CN111027822B - Method and device for determining feed type - Google Patents
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- CN111027822B CN111027822B CN201911164681.4A CN201911164681A CN111027822B CN 111027822 B CN111027822 B CN 111027822B CN 201911164681 A CN201911164681 A CN 201911164681A CN 111027822 B CN111027822 B CN 111027822B
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- 238000003307 slaughter Methods 0.000 claims description 7
- 241000282887 Suidae Species 0.000 abstract description 28
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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Abstract
The application provides a method and a device for determining feed types, wherein the method comprises the following steps: before feeding the livestock, collecting the current weight value and the current feeding time of the livestock; determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time; the current feeding time is the difference between the current time and the initial feeding time. The technical scheme can determine the feed type of the individual pigs, thereby realizing individual feeding.
Description
Technical Field
The application relates to the field of computers, in particular to a method and a device for determining feed types.
Background
The traditional breeding industry researches the characteristics of pigs through experts, carries out long-term tracking test and obtains the time node for replacing the pig feed. The feed is replaced to save the cost and ensure the rapid growth of pigs. In the traditional method, a plurality of groups of test pigs are selected randomly by an expert to carry out a large number of comparison tests, and a time node conclusion for replacing the feed is obtained by carrying out the tests under various fattening conditions.
In the prior art, the feed for a certain type of pigs is judged mainly through expert tests. From the suckling pigs to the weaning pigs, from the weaning pigs to the growing pigs and from the growing pigs to the fattening pigs, the feeding of which feeds in the stages is operated according to the conclusion obtained by expert tests.
The following problems exist in the expert guidance mode: 1) The expertise is required to obtain empirical data through experiments, the period is long, and the ordinary pig farm is difficult to ask the expertise to stay for a long time for guidance. 2) In the traditional method, a plurality of pigs are taken as a test group, and a plurality of test groups are subjected to a comparison test, so that the conclusion obtained by the test is usually aimed at a certain type of pig, and the individuation conclusion aimed at a single pig cannot be obtained. 3) For pig farms without expert guidance, the pigs in the pig farms may differ from the initial or growing condition of the pigs tested by the expert, and if the expert experience value is applied, there will generally be an error of about 10 days. 4) The accuracy of the expert's test data is limited.
Disclosure of Invention
The application aims to provide a method and a device for determining the feed type, which can determine the feed type of individual pigs, thereby realizing individual feeding.
In order to solve the technical problems, the application provides a method for determining a feed type, which comprises the following steps:
before feeding the livestock, collecting the current weight value and the current feeding time of the livestock;
determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
the current feeding time is the difference between the current time and the initial feeding time.
Optionally, the determining the feed type corresponding to the livestock according to the current weight value and the current feeding duration includes:
and inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock.
Optionally, the feed prediction model is a model obtained according to training of a feedlot to which the livestock belongs.
Optionally, the feed prediction model is trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
Optionally, the method further comprises:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
The application also provides an apparatus for determining a feed type, the apparatus comprising: a memory and a processor;
the memory is used for storing a program for determining the feed type;
the processor is used for reading and executing the program for determining the feed type, and executing the following operations:
before feeding the livestock, collecting the current weight value and the current feeding time of the livestock;
determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
the current feeding time is the difference between the current time and the initial feeding time.
Optionally, the determining the feed type corresponding to the livestock according to the current weight value and the current feeding duration includes:
and inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock.
Optionally, the feed prediction model is a model obtained according to training of a feedlot to which the livestock belongs.
Optionally, the feed prediction model is trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
Optionally, the processor is configured to read and execute the program for determining a feed type, and further perform the following operations:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
The application comprises the following steps: before feeding the livestock, collecting the current weight value and the current feeding time of the livestock; determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time; the current feeding time is the difference between the current time and the initial feeding time. The technical scheme can determine the feed type of the individual pigs, thereby realizing individual feeding.
Drawings
The accompanying drawings are included to provide an understanding of the principles of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the principles of the application.
FIG. 1 is a flow chart of a method of determining a feed type according to a first embodiment of the application;
FIG. 2 is a schematic view showing the construction of a device for determining the type of feed according to the first embodiment of the present application;
FIG. 3 is another flow chart of a method of determining a feed type according to an embodiment of the application;
fig. 4 is a training schematic diagram of a feed prediction model according to a first embodiment of the present application.
Detailed Description
The present application has been described in terms of several embodiments, but the description is illustrative and not restrictive, and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the described embodiments. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or in place of any other feature or element of any other embodiment unless specifically limited.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The disclosed embodiments, features and elements of the present application may also be combined with any conventional features or elements to form a unique inventive arrangement as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive arrangements to form another unique inventive arrangement as defined in the claims. It is therefore to be understood that any of the features shown and/or discussed in the present application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Further, various modifications and changes may be made within the scope of the appended claims.
Furthermore, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other sequences of steps are possible as will be appreciated by those of ordinary skill in the art. Accordingly, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Furthermore, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
Example 1
As shown in fig. 1, the present embodiment provides a method of determining a feed type, the method including:
step S101, collecting a current weight value and a current feeding time of livestock before feeding the livestock;
step S102, determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
the current feeding time is the difference between the current time and the initial feeding time.
