An egg-laying hen breeding management system and management method

Abstract

The present invention discloses a breeding management system and method for laying hens, which relates to the technical field of data management and is used to solve the problems that the previous mixer did not determine the self-state of the feed, and uneven mixing of the feed would occur when the adjustable-speed motor was too fast or too slow, and the feed was easily damaged; it includes classifying the feed according to the feed data by the self-service sampling classification method, setting the rotation speed threshold according to the historical data, classifying the rotation speed of the mixer through the rotation speed threshold, and matching different types of feed with different rotation speeds of the mixer; collecting the feed toughness data of the feed classified as intermediate-type feed, mixing the intermediate-type feed at the medium rotation speed of the mixer and collecting the feedback data; distributing the feed with different toughness to different rotation speeds of the mixer according to the feedback data. By matching different qualities of feed with different rotation speeds of the mixer, the damage of the feed is reduced, the cost is saved, and the production efficiency of laying hens is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more specifically, to a management system and method for laying hen breeding. Background Art

[0002] Agricultural automation technology, such as using drones, agricultural robots and other technologies, can realize the automation of agricultural production such as planting and harvesting, improving agricultural efficiency. In the process of laying hen breeding, many processes involve automation technology, especially in feed manufacturing, which further improves agricultural production efficiency.

[0003] The prior art has the following deficiencies:

[0004] Laying hen feed mixers are usually equipped with adjustable-speed motors. The previous mixers did not determine the self-state of the feed. When the adjustable-speed motor is too fast or too slow, it will cause problems such as uneven mixing and easy breakage of the feed.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a management system and method for laying hen breeding to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A management method for laying hen breeding, comprising the following steps:

[0009] Step S1, collecting feed data and historical data;

[0010] Step S2, classifying the feed according to the feed data by the self-sampling classification method, setting a rotation speed threshold according to the historical data, classifying the rotation speed of the mixer by the rotation speed threshold, and matching different types of feed with different rotation speeds of the mixer;

[0011] Step S3, collecting the feed toughness data of the feed classified as intermediate-type feed, mixing the intermediate-type feed at the medium rotation speed of the mixer and collecting feedback data;

[0012] Step S4, comprehensively using the feed toughness data and the feedback data to allocate the intermediate-type feed to different rotation speeds of the mixer by fuzzy inference.

[0013] In a preferred embodiment, in step S1, the feed data includes the particle size difference of the feed raw materials and the fluidity difference of the feed raw materials, and the historical data is the rotation speed of the mixer.

[0014] In a preferred embodiment, in step S2, the particle size difference of the feed raw materials is calculated using the self-help sampling classification method by detecting the particle sizes of different feed raw material particles with a particle counter. The specific steps are as follows:

[0015] Collect N particle sizes of the raw materials of each feed in the feed ratio, and combine the particle sizes of the raw materials of each feed into different data sets respectively. Randomly extract one data from each data set each time, take the difference between the extracted data in pairs, and then take the average value of the obtained difference data as the first particle size difference of the raw materials. Repeat the operation multiple times to obtain N particle size differences of the raw materials, and take the median of them as the particle size difference of the feed raw materials.

[0016] The poor fluidity of the feed raw materials is represented by calculating the flow time of the feed raw materials detected by an automatic outflow time tester. The specific steps are as follows:

[0017] First, determine the raw materials of each feed in the feed ratio. Take the same mass of each raw material and pass it through the automatic outflow time tester respectively, and record the flow time of each raw material. Take the difference between the obtained data in pairs and calculate the average value as the first raw material fluidity difference. Change the sampling mass and repeat the operation N times to obtain N raw material fluidity differences, and take the median of them as the feed raw material fluidity difference.

