CN119294590A - A laying hen breeding capacity model and prediction method and system - Google Patents
A laying hen breeding capacity model and prediction method and system Download PDFInfo
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Abstract
本申请提供一种蛋鸡类养殖产能模型及预测方法，属于养殖技术领域，方法包括利用蛋鸡类养殖收益训练集对产能评估模型进行训练，并利用训练好的产能评估模型来确定确定鸡蛋预购价格区间。本申请具有可以较为准确的评估养殖户能在未来卖多鸡蛋，使得养殖户能够与金融个体进行产能交易的过程中，对自己的预期收益有更直观的了解，可以进一步降低养殖户的资金压力的技术效果。本申请还提供一种蛋鸡类养殖产能模型及预测系统。
The present application provides a laying hen farming capacity model and prediction method, which belongs to the field of farming technology. The method includes training the capacity assessment model using a laying hen farming income training set, and using the trained capacity assessment model to determine the pre-purchase price range of eggs. The present application has the technical effect of being able to more accurately assess how many eggs a farmer can sell in the future, so that farmers can have a more intuitive understanding of their expected returns in the process of capacity transactions with financial individuals, which can further reduce the financial pressure of farmers. The present application also provides a laying hen farming capacity model and prediction system.
Description
Technical Field
The application relates to the technical field of cultivation, in particular to a model for producing capacity of layer cultivation and a prediction method and a prediction system thereof.
Background
Regarding the production capacity trade, i.e. trade of product yield (or stock) in a future period of time, i.e. long-term contract trade, taking the egg industry as an example, farmers can sell the egg yield 180 days in the future in advance by means of long-term contract at the beginning of purchasing chicken fries. The money can be obtained in advance, the money is used for purchasing production data such as feeds, the fund turnover pressure is effectively relieved, and the cultivation efficiency and level can be further improved. This is the core meaning of the capacity trading model.
In the egg productivity transaction, the seller is a farmer, and the buyer includes a marketing company, a guarantee supply unit, and enterprise purchasing personnel. In order to ensure that farmers can fulfill the forward contracts, a method is needed to evaluate and confirm the farmer's future capacity through scientific capacity models and algorithms.
Disclosure of Invention
The embodiment of the application provides a model for the productivity of layer chicken breeding and a prediction method and a prediction system thereof, which are used for solving the problems.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a model and a prediction method for productivity of layer chicken cultivation, where the method includes:
Obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the egg yield benefit predicted by the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit;
acquiring existing laying hen breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing laying hen breeding data comprises perceived feed intake, perceived behavior characteristic data and perceived henhouse environment data acquired from an existing laying hen breeding place;
importing the existing laying hen breeding data and target time into a productivity evaluation model, and determining target egg yield benefits according to predicted egg yield benefits output by the productivity evaluation model, wherein the target egg yield benefits are egg yield benefits at the target time;
determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit;
An egg pre-purchase price interval is determined based on the maximum egg yield benefit and the minimum egg yield benefit.
With reference to the first aspect, in some embodiments, a training set of the laying hen breeding benefits is obtained, and a productivity assessment model is trained based on the training set of the laying hen breeding benefits, wherein the productivity assessment model is used for assessing the predicted egg yield benefits of the laying hen after a preset time, and the training set of the laying hen breeding benefits includes perceived feed intake, perceived behavioral characteristic data, perceived henhouse environment data, benefit time and egg yield benefits, and includes:
And acquiring a verification set based on the training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is finished according to the verification result.
With reference to the first aspect, in some embodiments, the method includes obtaining a verification set based on a training set of layer cultivation benefits, verifying a productivity assessment model based on the verification set, and determining whether the training process is completed according to a result of the verification, including:
Extracting partial data from the training set of the layer chicken breeding benefits as a verification set;
Importing the verification set into an evaluation model, and obtaining yield benefits of the verification eggs output by the evaluation model;
and comparing the yield gains of the verification eggs with the yield gains of eggs in the verification set, and determining a verification result according to the comparison result.
With reference to the first aspect, in some embodiments, comparing the validated egg yield gain with the egg yield gain in the validation set, determining the validation result based on the comparison result includes:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and if the yield gain of the verified eggs is larger than the yield gain of eggs in the verification set, acquiring an error value, wherein the error value T meets the following conditions:
T=(-)
If the yield gain of the verified eggs is smaller than the yield gain of the eggs in the verification set, an error value is obtained, and the error value T meets the following conditions:
T=(-)
Wherein, In order to verify the production of eggs,To verify the focused egg yield benefits.
