A laying hen production data management system based on cloud computing

Abstract

The present invention discloses a laying hen production data management system based on cloud computing, belonging to the technical field of cloud computing. It includes an environment acquisition module, a cloud computing platform, and a production management mode adjustment module. Among them, the environment acquisition module is used to periodically collect the environmental parameters, feed ratio data, and laying hen health status data in the chicken coop, and transmit them to the cloud computing platform by wireless or wired means. The cloud computing platform is used to analyze and evaluate the collected environmental parameters, feed ratio data, and laying hen health status data to obtain a comprehensive evaluation value of the laying hen production environment. The mode adjustment module is used to select a management mode according to the comprehensive evaluation value and dynamically adjust the laying hen production data. The present invention realizes the precise control of the production environment and improves the production efficiency of laying hens by collecting, analyzing, and optimizing the laying hen production data through the cloud computing platform.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and particularly to a laying hen production data management system based on cloud computing. Background Art

[0002] In recent years, with the rapid development of cloud computing technology, its application in the agricultural field has become increasingly widespread. Cloud computing technology, with its powerful data storage, processing, and analysis capabilities, provides a new solution for laying hen production data management. Through the cloud computing platform, it is possible to achieve real-time collection, efficient transmission, and intelligent analysis of a large amount of data generated during the laying hen breeding process, thereby realizing precise assessment and dynamic management of the laying hen production environment.

[0003] In the current laying hen breeding industry, production data management is a crucial link. With the expansion of the breeding scale and the complexity of the breeding environment, the traditional manual management method has been difficult to meet the needs of modern laying hen breeding. There are many drawbacks in the traditional management method, such as untimely and inaccurate data collection, low efficiency of data analysis and processing, as well as a single management mode and lack of flexibility. At the same time, traditional laying hen breeding usually adopts a fixed breeding mode and feed ratio, and cannot be flexibly adjusted according to factors such as the growth status of laying hens, market demand, and breeding costs. This single management mode often leads to low breeding efficiency, poor health of laying hens, and even affects egg production and egg quality. Summary of the Invention

[0004] To solve the above problems, the present invention provides a laying hen production data management system based on cloud computing, which realizes precise regulation of the production environment and improves the production efficiency of laying hens by collecting, analyzing, and optimizing laying hen production data through the cloud computing platform.

[0005] The above object can be achieved by the following solutions:

[0006] A laying hen production data management system based on cloud computing, comprising an environment collection module, a cloud computing platform, and a production management mode adjustment module. Among them, the environment collection module is used to periodically collect the environmental parameters, feed ratio data, and laying hen health status data in the chicken coop, and transmit them to the cloud computing platform by wireless or wired means. The cloud computing platform is used to analyze and evaluate the collected environmental parameters, feed ratio data, and laying hen health status data to obtain a comprehensive evaluation value of the laying hen production environment. The mode adjustment module is used to select a management mode according to the comprehensive evaluation value and dynamically adjust the laying hen production data.

[0007] Optionally, the cloud computing platform includes: an evaluation model establishment unit and a comprehensive evaluation value calculation unit; wherein, the evaluation model establishment unit is configured to establish a comprehensive evaluation model representing the comprehensive evaluation value; the comprehensive evaluation value calculation unit is configured to substitute environmental parameters, feed ratio data, and laying hen health status data into the comprehensive evaluation model to calculate the comprehensive evaluation value of the laying hen production environment.

[0008] Optionally, establishing the comprehensive evaluation model representing the comprehensive evaluation value includes: collecting historical environmental parameters, historical feed ratio data, historical laying hen health status data, and historical evaluation values of the laying hen production environment to construct a historical evaluation data set; performing normalization processing on the data in the historical evaluation data set to obtain a standard evaluation data set; using the environmental parameters, feed ratio data, and laying hen health status data as inputs and the evaluation value of the laying hen production environment as an output, and establishing and training a neural network model using the standard evaluation data set to obtain a comprehensive evaluation model.

[0009] Optionally, the mode adjustment module includes: a mode selection unit, a production mode adjustment unit, an economic mode adjustment unit, and a health management mode adjustment unit; wherein, the mode selection unit is configured to determine the magnitudes of the comprehensive evaluation value and a preset first threshold and a preset second threshold, and select a production management mode according to the determination result; the production mode adjustment unit is configured to, when the comprehensive evaluation value is greater than the first threshold, use the production mode to adjust the laying hen production data; the economic mode adjustment unit is configured to, when the comprehensive evaluation value is less than or equal to the first threshold and greater than the second threshold, use the economic mode to adjust the laying hen production data; the health management mode adjustment unit is configured to, when the comprehensive evaluation value is less than or equal to the second threshold, use the health management mode to adjust the laying hen production data.