[0018] In a preferred embodiment, in step S2, the current feed is classified by comparing the currently calculated particle size difference and fluidity difference of the feed raw materials with the preset particle size difference threshold and fluidity difference threshold of the feed raw materials;

[0019] If the currently calculated particle size difference of the feed raw materials exceeds the particle size difference threshold of the feed raw materials and the currently calculated fluidity difference of the feed raw materials exceeds the fluidity difference threshold of the feed raw materials, mark the current feed as an anti-mixing feed; if the currently calculated particle size difference of the feed raw materials is lower than the particle size difference threshold of the feed raw materials and the currently calculated fluidity difference of the feed raw materials is lower than the fluidity difference threshold of the feed raw materials, mark the current feed as an easy-to-mix feed; otherwise, mark the current feed as an intermediate-type feed.

[0020] Access the feed equipment database to obtain multiple historical rotation speeds of the mixer in the historical data and combine them into a rotation speed data set. Set the mixer rotation speed threshold by calculating the average value and standard deviation of the rotation speed data set. After removing the units of the data in the rotation speed data set, take the average value of all the obtained values and mark it as And calculate the standard deviation. The mixer rotation speed threshold includes a high-speed threshold and a low-speed threshold. Set the high-speed threshold to Set the low-speed threshold to where a is the threshold proportionality coefficient, and the threshold proportionality coefficient can be set according to the actual situation to adjust the high-speed threshold and the low-speed threshold;

[0021] If the mixing machine speed exceeds the high-speed threshold, it is marked as the high-speed rotation speed of the mixing machine; if the mixing machine speed is lower than the low-speed threshold, it is marked as the low-speed rotation speed of the mixing machine; otherwise, the mixing machine speed is marked as the medium-speed rotation speed of the mixing machine.

[0022] Match the anti-mixed feed with the high-speed rotation speed of the mixing machine; match the easy-to-mix feed with the low-speed rotation speed of the mixing machine; match the intermediate-type feed with the medium-speed rotation speed of the mixing machine.

[0023] In a preferred embodiment, in step S3, collect the feed toughness data marked as intermediate-type feed. The feed toughness data is the feed crushing index. After mixing the intermediate-type feed at the medium-speed rotation speed of the mixing machine, collect the feedback data, and the feedback data is the feed falling density;

[0024] The feed crushing index is an index to measure the degree of particle crushing of each raw material in the feed formula;

[0025] The feed falling density is the difference between the density of the intermediate-type feed given in the feed management system database and the actual density of the intermediate-type feed after mixing at the medium-speed rotation speed of the mixing machine.

[0026] In a preferred embodiment, in step S4, if the current feed is intermediate-type feed, use a particle strength analyzer to collect the crushing index of each raw material in the current feed formula and calculate the average value, and use the average value calculation result as the current feed crushing index.

[0027] Mix the current feed at the medium-speed rotation speed of the mixing machine. The specific steps to calculate the feed falling density are as follows:

[0028] First, access the feed management system database to obtain the standard density of the current feed, denoted as ρ b , take N equal amounts of the same intermediate-type feed samples, select N different rotation speeds within the medium-speed rotation speed range of the mixing machine. The medium-speed rotation speed range of the mixing machine is the range where the mixing machine speed is between the high-speed threshold and the low-speed threshold. For each equal amount of the same intermediate-type feed sample, measure the feed density at different rotation speeds by setting different rotation speeds within the medium-speed rotation speed range of the equal amount of the same intermediate-type feed sample and calculate the average value. Use the average value calculation result as the true density of the current feed after mixing at the medium-speed rotation speed of the mixing machine, denoted as Subtract the standard density ρ of the current feed b from the true density of the current feed after mixing at the medium-speed rotation speed of the mixing machine to obtain the current feed falling density.

[0029] Define the obtained current feed crushing index and the current feed falling density as input variables, divide them into different fuzzy sets respectively, and perform fuzzy reasoning according to the fuzzy sets to select the appropriate mixing machine speed.

[0030] Define the rotational speed of the mixer as the output variable and divide it into fuzzy sets;

[0031] Formulate a set of fuzzy rules to describe the influence of different input variables on the output variable;

[0032] Perform fuzzy inference according to the fuzzy rules to determine the rotational speed mode of the mixer.