With reference to the first aspect, in some embodiments, comparing the validated egg yield gain with the egg yield gain in the validation set, determining the validation result based on the comparison result includes:
And comparing the error value with a preset value, if the error value is smaller than the preset value, determining that verification is successful, and stopping the training process of the productivity assessment model.
With reference to the first aspect, in some embodiments, determining a maximum egg yield gain and a minimum egg yield gain based on the target egg yield gain comprises:
Acquiring yield benefits of the verification eggs which are output by the productivity assessment model for many times when the error value is larger than the predicted value in the process of training the productivity assessment model;
Determining a floating parameter based on the yield gain of the plurality of verification eggs, the floating parameter d satisfying:
;
Wherein n is the iteration number in the verification set iteration process, and x is the yield gain of the predicted eggs;
Obtaining predicted egg yield benefits output by the productivity assessment model, determining maximum egg yield benefits and minimum egg yield benefits based on the predicted egg yield benefits and floating parameters, and meeting the following conditions:
;
;
Wherein, In order to minimize the yield gain of eggs,Is the maximum yield gain of eggs.
With reference to the first aspect, in some embodiments, obtaining existing laying hen breeding data and a target time, where the target time is a time when a predicted yield is required, the existing laying hen breeding data including perceived feed intake, perceived behavioral characteristic data, and perceived henhouse environmental data obtained from an existing laying hen breeding place includes:
acquiring existing laying hen breeding data, wherein acquiring perception behavior feature data comprises:
Acquiring a first chicken farm image at a first moment, and confirming a plurality of individual breeding images from the first chicken farm image based on an image recognition technology;
Determining pose data of each visible cultivated individual based on the cultivated individual image, and endowing each cultivated individual with a separate mark and continuously tracking;
And acquiring the posture data of each cultured individual at the second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result.
With reference to the first aspect, in some embodiments, acquiring posture data of the cultured individual at the second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result includes:
Acquiring a posture data evaluation model, importing posture data of a first moment and posture data of a second moment into the posture data evaluation model, and determining perception behavior characteristic data according to the output result of the posture data evaluation model, wherein the posture data of the cultivated individuals comprise head-body ratio of the cultivated individuals, moving speed of the cultivated individuals and up-down movement times of the heads of the cultivated individuals in unit time.
With reference to the first aspect, in some embodiments, acquiring a posture data evaluation model, importing posture data of a first moment and posture data of a cultured individual of a second moment into the posture data evaluation model includes:
And training the posture data evaluation model based on the historical data to obtain a trained posture data evaluation model.
In a second aspect, an embodiment of the present application provides a model and a prediction system for productivity of layer farming, where the system is configured to:
Obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the egg yield benefit predicted by the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit;
acquiring existing laying hen breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing laying hen breeding data comprises perceived feed intake, perceived behavior characteristic data and perceived henhouse environment data acquired from an existing laying hen breeding place;
importing the existing laying hen breeding data and target time into a productivity evaluation model, and determining target egg yield benefits according to predicted egg yield benefits output by the productivity evaluation model, wherein the target egg yield benefits are egg yield benefits at the target time;
determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit;
An egg pre-purchase price interval is determined based on the maximum egg yield benefit and the minimum egg yield benefit.
With reference to the second aspect, in some embodiments, the system is configured to:
Obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the layer chicken predicting egg yield benefit after the productivity assessment model is used for assessing preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit, and comprises the following steps:
And acquiring a verification set based on the training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is finished according to the verification result.
With reference to the second aspect, in some embodiments, the system is configured to:
Obtaining a verification set based on a training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is completed according to the verification result, wherein the method comprises the following steps:
Extracting partial data from the training set of the layer chicken breeding benefits as a verification set;
Importing the verification set into an evaluation model, and obtaining yield benefits of the verification eggs output by the evaluation model;
and comparing the yield gains of the verification eggs with the yield gains of eggs in the verification set, and determining a verification result according to the comparison result.
With reference to the second aspect, in some embodiments, the system is configured to:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and determining a verification result according to the comparison result, wherein the method comprises the following steps:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and if the yield gain of the verified eggs is larger than the yield gain of eggs in the verification set, acquiring an error value, wherein the error value T meets the following conditions:
T=(-)
If the yield gain of the verified eggs is smaller than the yield gain of the eggs in the verification set, an error value is obtained, and the error value T meets the following conditions:
T=(-)
Wherein, In order to verify the production of eggs,To verify the focused egg yield benefits.
