4) The average value of multiple body weight records of each pig at each determination day is taken as the body weight at the determination day, and the sum of multiple feed records at each determination day is taken as the daily feed intake at the determination day.

[0109] (5) First, the determination period is corrected, the determination period of the entire pig determination group is calculated, the average value (Mean MP ) and the standard deviation (SD MP ) of the entire group during the determination period are calculated, and the individual meeting the condition: Mean MP -SD MP ≤MP≤Mean MP +SD MP is reserved, and the individual not meeting the condition is not included in the calculation of the corrected determination period. Then, the iterative method is used to calculate and compare the determination period covering the most individuals as the corrected determination period, the determination period of the corrected determination period is 88, the determination period of the corrected determination period is 168, and the coverage rate of the correction is 100%.

[0110] (6) The age and body weight records of each pig after step five are modeled by using the Logistic method to find the normal body weight interval of each pig at the age. The Logistic formula is as follows:

[0111] Yt=A / (1+Be-kt)

[0112] Yt: weight of the tth day (kg); A: maximum weight (kg); k: instantaneous growth rate; B: biological constant; t: age; e: natural logarithm.

[0113] (7) The weight value of each pig at different ages in the correction determination period predicted by the calculation model is filled into the missing weight value at the corresponding age in the correction determination period. The average daily feed intake of each pig in the correction determination period is calculated, and the average daily feed intake is filled into the missing daily feed intake at the age in the correction determination period.

[0114] (8) After step seven is performed, the total weight gain, average daily weight gain (ADG), total feed intake, and feed conversion ratio (FCR) of each pig in the correction determination period are calculated.

[0115] (9) After step eight is performed, the growth curve of each pig in the correction determination period is drawn, with the correction determination period as the horizontal coordinate and the weight at different ages as the vertical coordinate.

[0116] A computer device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the following steps:

[0117] The determination data is obtained and summarized into a determination record table in units of each pig, the determination record table is matched with the birth information, the correction determination period in which the determined breeding pig population covers the most pig individuals is obtained by the maximum intersection method of determination ages, the missing values of the weight data and the feed intake data of each pig in the correction determination period are filled and corrected, the average daily weight gain and the feed conversion ratio of each pig in the correction determination period are calculated, the change of the weight of each pig with the increase of the age in the correction determination period is calculated, and the growth curve of each pig in the same determination age interval is drawn.

[0118] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to make the processor execute the following steps:

[0119] The determination data is obtained and summarized into a determination record table in units of each pig, the determination record table is matched with the birth information, the correction determination period in which the determined breeding pig population covers the most pig individuals is obtained by the maximum intersection method of determination ages, the missing values of the weight data and the feed intake data of each pig in the correction determination period are filled and corrected, the average daily weight gain and the feed conversion ratio of each pig in the correction determination period are calculated, the change of the weight of each pig with the increase of the age in the correction determination period is calculated, and the growth curve of each pig in the same determination age interval is drawn.

[0120] An information data processing terminal is used to realize the breeding pig growth performance determination data correction system provided by the present application.

[0121] A method for correcting sow growth performance test data first includes obtaining test data and summarizing it into a test record table with each sow as a unit, and then matching the test record table with birth information.

[0122] The most covered sow individual number of the correction test period of the sow population is obtained by finding the maximum intersection of the test age. The specific method is to count the entry and exit test age of all pigs, and the individuals that meet the condition: Mean MP -SD MP ≤MP≤Mean MP +SD MP are screened and reserved, and then a loop iteration method is used to adjust the age interval until an age interval is found that has the most pigs in the interval.

[0123] For the missing values of body weight data and feed intake data of each pig in the correction test period, we can use a Logistic regression model to fill them. This model can predict the body weight and feed intake at the corresponding age of the missing value according to the body weight and feed intake data of each pig at other ages. In this way, we get complete and continuous body weight and feed intake data.

[0124] Then, calculate the average daily gain and feed conversion ratio of each pig in the correction test period, as well as the change of body weight with age. These calculation results can help us better understand the growth performance of sows. Draw the growth curve of each pig in the same test age interval. This graph can visually show the growth trend of sows, helping us better evaluate the growth performance of sows.

[0125] First, calculate the sum of all feed records of each pig at each age in the correction test period, and then divide this sum by the number of ages to get the average daily feed intake. Use this average daily feed intake to fill in the missing values of the daily feed intake test record.

[0126] The advantage of this method is that it does not rely on complex statistical models, but directly uses the actual average feed intake. This ensures that the filled data is closer to the true feed intake, resulting in more accurate growth performance test results.