Optionally, the determining the feed type corresponding to the livestock according to the current weight value and the current feeding duration may include:
and inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock.
Alternatively, the feed prediction model may be a model trained from a farm to which the livestock belongs.
Alternatively, the feed predictive model may be trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
Optionally, the method may further include:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
According to the technical scheme, the feed type of the individual pigs can be determined under the condition that expert intervention is not needed, so that individual feeding is realized.
As shown in fig. 2, the present embodiment further provides an apparatus for determining a feed type, the apparatus including: a memory 10 and a processor 11;
the memory 10 is used for storing a program for determining the feed type;
the processor 11 is configured to read and execute the program for determining a feed type, and perform the following operations:
before feeding the livestock, collecting the current weight value and the current feeding time of the livestock;
determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
the current feeding time is the difference between the current time and the initial feeding time.
Optionally, the determining the feed type corresponding to the livestock according to the current weight value and the current feeding duration may include:
and inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock.
Alternatively, the feed prediction model may be a model trained from a farm to which the livestock belongs.
Alternatively, the feed predictive model may be trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
Optionally, the processor is configured to read and execute the program for determining a feed type, and may further perform the following operations:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
According to the technical scheme, the feed type of the individual pigs can be determined under the condition that expert intervention is not needed, so that individual feeding is realized.
The method of determining the feed type according to the application will be further described below by taking a pig farm as an example, as shown in fig. 3.
Step S301, training a corresponding feed prediction model of a pig farm;
as shown in fig. 4, a schematic diagram of the training of the feed prediction model is shown, when the model is trained, a certain number of test pigs can be selected from the pig farm, and data information of each test pig in the complete process from the start of feeding to the qualified feeding of the test pigs is collected through a sensor, wherein the data information comprises weight information of each test pig and the type of feed fed each time.
The rewards per feeding can be set to be the sum of the following three contents: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length; the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice; the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1. Through the set rewards, when the trained feed prediction model aims at achieving the aim of delivering the weight, the weight increase amount is larger, the better the weight increase amount is, the smaller the feed cost is, the better the feed cost is, and the shorter the feeding date is, the better the feeding date is. The inputs of the feed prediction model are the weight data and the feeding time period of each test pig, and the output is the feed type.
For each step in each round of training, the state value network predicts rewards expected to be obtained by feeding each feed type, selects the feed type that obtains the largest predicted rewards, and saves the current state, the selected feed type, the obtained rewards and the next state to the playback memory unit. And randomly reading part of records from the playback memory unit, and carrying out gradient back propagation training model by utilizing residual errors of the target value network and the state value network. And assigning parameters of the state value network to the target value network for updating at regular intervals of training steps. After the weight value of the test pig reaches the standard weight of the slaughter, the feed prediction model finishes a round of training and returns a successful rewarding value of the feed prediction model.
Step S302, loading a feed prediction model corresponding to a feedlot to which the livestock belongs;
the trained feed prediction model of the feeding field can be loaded into a local on-line service for feeding replacement prediction of fattening pigs, so that the type of the fed feed of each pig is predicted through the feed prediction model.
Step S303, collecting a current weight value and a current feeding time of pigs before feeding the pigs;
step S304, determining the type of feed to be fed to the first pig by a feed prediction model;
in this example, the current weight of the pig and the current feeding period may be acquired by the sensor. Then, the current weight and the current feeding time of the pig are input into a pig fattening feed conversion prediction online service (the pig fattening feed conversion prediction online service is loaded with a feed prediction model), the pig fattening feed conversion prediction online service can predict the optimal feed type for the current individual pig, and then the current individual pig is fed with the determined feed type.
And step S305, feeding each pig with the determined corresponding feed type.
The technical scheme has the following beneficial technical effects:
1) In the aspect of collecting data information of test pigs, the method is different from the traditional method in that the method is used for collecting the data information of a single pig, and each pig is used as a test unit by utilizing a big data method, so that the test data can be used more efficiently. Meanwhile, by using the abnormal value detection method of big data, the problem of abnormal test data of individual pigs can be effectively avoided. Through the technical scheme, the data utilization rate can be improved, and the time period required by the test is further saved.
2) Each pig farm can train a model suitable for the pig farm, and the problem of expert knowledge deviation caused by different environments (temperature, humidity, longitude and latitude, feed and the like) is avoided, so that an optimal model can be trained for each pig farm.
3) In the example, the reinforcement learning model is trained on test data of each type of pig, and the trained model can be remotely loaded into a local system of a pig farm in a network deployment mode. Thereby solving the problem that a plurality of pig farms are difficult to invite experts to camp on site to guide the replacement of pig feed.
4) The acquisition process of the test data of this example does not require expert intervention. If the farmer wants to customize the model for his own pig farm, but no expert provides a priori knowledge to choose the initial value of the test parameters (e.g. the time frame for the test to be reloaded can be chosen initially by the expert as initial value), the system can give a suggested initial value based on the statistics accumulated by the big data. Thus, the present example can help a pig farm without expert residence to train a corresponding reinforcement learning model for the pig type of the pig farm itself.