With reference to the second aspect, in some embodiments, the system is configured to:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and determining a verification result according to the comparison result, wherein the method comprises the following steps:
And comparing the error value with a preset value, if the error value is smaller than the preset value, determining that verification is successful, and stopping the training process of the productivity assessment model.
With reference to the second aspect, in some embodiments, the system is configured to:
determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit, comprising:
Acquiring yield benefits of the verification eggs which are output by the productivity assessment model for many times when the error value is larger than the predicted value in the process of training the productivity assessment model;
Determining a floating parameter based on the yield gain of the plurality of verification eggs, the floating parameter d satisfying:
;
Wherein n is the iteration number in the verification set iteration process, and x is the yield gain of the predicted eggs;
Obtaining predicted egg yield benefits output by the productivity assessment model, determining maximum egg yield benefits and minimum egg yield benefits based on the predicted egg yield benefits and floating parameters, and meeting the following conditions:
;
;
Wherein, In order to minimize the yield gain of eggs,Is the maximum yield gain of eggs.
With reference to the second aspect, in some embodiments, the system is configured to:
Acquiring existing laying hen breeding data and target time, wherein the target time is time for which predicted yield is required, the existing laying hen breeding data comprises perceived feed intake, perceived behavioral characteristic data and perceived henhouse environment data acquired from an existing laying hen breeding place, and the method comprises the following steps:
acquiring existing laying hen breeding data, wherein acquiring perception behavior feature data comprises:
Acquiring a first chicken farm image at a first moment, and confirming a plurality of individual breeding images from the first chicken farm image based on an image recognition technology;
Determining pose data of each visible cultivated individual based on the cultivated individual image, and endowing each cultivated individual with a separate mark and continuously tracking;
And acquiring the posture data of each cultured individual at the second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result.
With reference to the second aspect, in some embodiments, the system is configured to:
acquiring posture data of the cultured individuals at the second moment, comparing the posture data at the first moment, and determining perception behavior data according to a comparison result, wherein the method comprises the following steps:
Acquiring a posture data evaluation model, importing posture data of a first moment and posture data of a second moment into the posture data evaluation model, and determining perception behavior characteristic data according to the output result of the posture data evaluation model, wherein the posture data of the cultivated individuals comprise head-body ratio of the cultivated individuals, moving speed of the cultivated individuals and up-down movement times of the heads of the cultivated individuals in unit time.
With reference to the second aspect, in some embodiments, the system is configured to:
acquiring a posture data evaluation model, and importing posture data of a first moment and posture data of a cultured individual at a second moment into the posture data evaluation model, wherein the posture data evaluation model comprises:
And training the posture data evaluation model based on the historical data to obtain a trained posture data evaluation model.
A third aspect of an embodiment of the present invention provides an electronic device, including:
and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method set forth in the first aspect of the embodiments of the invention.
A fourth aspect of the embodiments of the present invention proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as proposed in the first aspect of the embodiments of the present invention.
In summary, the method and the device have the following technical effects:
According to the layer chicken breeding productivity model and the prediction method, the trained productivity assessment model is used for accurately assessing whether a raiser can sell more eggs in the future by acquiring the layer chicken breeding income training set and training the productivity assessment model, so that expected income of the raiser can be intuitively known in the process that the raiser can trade productivity with financial individuals, and the fund pressure of the raiser can be further reduced.
Drawings
Fig. 1 is a schematic flow chart of a model and a method for predicting productivity of layer farming according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the application provides a model and a prediction method for the productivity of layer chicken breeding, referring to fig. 1, the method comprises the following steps:
S101, obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the predicted egg yield benefit of the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit.
The core of the capacity trade is that farmers can sell eggs 180 days later in the future in advance when buying chicken fries, and take eggs in advance for buying feeds, so that the fund shortage is solved, and the cultivation level is improved. How to prove that the farmer can sell as many eggs in the future, the farmer can determine the future productivity, namely how many eggs can be sold through collecting sensing equipment (hardware) data and including algorithms through a productivity model. In this embodiment, therefore, the first and second embodiments,
In order to accurately evaluate the economic benefits of laying hen breeding, an exhaustive training set of the benefits of laying hen breeding needs to be collected and arranged. This training set will contain a series of key data to enable an efficient capacity assessment model to be trained. Specifically, this training set will include data for several aspects:
And sensing feed intake data, wherein the feed intake data records the feed intake of the laying hens in different time periods, so that the eating habit and the feeding mode of the laying hens are known. These data are critical to assessing the health and productivity of the laying hen.