[0033] A breeding management system for laying hens, used to implement the above-mentioned breeding management method for laying hens, including a data acquisition module, a data processing module, and a data storage module;

[0034] The data acquisition module is used to obtain feed data and historical data and send them to the data processing module to ensure the operation of the subsequent data processing module;

[0035] The data processing module is used to determine how to match different types of feed with different rotational speeds of the mixer according to the data collected by the data acquisition module;

[0036] The data storage module is used to store all the data generated during the processing of the laying hen breeding management system.

[0037] Technical effects and advantages of the breeding management system and management method for laying hens of the present invention:

[0038] The present invention collects feed data and historical data, classifies the feed into anti-mixed feed, easy-to-mix feed, and intermediate feed according to the feed data, calculates the adjustable-speed motor speed range according to the historical data, sets the high-speed threshold and low-speed threshold using the adjustable-speed motor speed range, matches different feeds with the speeds reaching different thresholds, marks the speeds between the high-speed threshold and the low-speed threshold as moderate speeds, collects the toughness data of the intermediate feed, classifies the intermediate feed according to toughness, the mixer uses the moderate speed to mix feeds with different toughnesses and collects feedback data, and distributes feeds with different toughnesses to different speeds of the mixer according to the feedback data. By matching feeds of different qualities with different speeds of the mixer, the damage of the feed is reduced, the cost is saved, and the production efficiency of laying hens is improved. Description of the Drawings

[0039] Figure 1 It is the first logical schematic diagram of a breeding management system for laying hens of the present invention;

[0040] Figure 2 It is the second logical schematic diagram of a breeding management system for laying hens of the present invention;

[0041] Figure 3 It is the flowchart of a breeding management method for laying hens of the present invention. Detailed Embodiments

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] The present invention classifies feeds into anti-mixed feeds, easily mixed feeds, and intermediate feeds according to feed data by collecting feed data and historical data, calculates the adjustable speed motor speed range according to historical data, sets high-speed and low-speed thresholds using the adjustable speed motor speed range, matches different feeds with speeds reaching different thresholds, marks the speed between the high-speed and low-speed thresholds as the moderate speed, collects the toughness data of intermediate feeds, classifies the intermediate feeds according to toughness, and the mixer uses the moderate speed to mix feeds with different toughnesses and collects feedback data, and distributes feeds with different toughnesses to different speeds of the mixer according to the feedback data. By matching feeds of different qualities with different speeds of the mixer, the damage of feeds is reduced, the cost is saved, and the production efficiency of laying hens is improved.

[0044] Embodiment 1. The present invention discloses a breeding management method for laying hens, as Figures 1 to 3 shown, including the following steps:

[0045] Step S1: Collect feed data and historical data.

[0046] Step S2: Classify feeds according to feed data using the self-sampling classification method, set speed thresholds according to historical data, classify the mixer speeds through the speed thresholds, and match different types of feeds with different mixer speeds.

[0047] Step S3: Collect the toughness data of feeds classified as intermediate feeds, mix the intermediate feeds using the medium speed of the mixer, and collect feedback data.

[0048] Step S4: Use fuzzy inference to distribute intermediate feeds to different speeds of the mixer by integrating the feed toughness data and the feedback data.

[0049] The specific implementation is as follows:

[0050] In step S1, the feed data includes the particle size difference of feed raw materials and the fluidity difference of feed raw materials. The particle size difference of feed raw materials can be calculated by detecting the particle sizes of each feed raw material with a particle counter. The fluidity difference of feed raw materials can be represented by calculating the flow time of feed raw materials detected by an automatic outflow time tester. The historical data is the mixer speed, which can be obtained by accessing the feed equipment database.

[0051] The particle size difference of feed raw materials refers to the difference in the particle sizes of the raw materials in the feed formulation. The greater the particle size difference of the raw materials, the more difficult it is to perform the mixing process, and the longer the time required to mix evenly in the mixer.

[0052] The poor fluidity of feed raw materials refers to the difference in the fluidity of the raw materials in the feed formulation. The greater the difference in the fluidity of the raw materials, the easier it is to produce a layering phenomenon, and the more difficult it is to mix in the mixer.