[0010] Optionally, using the production mode to adjust the laying hen production data includes: collecting historical egg production target values, historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a first training set; using the egg production target values and laying hen health status data as inputs and the environmental parameters and feed ratio data as outputs, and establishing and training a neural network model using the first training set to predict the optimal environmental parameters and feed ratio data to obtain a first environmental prediction model; collecting the current egg production target value and the current laying hen health status data; inputting the current egg production target value and the current laying hen health status data into the first environmental prediction model to obtain a first target environmental parameter and a first target feed ratio data; adjusting the chicken house environment and feed ratio according to the first target environmental parameter and the first target feed ratio data.

[0011] Optionally, the adjustment of laying hen production data using the economic model includes: using cost data and profit data to establish an egg production prediction function for characterizing the predicted value of egg production; collecting historical cost data and historical profit data and performing preprocessing to obtain a second training set; using the second training set to optimize the parameters of the egg production prediction function by the least squares method to obtain the final egg production prediction function.

[0012] Optionally, the adjustment of laying hen production data using the economic model further includes: collecting current cost data and profit data to obtain an economic data set; inputting the economic data set into the final egg production prediction function to obtain the current predicted value of egg production; inputting the current predicted value of egg production into the first environmental prediction model to obtain the second target environmental parameters and the second target feed ratio data; adjusting the chicken house environment and feed ratio according to the second target environmental parameters and the second target feed ratio data.

[0013] Optionally, the adjustment of laying hen production data using the health management model includes: collecting historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a third training set; using the third training set to establish and train a neural network model with the laying hen health status data as the input and the environmental parameters and feed ratio data as the output to predict the optimal environmental parameters and feed ratio data to obtain a second environmental prediction model; collecting the current laying hen health status data; inputting the current laying hen health status data into the second environmental prediction model to obtain the third target environmental parameters and the third target feed ratio data; adjusting the chicken house environment and feed ratio according to the third target environmental parameters and the third target feed ratio data.

[0014] Optionally, the system further includes: an optimization control module; wherein, the optimization control module is used to switch to the economic model to adjust the laying hen production data according to the current laying hen health status data.

[0015] Optionally, the switching to the economic model to adjust the laying hen production data according to the current laying hen health status data includes: when switching to the health management model to adjust the laying hen production data, start to obtain the current laying hen health status data in real time; determine whether the current laying hen health status data meets the preset requirements; if not, maintain the current mode to adjust the laying hen production data; if so, switch to the economic model to adjust the laying hen production data.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. Through the environmental acquisition module, the system can periodically and comprehensively collect the environmental parameters, feed ratio data, and laying hen health status data in the chicken house; the collected data is transmitted to the cloud computing platform in real time by wireless or wired means, ensuring the timeliness and accuracy of the data and providing a solid foundation for subsequent data analysis and management decision-making;

[0018] 2. The cloud computing platform uses a pre-established comprehensive evaluation model to intelligently analyze the collected environmental parameters, feed ratio data, and laying hen health status data; through comprehensive evaluation, a comprehensive evaluation value of the laying hen production environment is obtained, which can comprehensively reflect the advantages and disadvantages of the laying hen production environment and provide intuitive and quantitative decision-making basis for managers;

[0019] 3. According to the comprehensive evaluation value given by the cloud computing platform, the system can dynamically select the most suitable management mode (production mode, economic mode, or health management mode) to adjust the laying hen production data; this dynamic adjustment mechanism makes the laying hen breeding management more flexible and efficient, and can adapt to the changes in different production environments and market demands;

[0020] 4. Through precise environmental control and feed ratio adjustment, this system can significantly improve the egg production and health status of laying hens; at the same time, due to the adoption of cloud computing technology, the data processing and analysis capabilities of the system have been greatly improved, enabling managers to respond more quickly to changes in the production environment and adjust management strategies in a timely manner, thereby improving breeding efficiency;

[0021] 5. In the economic mode, this system can effectively reduce the cost of laying hen breeding by optimizing resource allocation and reducing unnecessary cost expenditures; at the same time, through precise environmental control and feed ratio adjustment, feed waste and disease incidence are reduced, further reducing the breeding cost;

[0022] 6. Through the health management mode, this system can ensure the health and welfare of laying hens, reduce the disease incidence, thereby improving the quality and safety of eggs; in addition, through precise environmental control and feed ratio adjustment, it can also improve the egg production performance and egg quality of laying hens, meeting the market demand for high-quality eggs.

[0023] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will become apparent from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1It is a framework diagram of a laying hen production data management system based on cloud computing according to an embodiment of the present invention.