And the activity rule and the activity mode of the laying hens can be better known by collecting the daily activity characteristic data of the laying hens. Such data include, but are not limited to, exercise frequency, standing time, rest time, etc. of the laying hen, which behavioral characteristics aid in assessing the health and production potential of the laying hen.
Sensing henhouse environment data, namely collecting the environment data such as temperature, humidity, illumination intensity, air quality and the like in the henhouse in order to ensure that the laying hens grow in a proper environment. These environmental factors directly affect the growth and egg laying performance of the laying hens, and therefore the importance in the training set is self-evident.
Time of return data, this part of data will record the time span from the start of the breeding of the laying hen to the laying of the egg, and the laying conditions during the different time periods. By analyzing these data, the growth rate and the egg laying period of the laying hen can be estimated, thereby predicting the future benefits more accurately.
Egg yield benefit data, which is core data of the training set, records the actual egg yield and quality of the laying hens in different time periods and economic benefit caused by the actual egg yield and quality. By analyzing these data, the productivity and the cultivation yield of the laying hens can be evaluated.
Based on the detailed training set of the layer cultivation benefits, the productivity assessment model is trained. The model will learn the production rules and income patterns of the laying hens by analyzing the data in the training set by using a machine learning algorithm. Finally, the model will be able to predict the predicted egg yield gain of the laying hen after a preset time. By the method, breeders can know future income conditions in advance, so that a more intelligent breeding decision is made, a breeding strategy is optimized, and economic benefits are improved.
In one embodiment, in order to ensure accuracy of the result, a verification set may be obtained based on the training set of the layer chicken breeding benefits, the productivity assessment model is verified based on the verification set, and whether the training process is completed is determined according to the verification result.
Specifically, firstly, partial data can be extracted from a training set of the egg raising yields as a verification set, then the verification set is imported into an evaluation model, the yield yields of the verification eggs output by the evaluation model are obtained, then the yield yields of the verification eggs are compared with the yield yields of the eggs in the verification set, and a verification result is determined according to the comparison result.
In this process, to avoid the problem of overfitting, stopping the neural network training process may be accomplished by determining the error value. In this embodiment, the yield gain of the verified eggs is compared with the yield gain of the eggs in the verification set, and if the yield gain of the verified eggs is greater than the yield gain of the eggs in the verification set, an error value is obtained, and the error value T satisfies:
T=(-)
If the yield gain of the verified eggs is smaller than the yield gain of the eggs in the verification set, an error value is obtained, and the error value T meets the following conditions:
T=(-)
Wherein, In order to verify the production of eggs,To verify the focused egg yield benefits.
By comparing the error value with the preset value, if the error value is smaller than the preset value, it can be determined that the verification is successful and the training process for the capacity assessment model is stopped.
S102, acquiring existing laying hen breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing laying hen breeding data comprises the perceived feed intake, the perceived behavioral characteristic data and the perceived henhouse environment data acquired from the existing laying hen breeding place.
In order to make a prediction of the production of laying hens, it is necessary to collect and analyze data related to the current laying hen breeding. These data include various information obtained from existing layer farms. First, it is necessary to know the target time, i.e., the specific point in time or period of time at which it is desired to predict production. This target time is a reference point for making predictions, helping to determine when a yield prediction is needed.
Next, various data of the existing laying hen breeding place need to be collected in detail. First, it is vital to sense feed intake data, which records the feed intake and feed intake of the layer in different time periods. By analyzing these data, the eating habits and eating patterns of the layers can be known, and thus factors that may affect the yield can be deduced.
Second, perceptual behavioral profile data is also indispensable. These data include the activity frequency, activity range, rest time, etc. behavior characteristics of the laying hen. By analyzing the behavior characteristics, the health condition and the production state of the laying hen can be better known, so that the future yield performance of the laying hen can be predicted.
As an embodiment, for the perceptual behavior feature data, this may be performed by means of image sensing, in particular,
Firstly, the existing laying hen breeding data needs to be acquired, specifically, the acquisition of the perception behavior characteristic data comprises the following steps:
s201, acquiring a first chicken farm image at a first moment, and confirming a plurality of individual breeding images from the first chicken farm image based on an image recognition technology.
It will be appreciated that image data from a first chicken farm is acquired at a first time and the images are analyzed using advanced image recognition techniques. By means of the techniques, image information of a plurality of breeding individuals can be identified and extracted from the image of the first chicken farm.
S202, determining posture data of each visible breeding individual based on the breeding individual image, and endowing each breeding individual with independent identification and continuously tracking.