[0053] The rotation speed of the mixer refers to the operating speed of the mixer when mixing the feed raw materials. For the raw materials of a difficult-to-mix feed formula, a relatively fast mixing speed is required to mix the raw materials evenly. On the contrary, for an easily mixable feed formula, a faster speed will cause a layering phenomenon, resulting in uneven mixing of the raw materials.

[0054] It should be noted that the particle counter is an instrument that can automatically measure and count the particle sizes of raw materials. Usually, optical and electronic sensing technologies are used to measure the particle sizes of raw materials, and it can be used to measure the particle sizes of each feed raw material. The automatic outflow time tester is an instrument and equipment used to measure the outflow time of feed raw materials or other powdery materials in pipelines or funnels. It can detect and record the outflow time to evaluate the fluidity of feed raw materials. The feed equipment database is a database containing information on various feed production equipment. Relevant data of various feed production equipment can be managed and retrieved through the feed equipment database, including the rotation speed of the mixer.

[0055] In step S₂, the particle size difference of feed raw materials can be calculated using the self-sampling classification method by detecting the particle sizes of different feed raw materials with a particle counter. The specific steps are as follows:

[0056] Collect N particle sizes of the raw materials of each type of feed in the feed formulation. Combine the particle sizes of each type of feed into different data sets respectively. Randomly extract one data from each data set each time, subtract the two extracted data pairwise, and then take the average of the obtained difference data as the first particle size difference. Repeat the operation multiple times to obtain N particle size differences, and take the median of them as the particle size difference of the feed raw materials.

[0057] For example, in a certain feed formulation, the raw materials are rice, wheat, and sorghum. After measuring the particle sizes of N grains of rice, wheat, and sorghum respectively, they are combined into a rice particle size data set, a wheat particle size data set, and a sorghum particle size data set. Randomly extract one rice particle size data, one wheat particle size data, and one sorghum particle size data from the rice particle size data set, the wheat particle size data set, and the sorghum particle size data set respectively. After de-unifying, arrange them in descending order according to the numerical values and mark them as Y max 、Y medium 、Y min . Subtract the obtained numerical values pairwise: Y1 = Y max -Y medium ,Y2 = Y max -Ymin , Y3 = Y medium -Y min , where Y1, Y2, and Y3 are the results of pairwise differences of the normalized values respectively, and the average value of Y1, Y2, and Y3 is marked as Repeat the operation N times to obtain Take The median in is used as the particle size difference of the feed raw material.

[0058] Similarly, the poor fluidity of the feed raw material can be calculated and represented by the flow time of the feed raw material detected by an automatic outflow time tester. The specific steps are as follows:

[0059] First, determine the raw materials of each feed in the feed ratio. Take the same mass of each raw material and pass it through the automatic outflow time tester respectively, and record the flow time of each raw material. Make pairwise differences of the obtained data and calculate the average value as the fluidity difference of the first raw material. Change the sampling mass and repeat the operation N times to obtain N fluidity differences of the raw materials, and take the median in them as the fluidity difference of the feed raw material.

[0060] For example: In a certain feed ratio, the raw materials are soybean meal, peanut meal, and rapeseed meal. Control the soybean meal, peanut meal, and rapeseed meal to have the same sample mass and pass them through the automatic outflow time tester. The sample mass can be 50 grams. Obtain the outflow time of the soybean meal, the outflow time of the peanut meal, and the outflow time of the rapeseed meal. After normalization, arrange them in descending order of the values and mark them as T max , T medium , T min , make pairwise differences of the obtained values: T1 = T max -T medium , T2 = T max -T min , T3 = T medium -T min , where T1, T2, and T3 are the results of pairwise differences of the normalized values respectively, and the average value of T1, T2, and T3 is denoted as Change the sample mass and repeat the operation N times to obtain Take The median in is used as the particle size difference of the feed raw material. The sample mass can be selected according to the actual situation, such as 40 grams, 60 grams, etc., which will not be elaborated here.