5) When the reinforcement learning model judges which type of feed is fed to pigs, the reinforcement learning model judges each individual pig, and the reinforcement learning model transmits the judged type of feed to a feed feeding system by utilizing the characteristics of the collected individual pigs so as to feed the pigs according to the determined type of feed. Thereby realizing accurate feeding.
It should be noted that the above technical solution is also applicable to other farms, such as cattle farms, sheep farms, etc.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
Claims (6)
1. A method of determining a feed type, the method comprising:
before feeding the livestock, collecting the current weight value and the current feeding time of the livestock;
determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
wherein the current feeding time length is the difference value between the current time and the initial feeding time,
wherein, according to the current weight value and the current feeding time, determining the feed type corresponding to the livestock comprises:
inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock,
wherein, the feed prediction model is trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
2. The method of claim 1, wherein:
the feed prediction model is a model obtained according to training of a feedlot to which the livestock belong.
3. The method of any one of claims 1 to 2, further comprising:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
4. An apparatus for determining a feed type, the apparatus comprising: a memory and a processor; the method is characterized in that:
the memory is used for storing a program for determining the feed type;
the processor is used for reading and executing the program for determining the feed type, and executing the following operations:
before feeding the livestock, collecting the current weight value and the current feeding time of the livestock;
determining the type of feed to be fed to the livestock according to the current weight value and the current feeding time;
wherein the current feeding time length is the difference value between the current time and the initial feeding time,
wherein, according to the current weight value and the current feeding time, determining the feed type corresponding to the livestock comprises:
inputting the current weight value and the current feeding time into a trained feed prediction model to obtain the type of feed to be fed to the livestock,
wherein, the feed prediction model is trained by:
for a plurality of test livestock selected in the feeding field, collecting weight value data and feeding feed type information of each test livestock in the process from initial feeding to qualified slaughtering;
training to obtain a feed prediction model corresponding to the feeding farm according to the set rewards of each feeding of each experimental livestock;
wherein, the rewards of each feeding are the sum of the following three items: the weight value coefficient, the reciprocal of the feed price coefficient and the reciprocal of the natural logarithm of the feeding time length;
the weight value coefficient is the absolute value of the difference value of the weight values acquired by the livestock twice;
the price coefficient of the feed is obtained by normalizing the unit price of the fed feed to the interval of 0, 1.
5. The apparatus as set forth in claim 4, wherein:
the feed prediction model is a model obtained according to training of a feedlot to which the livestock belong.
6. The apparatus of any one of claims 4 to 5, wherein the processor is configured to read and execute the program for determining the feed type, and further perform the following operations:
the acquired identity information, weight value and feeding duration of the livestock are stored in a model training data pool;
and when the data in the model training data pool meets the set conditions, updating the feed prediction model according to the data in the model training data pool.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7369979B1 (en) * | 2005-09-12 | 2008-05-06 | John Paul Spivey | Method for characterizing and forecasting performance of wells in multilayer reservoirs having commingled production |
| CN106875034A (en) * | 2016-12-29 | 2017-06-20 | 中国农业大学 | A kind of pig-breeding multivariable feedstuff feeding decision-making technique and its system |
| CN109906999A (en) * | 2019-03-11 | 2019-06-21 | 唐人神集团股份有限公司 | A kind of home farm pig feeding method and raising set meal |
| JP2019113926A (en) * | 2017-12-21 | 2019-07-11 | 株式会社Ihi | Model predictive control device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6681717B2 (en) * | 2000-12-15 | 2004-01-27 | Can Technologies, Inc. | Computer system for determining a customized animal feed |
| WO2005072379A2 (en) * | 2004-01-28 | 2005-08-11 | Ivy Animal Health, Inc. | Method and system for collecting, managing and reporting feedlot cattle data and feed additive consumption data |
| CA2839027A1 (en) * | 2014-01-02 | 2015-07-02 | Alltech, Inc. | Systems and methods for estimating feed efficiency and carbon footprint for milk producing animal |
-
2019
- 2019-11-25 CN CN201911164681.4A patent/CN111027822B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7369979B1 (en) * | 2005-09-12 | 2008-05-06 | John Paul Spivey | Method for characterizing and forecasting performance of wells in multilayer reservoirs having commingled production |
| CN106875034A (en) * | 2016-12-29 | 2017-06-20 | 中国农业大学 | A kind of pig-breeding multivariable feedstuff feeding decision-making technique and its system |
| JP2019113926A (en) * | 2017-12-21 | 2019-07-11 | 株式会社Ihi | Model predictive control device |
| CN109906999A (en) * | 2019-03-11 | 2019-06-21 | 唐人神集团股份有限公司 | A kind of home farm pig feeding method and raising set meal |
Non-Patent Citations (2)
| Title |
|---|
| 袁军鹏 ; .一种两阶段数量组合预测方法及实证.情报学报.2014,(第09期),第16-21页. * |
| 陈宇 ; 姚敦红 ; 李石君 ; .多方面属性归一化三维张量模型在区域旅游酒店的推荐应用.科学技术与工程.2017,(第06期),第208-214页. * |
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