It will be appreciated that after identifying the images of these farmed individuals, the pose data of each of the farmed individuals in view is further analyzed. To better manage and track these individuals, we assign a unique identifier to each individual cultivated and continuously monitor and record their behavior.
And S203, acquiring the posture data of each cultured individual at the second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result.
It will be appreciated that at the second moment, we again acquire the pose data of each individual cultivated and compare it in detail with the pose data acquired at the first moment. Through such comparison and analysis, we can accurately determine the perceived behavioral data of each individual cultivated, thereby better knowing their behavioral patterns and health conditions.
Specifically, as an implementation mode, a posture data evaluation model may be obtained, posture data of a first moment and posture data of a cultured individual at a second moment are imported into the posture data evaluation model, and perception behavior characteristic data is determined according to a result output by the posture data evaluation model, wherein the posture data of the cultured individual includes a head-body ratio of the cultured individual, a moving speed of the cultured individual and up-down movement times of the head of the cultured individual in a unit time. The initial model may be obtained by training the posture data evaluation model based on the historical data to obtain a trained posture data evaluation model, or may be obtained by manually inputting data, which is not limited herein.
For sensing henhouse environment data, the data cover environmental factors such as temperature, humidity, illumination intensity, ventilation condition and the like in the henhouse. These environmental factors have a direct impact on the growth and laying of the layers. By monitoring and analyzing the environmental data, the breeding environment can be timely adjusted to optimize the production performance of the laying hens.
In sum, by acquiring and analyzing the perceived feed intake data, perceived behavioral characteristic data and perceived henhouse environment data of the existing laying hen breeding place, a solid data base can be provided for the yield prediction of the laying hens. The comprehensive analysis of the data can help to more accurately predict the yield of the target time, thereby providing scientific basis for the production decision of the farm.
And S103, importing the existing laying hen breeding data and the target time into a productivity evaluation model, and determining target egg yield benefits according to the predicted egg yield benefits output by the productivity evaluation model, wherein the target egg yield benefits are egg yield benefits at the target time.
It will be appreciated that the present laying hen breeding data and the set target time are input into the productivity assessment model, and the predicted egg yield benefit can be obtained through calculation and analysis of the model. Based on these predictive data, a target egg yield benefit, i.e., an expected egg yield benefit over a set target time, may be further determined.
And S104, determining the maximum egg yield benefit and the minimum egg yield benefit based on the target egg yield benefit.
As an embodiment, for maximum egg yield benefit and minimum egg yield, one can do so by:
Acquiring yield benefits of the verification eggs which are output by the productivity assessment model for many times when the error value is larger than the predicted value in the process of training the productivity assessment model;
Determining a floating parameter based on the yield gain of the plurality of verification eggs, the floating parameter d satisfying:
;
Wherein n is the iteration number in the verification set iteration process, and x is the yield gain of the predicted eggs;
Obtaining predicted egg yield benefits output by the productivity assessment model, determining maximum egg yield benefits and minimum egg yield benefits based on the predicted egg yield benefits and floating parameters, and meeting the following conditions:
;
;
Wherein, In order to minimize the yield gain of eggs,Is the maximum yield gain of eggs.
And S105, determining an egg pre-purchase price interval based on the maximum egg yield benefit and the minimum egg yield benefit.
It will be appreciated that by analyzing and comparing the maximum egg yield gain to the minimum egg yield gain, a reasonable egg pre-purchase price interval may be determined. This price interval will be set based on the maximum and minimum yields of eggs to ensure that both parties and parties will receive fair trade conditions from them. In particular, the maximum egg yield benefit refers to the highest benefit that an egg producer can obtain under optimal production conditions, while the minimum egg yield benefit is the lowest benefit that the producer can obtain under the most adverse conditions. By comprehensively considering the two factors, a price interval can be set, so that the benefit of a producer can be guaranteed, the purchasing requirement of a consumer can be met, and the balance and stability of the market are realized.
According to the layer chicken breeding productivity model and the prediction method, the trained productivity assessment model is used for accurately assessing whether a raiser can sell more eggs in the future by acquiring the layer chicken breeding income training set and training the productivity assessment model, so that expected income of the raiser can be intuitively known in the process that the raiser can trade productivity with financial individuals, and the fund pressure of the raiser can be further reduced.
Based on the same inventive concept, the embodiment of the application also provides a model and a prediction system for the productivity of the layer chicken breeding, wherein the system is configured as follows:
Obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the egg yield benefit predicted by the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit;
acquiring existing laying hen breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing laying hen breeding data comprises perceived feed intake, perceived behavior characteristic data and perceived henhouse environment data acquired from an existing laying hen breeding place;
importing the existing laying hen breeding data and target time into a productivity evaluation model, and determining target egg yield benefits according to predicted egg yield benefits output by the productivity evaluation model, wherein the target egg yield benefits are egg yield benefits at the target time;
determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit;
An egg pre-purchase price interval is determined based on the maximum egg yield benefit and the minimum egg yield benefit.