[0061] The current feed can be classified by calculating the difference in particle size of the current feed raw materials and the difference in fluidity of the current feed raw materials and comparing them with the preset threshold values of the particle size difference and the fluidity difference of the feed raw materials. For the threshold values of the particle size difference and the fluidity difference of the feed raw materials, this example provides a setting method. The particle size difference threshold can be obtained by detecting the particle sizes of all types of raw materials in the existing feed formula and using the self-sampling classification method to get a calculation result as the threshold value of the particle size difference of the feed raw materials. Similarly, for the threshold value of the fluidity difference of the feed raw materials, by detecting the outflow times of all types of raw materials in the existing feed formula, a calculation result can be obtained for setting.

[0062] If the difference in particle size of the current feed raw materials exceeds the threshold value of the particle size difference of the feed raw materials and the difference in fluidity of the current feed raw materials exceeds the threshold value of the fluidity difference of the feed raw materials, the current feed is marked as anti-mixable feed; if the difference in particle size of the current feed raw materials is lower than the threshold value of the particle size difference of the feed raw materials and the difference in fluidity of the current feed raw materials is lower than the threshold value of the fluidity difference of the feed raw materials, the current feed is marked as easy-mixable feed; otherwise, the current feed is marked as intermediate feed.

[0063] Access the feed equipment database to obtain multiple historical rotation speeds of the mixer in the historical data and merge them into a rotation speed data set. The rotation speed threshold of the mixer can be set by calculating the average value and standard deviation of the rotation speed data set. After removing the units of the data in the rotation speed data set, the average value of all the obtained numerical values is marked as and calculate the standard deviation. The formula for calculating the standard deviation is: where z is the standard deviation, n is the total number of numerical values, i is the numerical subscript and i = 1, 2, 3, etc. The rotation speed threshold of the mixer includes a high-speed threshold and a low-speed threshold. The high-speed threshold can be set as The low-speed threshold is set as where a is the threshold proportionality coefficient. The threshold proportionality coefficient can be set according to the actual situation to adjust the high-speed threshold and the low-speed threshold. For example, if a is set to 1, the high-speed threshold and the low-speed threshold are respectively and

[0064] If the rotation speed of the mixer exceeds the high-speed threshold, it is marked as the high-speed rotation of the mixer; if the rotation speed of the mixer is lower than the low-speed threshold, it is marked as the low-speed rotation of the mixer; otherwise, the rotation speed of the mixer is marked as the medium-speed rotation of the mixer. For example, if the high-speed threshold is 70 rpm, when the rotation speed of the mixer exceeds 70 rpm, the rotation speed of the mixer is marked as the high-speed rotation of the mixer.

[0065] Match the anti-mixable feed with the high-speed rotation of the mixer; match the easy-mixable feed with the low-speed rotation of the mixer; match the intermediate feed with the medium-speed rotation of the mixer.

[0066] It should be noted that the self-sampling classification method is a machine learning classification algorithm based on resampling technology, mainly used to handle the situation where the sample size of the dataset is small. Through multiple rounds of resampling, multiple different training sets can be generated to improve the generalization ability of the model. The self-sampling classification method can be used to calculate the particle size difference and fluidity difference of feed raw materials and classify and label the feed. The calculation methods for calculating the particle size difference and fluidity difference of feed raw materials and the setting methods for various thresholds are not unique. When performing classification matching, the specific speed of the mixer can be set according to the actual situation. For example, if the current feed is an easily mixable feed and the low-speed threshold is 30 rpm, then the speed of the mixer is adjusted to below 30 rpm, such as 25, etc., which will not be elaborated here.

[0067] In step S3, collect the feed toughness data of the feed marked as intermediate feed. The feed toughness data is the feed crushing index, which can be obtained by measuring with a particle strength analyzer. After mixing the intermediate feed at the medium speed of the mixer, collect the feedback data. The feedback data is the feed falling density, which can be obtained by calculating the difference between the true density of the intermediate feed after mixing at the medium speed of the mixer and the feed standard density in the feed management system database. The true density can be detected and obtained by an electronic density meter.