[0127] Four specific examples and their implementation schemes are provided:

[0128] Example 1: Correction of sow growth performance test data for large-scale breeding farms

[0129] In this example, we assume a large-scale pork production farm wants to use this measurement method to monitor the growth performance of its breeding pigs. In this case, data will be collected from thousands of pigs within a few months. All data will be collected through automated measurement systems (such as RFID technology) and then uploaded to a cloud server through a wireless network. This implementation will enable the farm to monitor and adjust the growth performance of its breeding pigs on a large-scale and continuous basis, improving its meat quality and overall production efficiency.

[0130] Example Two: Breeding Pig Growth Performance Measurement Data Correction for Research Institutes

[0131] In this example, we assume a research institute is studying the growth performance of breeding pigs. The institute can use this method to correct its research data, ensuring the accuracy and consistency of its data. In addition, the institute can also use this method to compare the growth performance of pigs of different breeds or under different feeding conditions. This implementation will enable the institute to more accurately conduct scientific research and publish research results.

[0132] Example Three: Breeding Pig Growth Performance Measurement Data Correction for Individual Farmers

[0133] In this example, we assume a small individual farmer is raising a small number of breeding pigs. They can use this method to monitor the growth performance of each pig so that they can adjust their feeding strategies, such as changing the feed ratio, adjusting the pigsty environment, etc. This implementation will enable individual farmers to improve the growth performance of their breeding pigs on a relatively small scale.

[0134] Example Four: Breeding Pig Growth Performance Measurement Data Correction for Animal Health Care Companies

[0135] In this example, we assume an animal health care company is researching how to improve the growth performance of breeding pigs. The company can use this method to test the effects of its products (such as vaccines, feed additives, etc.) on pig growth performance. Through this method, the company can more accurately measure the effects of its products, thereby improving its products and more effectively promoting them in the market. This implementation will enable animal health care companies to better utilize their products in a commercial environment.

[0136] It should be noted that embodiments of the present application can be realized by hardware, software, or a combination of software and hardware. The hardware portion can be realized by a special logic; the software portion can be stored in a memory and executed by a proper instruction execution system, such as a microprocessor or a specially designed hardware. A person of ordinary skill in the art can understand that the above-mentioned apparatus and method can be realized by computer executable instructions and / or included in processor control codes, such as a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The apparatus of the present application and its modules can be realized by a hardware circuit, such as a very large scale integrated circuit or a gate array, a semiconductor, such as a logic chip, a transistor, or a programmable hardware device, such as a field programmable gate array, a programmable logic device, or the like, by software executed by various types of processors, or by a combination of the above-mentioned hardware circuit and software, such as firmware.

[0137] The above description is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any modification, equivalent replacement, and improvement within the technical range disclosed by the present application, and within the spirit and principle of the present application, should be included in the protection scope of the present application.

Claims

1. A method for correcting growth performance measurement data of breeding pigs, characterized in that, include: The measurement data were acquired and summarized into a measurement data table per pig. The measurement data table for each pig was matched with the birth information of the breeding pig population to obtain the age, sex, and breed information of each pig. By calculating the optimal intersection between the sets formed during the measurement period of each pig, the calibration measurement period covering the most individuals in the entire breeding pig population was determined. Missing values ​​in the daily weight data and daily feed intake data of each pig during the calibration measurement period were filled and corrected. The average daily weight gain and feed conversion ratio of each pig during the calibration measurement period were calculated. The change in weight of each pig with age during the calibration measurement period was calculated, and the growth curve of each pig during the calibration measurement period was plotted. The specific steps of the method for correcting growth performance measurement data of breeding pigs include: Step 1: Download all continuous measurement data for the measurement period from the pig growth performance measurement system; Step 2: Compile all measurement records for each pig on different measurement dates into a single spreadsheet, and name the spreadsheet using the pig's individual ID; delete any data without an individual ID. Step 3: Match the measurement record sheet for each pig with the birth information, and add columns for breed, sex, date of birth, and age to the table; Step 4: Calculate the average of multiple weight records for each pig at each measurement day for each pig in Step 3 as the weight for that measurement day, and sum the multiple feed intake records for each measurement day as the daily feed intake for that measurement day. Step 5: Perform age interval correction for the measurement. Statistical analysis is conducted on the entry and exit ages of the entire breeding pig population. For pigs meeting the following criteria during the measurement period: Mean MP - SD MP ≤MP≤Mean MP +SD MP Individuals are selected and retained, and then the measurement interval with the largest number of covered individuals in the whole population is calculated using a cyclic iterative method. Step 6: Use a Logistic regression model to fit the weight records of each pig obtained in Step 4 to the model of age versus weight, and predict the weight at each age; fill in the missing values ​​of the weight measurement records of each pig during the calibration period obtained in Step 4 with the corresponding age-weight predicted by the Logistic regression model. Step 7: Sum all feeding records for each pig at each age during the calibration period, calculate the total feed intake and average daily feed intake with records during the calibration period, and use the average daily feed intake to fill in the missing values ​​of the daily feed intake records during the calibration period. Step 8: Calculate the multiple growth data points for each pig obtained in Step 7 during the calibration period, calculate the average daily weight gain and feed conversion ratio for each pig during the calibration period, and record them in a spreadsheet. Step nine: Plot the corrected growth data of each pig obtained in step six, using the correction period as the x-axis and the weight value corresponding to each age as the y-axis, to create a growth curve for the pigs.