In some embodiments, the system is configured to:
Obtaining a layer chicken breeding benefit training set, training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the layer chicken predicting egg yield benefit after the productivity assessment model is used for assessing preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit, and comprises the following steps:
And acquiring a verification set based on the training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is finished according to the verification result.
In some embodiments, the system is configured to:
Obtaining a verification set based on a training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is completed according to the verification result, wherein the method comprises the following steps:
Extracting partial data from the training set of the layer chicken breeding benefits as a verification set;
Importing the verification set into an evaluation model, and obtaining yield benefits of the verification eggs output by the evaluation model;
and comparing the yield gains of the verification eggs with the yield gains of eggs in the verification set, and determining a verification result according to the comparison result.
In some embodiments, the system is configured to:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and determining a verification result according to the comparison result, wherein the method comprises the following steps:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and if the yield gain of the verified eggs is larger than the yield gain of eggs in the verification set, acquiring an error value, wherein the error value T meets the following conditions:
T=(-)
If the yield gain of the verified eggs is smaller than the yield gain of the eggs in the verification set, an error value is obtained, and the error value T meets the following conditions:
T=(-)
Wherein, In order to verify the production of eggs,To verify the focused egg yield benefits.
In some embodiments, the system is configured to:
Comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and determining a verification result according to the comparison result, wherein the method comprises the following steps:
And comparing the error value with a preset value, if the error value is smaller than the preset value, determining that verification is successful, and stopping the training process of the productivity assessment model.
In some embodiments, the system is configured to:
determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit, comprising:
Acquiring yield benefits of the verification eggs which are output by the productivity assessment model for many times when the error value is larger than the predicted value in the process of training the productivity assessment model;
Determining a floating parameter based on the yield gain of the plurality of verification eggs, the floating parameter d satisfying:
;
Wherein n is the iteration number in the verification set iteration process, and x is the yield gain of the predicted eggs;
Obtaining predicted egg yield benefits output by the productivity assessment model, determining maximum egg yield benefits and minimum egg yield benefits based on the predicted egg yield benefits and floating parameters, and meeting the following conditions:
;
;
Wherein, In order to minimize the yield gain of eggs,Is the maximum yield gain of eggs.
In some embodiments, the system is configured to:
Acquiring existing laying hen breeding data and target time, wherein the target time is time for which predicted yield is required, the existing laying hen breeding data comprises perceived feed intake, perceived behavioral characteristic data and perceived henhouse environment data acquired from an existing laying hen breeding place, and the method comprises the following steps:
acquiring existing laying hen breeding data, wherein acquiring perception behavior feature data comprises:
Acquiring a first chicken farm image at a first moment, and confirming a plurality of individual breeding images from the first chicken farm image based on an image recognition technology;
Determining pose data of each visible cultivated individual based on the cultivated individual image, and endowing each cultivated individual with a separate mark and continuously tracking;
And acquiring the posture data of each cultured individual at the second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result.
With reference to the second aspect, in some embodiments, the system is configured to:
acquiring posture data of the cultured individuals at the second moment, comparing the posture data at the first moment, and determining perception behavior data according to a comparison result, wherein the method comprises the following steps:
Acquiring a posture data evaluation model, importing posture data of a first moment and posture data of a second moment into the posture data evaluation model, and determining perception behavior characteristic data according to the output result of the posture data evaluation model, wherein the posture data of the cultivated individuals comprise head-body ratio of the cultivated individuals, moving speed of the cultivated individuals and up-down movement times of the heads of the cultivated individuals in unit time.
In some embodiments, the system is configured to:
acquiring a posture data evaluation model, and importing posture data of a first moment and posture data of a cultured individual at a second moment into the posture data evaluation model, wherein the posture data evaluation model comprises:
And training the posture data evaluation model based on the historical data to obtain a trained posture data evaluation model.
According to the layer chicken breeding productivity model and the prediction system, the trained productivity evaluation model is used for accurately evaluating whether a raiser can sell more eggs in the future by acquiring the layer chicken breeding income training set and training the productivity evaluation model, so that expected income of the raiser can be intuitively known in the process that the raiser can trade productivity with financial individuals, and the fund pressure of the raiser can be further reduced.