[0068] The feed crushing index is an index to measure the degree of particle breakage of each raw material in the feed formula. The larger the feed crushing index, the easier it is for each raw material in the feed to be damaged. The feed crushing index can be expressed by calculating the average crushing index of each raw material in the feed formula.

[0069] The feed falling density is the difference between the density of the intermediate feed given in the feed management system database and the actual density of the intermediate feed after mixing at the medium speed of the mixer. The raw materials in the feed formula are damaged and become finer powder particles during mixing, which have a larger specific surface area and a smaller volume density compared to the raw material particles, resulting in a decrease in the overall density of the feed. The larger the feed falling density, the more serious the damage to the raw materials in the feed formula.

[0070] It should be noted that a particle strength analyzer is an instrument and equipment used to measure and evaluate the compressive strength of particulate materials. Usually, a load is applied to the particles in a compressed or extruded manner, and the load value at the time of its failure is measured and recorded. The feed management system database refers to a database system used to manage and store information related to feed production, procurement, distribution, etc. It includes various information of the feed formula, and the corresponding intermediate feed density data can be obtained from the feed management system database. An electronic density meter can detect the density of a substance in real time, record the data, and display it on the display. When the detected substance enters the measuring cell, it will change the characteristics of the electromagnetic field in the cell, thereby causing changes in the internal circuit of the instrument. It calculates the density value of the substance by detecting the changes in the internal circuit, and the density of the intermediate feed after mixing at the medium speed of the mixer can be obtained by an electronic density meter.

[0071] In step S4, if the current feed is an intermediate feed, use a particle strength analyzer to collect the crushing indices of the raw materials in the current feed formula and calculate the average value, and use the average value calculation result as the crushing index of the current feed.

[0072] Mix the current feed at a medium speed in a mixer. The specific steps for calculating the falling density of the feed are as follows:

[0073] First, access the database of the feed management system to obtain the standard density of the current feed, denoted as ρ b , take N equal - quantity samples of the same intermediate feed, select N different rotation speeds within the medium - speed rotation speed range of the mixer. The medium - speed rotation speed range of the mixer is the range where the mixer rotation speed is between the high - speed threshold and the low - speed threshold. For example, if the high - speed threshold and the low - speed threshold are set to 70 rpm and 30 rpm, then the medium - speed rotation speed range of the mixer is (30, 70). For each equal - quantity sample of the same intermediate feed, ensure that the rotation speeds are different. Measure the densities of the feeds mixed at different rotation speeds within the medium - speed rotation speed range of the mixer for each equal - quantity sample of the same intermediate feed and calculate the average value. Use the average value calculation result as the true density of the current feed after mixing at the medium - speed rotation speed of the mixer, denoted as The standard density ρ of the current feed b and the true density of the current feed after mixing at the medium - speed rotation speed of the mixer are subtracted to obtain the falling density of the current feed.

[0074] The setting of the sample quantity N is not unique. For example, in this case, when N is taken as 5, take 5 equal - quantity samples of the same intermediate feed, select 5 different rotation speeds within the medium - speed rotation speed range of the mixer. The rotation speeds are set according to the medium - speed rotation speed range (, 70) of the mixer, which can be 45 rpm, 50 rpm, 55 rpm, 60 rpm, 65 rpm, etc. This will not be elaborated here.

[0075] Define the obtained crushing index of the current feed and the falling density of the current feed as input variables, divide them into different fuzzy sets respectively, and perform fuzzy reasoning according to the fuzzy sets to select the appropriate rotation speed of the mixer.

[0076] For example, "High", "Medium", "Low" for the crushing index of the current feed, "Big", "Normal", "Small" for the falling density of the current feed.

[0077] Define the rotation speed of the mixer as the output variable and divide it into a fuzzy set. For example, "Fast", "Medium", "Slow" for the rotation speed of the mixer.