2. The method for correcting growth performance measurement data of breeding pigs as described in claim 1, characterized in that, In step one, all CSV format data generated during the measurement period is downloaded. The CSV format data contains all records generated during the measurement period. In step two, the records of the same pig distributed in different measurement date tables are summarized to obtain all the measurement records of each pig during its entire measurement period. In step three, the measurement records of each pig are matched with its birth information to observe the growth pattern of each pig at different ages, which is used for comparison between or within breeds. In step four, all measurement records for the entire measurement period are standardized based on two categories: body weight at different ages and total daily feed intake, for subsequent correction.

3. The method for correcting growth performance measurement data of breeding pigs as described in claim 1, characterized in that, In step five, the age range of the entire test population is corrected, and the corrected test period covering the largest number of individuals is calculated. The minimum value of the corrected test period is taken as the entry age and the maximum value is taken as the exit age. This enables the comparison of growth performance between pigs at different growth stages within the same corrected test period. In step six, the age and weight records of each pig are modeled using the Logistic method to calculate and predict the weight of each pig at its age during the correction period; the weight corresponding to the age is predicted using the Logistic method to fill in the missing weight at the corresponding age during the correction period. In step seven, the total feed intake and average daily feed intake of each pig during the correction period are calculated based on the measurement record data of each pig obtained after step six, and the average daily feed intake is used to fill in the feed intake missing due to age during the correction period.

4. The method for correcting growth performance measurement data of breeding pigs as described in claim 3, characterized in that, In step eight, the average daily weight gain and feed conversion ratio growth data of each pig during the calibration period after step seven are calculated on a per-pig basis and recorded in a spreadsheet. In step nine, after performing step eight, during the calibration and measurement period, growth curves are plotted using the corrected growth information data of each pig. The interval after calibration based on the measured age is used as the abscissa of the growth curve, and the weight data of each pig at different ages is used as the ordinate of the growth curve.

5. The method for correcting growth performance measurement data of breeding pigs as described in claim 1, characterized in that, In step five, firstly, all actual ages of each individual are listed. Then, the actual ages of each individual are traversed, and the intersection of the age intervals of the individual's age interval with the actual ages of other individuals is calculated. The number of individuals covered by the intersection of different age intervals is calculated, and the intersection of the age intervals covering the largest number of individuals is taken as the correction period for the entire breeding pig population. The minimum value in the age interval intersection covering the largest number of individuals is the entry age of the correction period, and the maximum value is the exit age of the correction period.

6. A data correction system for measuring the growth performance of breeding pigs, used to implement the data correction method for measuring the growth performance of breeding pigs according to any one of claims 1 to 5, characterized in that, The boar growth performance measurement data correction system includes: The measurement data acquisition module is used to download all continuous measurement data from the pig growth performance measurement system. The data aggregation module is used to summarize all the test records for each pig into a single spreadsheet; The information matching module is used to match the measurement record sheet of each pig with the birth information; The testing period correction module is used to correct the testing period of the entire breeding pig population using the iterative maximum intersection method of age calculation. It calculates the corrected testing period that covers the largest number of people in the population, and uses the minimum age in the corrected testing period as the corrected entry age and the maximum age as the corrected exit age. The weight and feed intake calculation and filling module uses the Logistic method to predict and calculate the weight of each pig at each age, and uses the predicted weight to fill the missing weight at the corresponding age. It uses the feed intake with real data records during the correction period to calculate the total feed intake and average daily feed intake of each pig, and uses the average daily feed intake to fill the missing feed intake at each age during the correction period. The growth curve plotting module uses the calibration measurement period as the horizontal axis of the curve and the daily calibrated body weight as the vertical axis to plot the complete growth curve within the calibration measurement period.

7. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the following steps: The measurement data were acquired and summarized into a measurement record table per pig. The measurement record table was then matched with the birth information. The correction measurement period covering the largest number of pigs in the measured breeding pig population was obtained by finding the maximum intersection of measurement ages. Missing values ​​in the weight and feed intake data of each pig during the correction measurement period were filled and corrected. The average daily weight gain and feed conversion ratio of each pig during the correction measurement period were calculated. The change in weight of each pig with age during the correction measurement period was calculated, and the growth curve of each pig within the same measurement age interval was plotted. The specific steps of the method for correcting growth performance measurement data of breeding pigs include: Step 1: Download all continuous measurement data for the measurement period from the pig growth performance measurement system; Step 2: Compile all measurement records for each pig on different measurement dates into a single spreadsheet, and name the spreadsheet using the pig's individual ID; delete any data without an individual ID. Step 3: Match the measurement record sheet for each pig with the birth information, and add columns for breed, sex, date of birth, and age to the table; Step 4: Calculate the average of multiple weight records for each pig at each measurement day for each pig in Step 3 as the weight for that measurement day, and sum the multiple feed intake records for each measurement day as the daily feed intake for that measurement day. Step 5: Perform age interval correction for the measurement. Statistical analysis is conducted on the entry and exit ages of the entire breeding pig population. For pigs meeting the following criteria during the measurement period: Mean MP - SD MP ≤MP≤Mean MP +SD MP Individuals are selected and retained, and then the measurement interval with the largest number of covered individuals in the whole population is calculated using a cyclic iterative method. Step 6: Use a Logistic regression model to fit the weight records of each pig obtained in Step 4 to the model of age versus weight, and predict the weight at each age; fill in the missing values ​​of the weight measurement records of each pig during the calibration period obtained in Step 4 with the corresponding age-weight predicted by the Logistic regression model. Step 7: Sum all feeding records for each pig at each age during the calibration period, calculate the total feed intake and average daily feed intake with records during the calibration period, and use the average daily feed intake to fill in the missing values ​​of the daily feed intake records during the calibration period. Step 8: Calculate the multiple growth data points for each pig obtained in Step 7 during the calibration period, calculate the average daily weight gain and feed conversion ratio for each pig during the calibration period, and record them in a spreadsheet. Step nine: Plot the corrected growth data of each pig obtained in step six, using the correction period as the x-axis and the weight value corresponding to each age as the y-axis, to create a growth curve for the pigs.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps: The measurement data were acquired and summarized into a measurement record table per pig. The measurement record table was then matched with the birth information. The correction measurement period covering the largest number of pigs in the measured breeding pig population was obtained by finding the maximum intersection of measurement ages. Missing values ​​in the weight and feed intake data of each pig during the correction measurement period were filled and corrected. The average daily weight gain and feed conversion ratio of each pig during the correction measurement period were calculated. The change in weight of each pig with age during the correction measurement period was calculated, and the growth curve of each pig within the same measurement age interval was plotted. The specific steps of the method for correcting growth performance measurement data of breeding pigs include: Step 1: Download all continuous measurement data for the measurement period from the pig growth performance measurement system; Step 2: Compile all measurement records for each pig on different measurement dates into a single spreadsheet, and name the spreadsheet using the pig's individual ID; delete any data without an individual ID. Step 3: Match the measurement record sheet for each pig with the birth information, and add columns for breed, sex, date of birth, and age to the table; Step 4: Calculate the average of multiple weight records for each pig at each measurement day for each pig in Step 3 as the weight for that measurement day, and sum the multiple feed intake records for each measurement day as the daily feed intake for that measurement day. Step 5: Perform age interval correction for the measurement. Statistical analysis is conducted on the entry and exit ages of the entire breeding pig population. For pigs meeting the following criteria during the measurement period: Mean MP - SD MP ≤MP≤Mean MP +SD MP Individuals are selected and retained, and then the measurement interval with the largest number of covered individuals in the whole population is calculated using a cyclic iterative method. Step 6: Use a Logistic regression model to fit the weight records of each pig obtained in Step 4 to the model of age versus weight, and predict the weight at each age; fill in the missing values ​​of the weight measurement records of each pig during the calibration period obtained in Step 4 with the corresponding age-weight predicted by the Logistic regression model. Step 7: Sum all feeding records for each pig at each age during the calibration period, calculate the total feed intake and average daily feed intake with records during the calibration period, and use the average daily feed intake to fill in the missing values ​​of the daily feed intake records during the calibration period. Step 8: Calculate the multiple growth data points for each pig obtained in Step 7 during the calibration period, calculate the average daily weight gain and feed conversion ratio for each pig during the calibration period, and record them in a spreadsheet. Step nine: Plot the corrected growth data of each pig obtained in step six, using the correction period as the x-axis and the weight value corresponding to each age as the y-axis, to create a growth curve for the pigs.

9. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the data correction system for measuring the growth performance of breeding pigs as described in claim 6.

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