Based on the same inventive concept, the embodiment of the application also provides an electronic device, which comprises:
The device comprises at least one processor, and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the model and the prediction method for the productivity of the layer cultivation.
In addition, in order to achieve the above objective, an embodiment of the present application further provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements the model and the prediction method for raising productivity of laying hens according to the embodiments of the present application.
The following describes each component of the electronic device in detail:
The processor is a control center of the electronic device, and may be one processor or a collective name of a plurality of processing elements. For example, the processor is one or more central processing units (central processing unit, CPU), or may be an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the invention, such as one or more microprocessors (DIGITALSIGNAL PROCESSOR, DSPs), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGAs).
In the alternative, the processor may perform various functions of the electronic device by executing or executing software programs stored in memory, and invoking data stored in memory.
The memory is configured to store a software program for executing the solution of the present invention, and the processor is used to control the execution of the software program, and the specific implementation manner may refer to the above method embodiment, which is not described herein again.
Alternatively, the memory may be read-only memory (ROM) or other type of static storage device that can store static information and instructions, random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electricallyerasable programmable read-only memory, EEPROM), compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation. The memory may be integral with the processor or may exist separately and be coupled to the processor through interface circuitry of the electronic device, as the embodiments of the invention are not limited in detail.
A transceiver for communicating with a network device or with a terminal device.
Alternatively, the transceiver may include a receiver and a transmitter. The receiver is used for realizing the receiving function, and the transmitter is used for realizing the transmitting function.
Alternatively, the transceiver may be integrated with the processor, or may exist separately, and be coupled to the processor through an interface circuit of the router, which is not specifically limited by the embodiment of the present invention.
In addition, the technical effects of the electronic device may refer to the technical effects of the data transmission method in the above method embodiment, which is not described herein again.
It should be appreciated that the processor in embodiments of the invention may be a central processing unit (central processing unit, CPU), which may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application Specific Integrated Circuits (ASICs), off-the-shelf programmable gate arrays (fieldprogrammable GATE ARRAY, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should also be appreciated that the memory in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (programmableROM, PROM), an erasable programmable read-only memory (erasable PROM, EPROM), an electrically erasable programmable read-only memory (electricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be random access memory (random access memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of random access memory (randomaccess memory, RAM) are available, such as static random access memory (STATIC RAM, SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (double DATA RATE SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (ENHANCEDSDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and direct memory bus random access memory (directrambus RAM, DR RAM).
The above embodiments may be implemented in whole or in part by software, hardware (e.g., circuitry), firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B, and may mean that a exists alone, while a and B exist alone, and B exists alone, wherein a and B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a, b, or c) of a, b, c, a-b, a-c, b-c, or a-b-c may be represented, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Claims (12)
1. A model and a prediction method for the productivity of layer chicken breeding are characterized in that the method comprises the following steps:
Obtaining a layer chicken breeding benefit training set, and training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the predicted egg yield benefit of the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit;
acquiring existing layer chicken breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing layer chicken breeding data comprises perceived feed intake, perceived behavioral characteristic data and perceived henhouse environment data acquired from an existing layer chicken breeding place;
Importing the existing laying hen breeding data and the target time into the productivity assessment model, and determining a target egg yield benefit according to the predicted egg yield benefit output by the productivity assessment model, wherein the target egg yield benefit is the egg yield benefit at the target time;
Determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit;
an egg pre-purchase price interval is determined based on the maximum egg yield benefit and the minimum egg yield benefit.
2. The method for predicting the yield of eggs according to claim 1, wherein a training set of yield of eggs is obtained, and a yield evaluation model is trained based on the training set of yield of eggs, wherein the yield evaluation model is used for evaluating the yield of eggs after a preset time, and the training set of yield of eggs comprises the following steps:
and acquiring a verification set based on the training set of the layer chicken breeding benefits, verifying the productivity assessment model based on the verification set, and determining whether the training process is finished according to the verification result.
3. The method according to claim 2, wherein obtaining a verification set based on the training set of yield of layer cultivation, verifying the yield evaluation model based on the verification set, and determining whether the training process is completed according to the result of verification, comprises:
extracting partial data from the training set of the layer cultivation benefits as the verification set;
importing the verification set into the assessment model, and obtaining yield benefits of verification eggs output by the assessment model;
comparing the yield gain of the verification eggs with the yield gain of the eggs in the verification set, and determining the verification result according to the comparison result.