[0078] Develop a set of fuzzy rules to describe the influence of different input variables on the output variable. The definition of the rules can be based on professional knowledge or data analysis and experiments. For example:

[0079] Mark the current feed crushing index as F, the current feed falling density as X, and the mixer speed as V, then the following can be defined

[0080] Rule 1: IF (F is Low) AND (X is Small) THEN (V is Fast)

[0081] Rule 2: IF (F is Medium) AND (X is Normal) THEN (V is Medium)

[0082] Rule 3: IF (F is Hot) AND (X is High) THEN (V is Slow) ...

[0084] Based on the fuzzy rules for fuzzy inference, when the output result is Fast, use the high-speed rotation speed of the mixer to mix the current feed; when the output result is Medium, use the medium-speed rotation speed of the mixer to mix the current feed; when the output result is Slow, use the low-speed rotation speed of the mixer to mix the current feed.

[0085] It should be noted that the division of the fuzzy set can be adjusted according to the actual situation. For example, taking three fuzzy sets as an example, in fact, the current feed crushing index and the current feed falling density can be divided into more than three fuzzy sets to refine the classification. In addition, for the judgment of high, medium, and low values of the current feed crushing index and the current feed falling density, the threshold can be set according to the actual situation for judgment. For example, if the current feed crushing index exceeds 1.2, it is calibrated as "High"; if the current feed falling density exceeds 30 kg / m 3 , it is calibrated as "Big", etc., which will not be elaborated here.

[0086] Embodiment 2, the present invention also discloses a layer breeding management system, including a data acquisition module, a data processing module, and a data storage module;

[0087] The data acquisition module is used to obtain feed data and historical data and send them to the data processing module to ensure the operation of the subsequent data processing module;

[0088] The data processing module is used to determine how to match different types of feed with different rotation speeds of the mixer according to the data collected by the data acquisition module;

[0089] The data storage module is used to store all the data generated during the processing of the layer breeding management system.

[0090] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0091] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0092] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and invention constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0093] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0094] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0095] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A breeding management method for laying hens, characterized in that, It includes the following steps: Step S1, collect feed data and historical data; Step S2, classify the feed using the self - sampling classification method according to the feed data, set the rotational speed threshold according to the historical data, classify the rotational speed of the mixer through the rotational speed threshold, and match different types of feed with different rotational speeds of the mixer; Step S3, collect the feed toughness data of the feed classified as intermediate - type feed, mix the intermediate - type feed at the medium rotational speed of the mixer and collect feedback data; Step S4, allocate the intermediate - type feed to different rotational speeds of the mixer using fuzzy inference by integrating the feed toughness data and the feedback data; In step S3, collect the feed toughness data of the feed marked as intermediate - type feed. The feed toughness data is the feed crushing index. After mixing the intermediate - type feed at the medium rotational speed of the mixer, collect the feedback data, and the feedback data is the feed falling density; The feed crushing index is an index to measure the degree of particle breakage of each raw material in the feed formula; The feed falling density is the difference between the density of the intermediate - type feed given in the feed management system database and the actual density of the intermediate - type feed after mixing at the medium rotational speed of the mixer; In step S4, if the current feed is intermediate - type feed, use a particle strength analyzer to collect the average value of the crushing indexes of each raw material in the current feed formula, and take the average value calculation result as the current feed crushing index; Mix the current feed at the medium rotational speed of the mixer. The specific steps to calculate the feed falling density are as follows: First, access the feed management system database to obtain the current feed standard density, denoted as , take N equal - quantity samples of the same intermediate - type feed, select N different rotation speeds within the medium - speed rotation speed range of the mixer. The medium - speed rotation speed range of the mixer means that the rotation speed of the mixer is between the high - speed threshold and the low - speed threshold. For each equal - quantity sample of the same intermediate - type feed, measure the feed density after mixing at different rotation speeds by setting different rotation speeds within the medium - speed rotation speed range of the mixer, and calculate the average value. Record the average value calculation result as the true density of the current feed after mixing at the medium - speed rotation speed of the mixer, denoted as ; Subtract the current feed standard density from the true density of the current feed after mixing at the medium - speed rotation speed of the mixer to obtain the current feed density reduction; Define the obtained current feed crushing index and the current feed falling density as input variables, divide them into different fuzzy sets respectively, and select the rotational speed of the mixer through fuzzy inference according to the fuzzy sets; Define the rotational speed of the mixer as the output variable and divide it into a fuzzy set; Formulate a set of fuzzy rules to describe the influence of different input variables on the output variable; Conduct fuzzy inference according to the fuzzy rules to determine the rotational speed mode of the mixer.

2. A method for breeding management of laying hens according to claim 1, characterized in that: In step S1, the feed data includes the particle size difference of feed raw materials and the fluidity difference of feed raw materials, and the historical data is the rotational speed of the mixer.

3. A method for breeding management of laying hens according to claim 1, characterized in that: In step S2, the particle size difference of feed raw materials is calculated using the self - sampling classification method by detecting the particle sizes of different feed raw materials through a particle counter. The specific steps are as follows: Collect n particle sizes of the raw materials of each feed in the feed ratio, merge the particle sizes of the raw materials of each feed into different data sets respectively. Randomly extract one data from each data set each time, take the difference between the extracted data in pairs, and then take the average value of the obtained difference data as the first particle size difference. Repeat the operation multiple times to obtain n particle size differences, and take the median of them as the particle size difference of feed raw materials; The fluidity difference of feed raw materials is calculated and represented by the flow time of feed raw materials detected by an automatic outflow time tester. The specific steps are as follows: First, determine the raw materials of each feed in the feed ratio. Take the same mass of each raw material and pass it through an automatic outflow time tester respectively, and record the flow time of each raw material; Take the difference between the obtained data in pairs and calculate the average value as the first raw material fluidity difference. Repeat the operation n times by changing the sampling mass to obtain n raw material fluidity differences, and take the median among them as the feed raw material fluidity difference.

4. The method for breeding management of laying hens according to claim 2, wherein ； In step S2, classify the current feed by comparing the calculated current feed raw material particle size difference and current feed raw material fluidity difference with the preset feed raw material particle size difference threshold and feed raw material fluidity difference threshold. If the current feed raw material particle size difference exceeds the feed raw material particle size difference threshold and the current feed raw material fluidity difference exceeds the feed raw material fluidity difference threshold, mark the current feed as anti-mixing feed; If the current feed raw material particle size difference is lower than the feed raw material particle size difference threshold and the current feed raw material fluidity difference is lower than the feed raw material fluidity difference threshold, mark the current feed as easy-to-mix feed; Otherwise, mark the current feed as intermediate feed. Access the feed equipment database to obtain multiple historical speeds of the mixer in the historical data, merge them into a speed data set, set the mixer speed threshold by calculating the average value and standard deviation of the speed data set, and mark the average value of all the values obtained after de-uniting the data in the speed data set as and calculate the standard deviation z. The mixer speed threshold includes a high-speed threshold and a low-speed threshold. Set the high-speed threshold to , and set the low-speed threshold to , where is the threshold proportionality coefficient; If the mixer speed exceeds the high-speed threshold, mark it as the mixer high-speed rotation; If the mixer speed is lower than the low-speed threshold, mark it as the mixer low-speed rotation; Otherwise, mark the mixer speed as the mixer medium-speed rotation. Match the anti-mixing feed with the mixer high-speed rotation; Match the easy-to-mix feed with the mixer low-speed rotation; Match the intermediate feed with the mixer medium-speed rotation.

5. A laying hen breeding management system for implementing the laying hen breeding management method according to any one of claims 1-4, characterized in that: It includes a data acquisition module, a data processing module, and a data storage module. The data acquisition module is used to obtain feed data and historical data and send them to the data processing module to ensure the operation of the subsequent data processing module. The data processing module is used to determine how to match different types of feeds with different mixer speeds according to the data collected by the data acquisition module. The data storage module is used to store all the data generated during the processing of the laying hen breeding management system.

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