4. A model and method for predicting productivity of laying hen farming as claimed in claim 3, wherein comparing the yield gain of the verified eggs with the yield gain of eggs in the verification set, and determining the verification result based on the comparison result comprises:
Comparing the yield gain of the verified eggs with the yield gain of the eggs in the verification set, and if the yield gain of the verified eggs is greater than the yield gain of the eggs in the verification set, obtaining an error value T, wherein the error value T meets the following conditions:
T=( -)
5. If the yield gain of the verified eggs is smaller than the yield gain of the eggs in the verification set, obtaining an error value T, wherein the error value T meets the following conditions:
T=(-)
6. wherein, For the purpose of verifying the production of eggs,Yield benefits for the eggs in the validation set.
7. The model and prediction method for egg production capacity according to claim 4, wherein comparing the verified egg yield gain with the egg yield gain in the verification set, and determining the verification result according to the comparison result comprises:
And comparing the error value with a preset value, and if the error value is smaller than the preset value, determining that verification is successful and stopping the training process of the productivity assessment model.
8. The model and the prediction method for the productivity of the layer chicken breeding according to claim 5;
wherein determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit comprises:
acquiring yield benefits of the verification eggs which are output by the productivity assessment model for many times when the error value is larger than the predicted value in the process of training the productivity assessment model;
determining a float parameter based on a plurality of said validated egg yield benefits, said float parameter d satisfying:
;
Wherein n is the iteration number in the verification set iteration process, and x is the predicted egg yield benefit;
Obtaining the predicted egg yield benefit output by the productivity assessment model, determining the maximum egg yield benefit and the minimum egg yield benefit based on the predicted egg yield benefit and the floating parameter, and meeting the following conditions:
;
;
Wherein, For the minimum egg yield benefit,Is the maximum egg yield benefit.
9. The model and method for predicting productivity of laying hen breeding according to claim 1, wherein the obtaining of the existing laying hen breeding data including the perceived feed intake, perceived behavioral characteristic data, and perceived henhouse environment data obtained from the existing laying hen breeding site and the target time, the target time being the time at which the predicted productivity is required, comprises:
acquiring the existing laying hen breeding data, wherein acquiring the perception behavior feature data comprises:
Acquiring a first chicken farm image at a first moment, and confirming a plurality of individual breeding images from the first chicken farm image based on an image recognition technology;
Determining pose data of each visible cultivated individual based on the cultivated individual image, and assigning a separate identification and continuous tracking to each cultivated individual;
And acquiring the posture data of each cultured individual at a second moment, comparing the posture data at the first moment, and determining the perception behavior data according to the comparison result.
10. The model and prediction method for laying hen breeding capacity according to claim 7, wherein the steps of obtaining the posture data of the breeding individuals at the second moment, comparing the posture data at the first moment, and determining the perceived behavior data according to the comparison result include:
Acquiring a posture data evaluation model, importing the posture data of the cultured individual at the first moment and the posture data of the cultured individual at the second moment into the posture data evaluation model, and determining the perception behavior characteristic data according to the result output by the posture data evaluation model, wherein the posture data of the cultured individual comprises the head-body ratio of the cultured individual, the moving speed of the cultured individual and the up-down movement times of the head of the cultured individual in unit time.
11. The method according to claim 8, wherein acquiring a posture data evaluation model, and importing the posture data of the first time and the posture data of the cultivated individual at the second time into the posture data evaluation model, comprises:
and training the attitude data evaluation model based on the historical data to obtain the trained attitude data evaluation model.
12. A model and a prediction method for the productivity of layer cultivation are characterized in that the system is configured to:
Obtaining a layer chicken breeding benefit training set, and training a productivity assessment model based on the layer chicken breeding benefit training set, wherein the productivity assessment model is used for assessing the predicted egg yield benefit of the layer chicken after a preset time, and the layer chicken breeding benefit training set comprises perceived feed intake, perceived behavior characteristic data, perceived henhouse environment data, benefit time and egg yield benefit;
acquiring existing layer chicken breeding data and target time, wherein the target time is the time for which the yield needs to be predicted, and the existing layer chicken breeding data comprises perceived feed intake, perceived behavioral characteristic data and perceived henhouse environment data acquired from an existing layer chicken breeding place;
Importing the existing laying hen breeding data and the target time into the productivity assessment model, and determining a target egg yield benefit according to the predicted egg yield benefit output by the productivity assessment model, wherein the target egg yield benefit is the egg yield benefit at the target time;
Determining a maximum egg yield benefit and a minimum egg yield benefit based on the target egg yield benefit;
an egg pre-purchase price interval is determined based on the maximum egg yield benefit and the minimum egg yield benefit.
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