[0026] Figure 2 It is a schematic structural diagram of a laying hen production data management system based on cloud computing according to an embodiment of the present invention.

[0027] Figure 3 It is an execution flowchart of a laying hen production data management system based on cloud computing according to an embodiment of the present invention. Detailed implementation manners

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Referring to Figure 1 , an embodiment of the present invention provides a laying hen production data management system based on cloud computing, which can collect, analyze and optimize laying hen production data in real time through a cloud computing platform, realize precise control of the production environment, and improve the production efficiency of laying hens.

[0030] The system in this embodiment specifically includes: an environment collection module, a cloud computing platform, and a production management mode adjustment module; among them,

[0031] The environment collection module is used to periodically collect environmental parameters, feed ratio data, and laying hen health status data in the chicken coop, and transmit them to the cloud computing platform wirelessly or wiredly;

[0032] Specifically, this module is responsible for periodically collecting key data in the chicken coop, including environmental parameters (such as temperature, humidity, light intensity, etc.), feed ratio data (the proportion and dosage of different types of feed), and laying hen health status data (such as weight, egg production rate, disease conditions, etc.); the collected data is efficiently and accurately transmitted to the cloud computing platform for analysis wirelessly (such as Wi-Fi, Bluetooth) or wiredly.

[0033] The cloud computing platform is used to analyze and evaluate the collected environmental parameters, feed ratio data, and laying hen health status data to obtain a comprehensive evaluation value of the laying hen production environment;

[0034] Specifically, after receiving the data transmitted by the environmental acquisition module, the cloud computing platform will analyze and evaluate this data using a pre-established comprehensive evaluation model. This model takes into account various factors, can comprehensively reflect the advantages and disadvantages of the laying hen production environment, and gives a comprehensive evaluation value. The higher this value, the more ideal the laying hen production environment is. Conversely, adjustments are needed.

[0035] The mode adjustment module is used to select a management mode according to the comprehensive evaluation value and dynamically adjust the laying hen production data.

[0036] Specifically, this module selects the most suitable management mode according to the comprehensive evaluation value given by the cloud computing platform to dynamically adjust the laying hen production data.

[0037] Optionally, as Figure 2 shown, the cloud computing platform includes: an evaluation model establishment unit and a comprehensive evaluation value calculation unit; where

[0038] The evaluation model establishment unit is used to establish a comprehensive evaluation model representing the comprehensive evaluation value;

[0039] Specifically, the main task of the evaluation model establishment unit is to construct a comprehensive evaluation model, which is used to quantify the overall situation of the laying hen production environment. By a series of mathematical algorithms or machine learning techniques, multiple influencing factors (such as environmental parameters, feed ratio, laying hen health status, etc.) are comprehensively considered, and a comprehensive evaluation value is output; this comprehensive evaluation value can reflect the advantages and disadvantages of the laying hen production environment and provide a decision-making basis for managers.

[0040] The comprehensive evaluation value calculation unit is used to substitute the environmental parameters, feed ratio data, and laying hen health status data into the comprehensive evaluation model to calculate the comprehensive evaluation value of the laying hen production environment.

[0041] Specifically, the main task of the comprehensive evaluation value calculation unit is to substitute the actual environmental parameters, feed ratio data, and laying hen health status data into the established comprehensive evaluation model to calculate the comprehensive evaluation value of the laying hen production environment; this value can be a numerical value representing the overall advantages and disadvantages of the production environment, or a classification result, such as "excellent", "good", "general", "poor", etc.

[0042] Optionally, establishing the comprehensive evaluation model representing the comprehensive evaluation value includes:

[0043] Collect historical environmental parameters, historical feed ratio data, historical laying hen health status data, and historical evaluation values of the laying hen production environment to construct a historical evaluation data set;

[0044] Specifically, a large amount of historical data needs to be collected, including environmental parameters (such as temperature, humidity, light intensity, etc.), feed ratio data (such as the proportions of nutritional components like protein, energy, minerals, etc.), laying hen health status data (such as mortality rate, morbidity rate, egg production rate, etc.), and the evaluation values of the corresponding laying hen production environment; these evaluation values can be obtained through expert scoring, historical records, or other means, and are used to represent the advantages and disadvantages of different production environments.

[0045] Normalize the data in the historical evaluation dataset to obtain a standard evaluation dataset.

[0046] Specifically, since the dimensions and ranges of different data may vary, in order to improve the training effect and generalization ability of the model, it is necessary to normalize the data in the historical evaluation dataset; normalization is to scale the data to a specific range (usually between 0 and 1) so that the model can learn and process more easily.

[0047] Using the environmental parameters, feed ratio data, and laying hen health status data as inputs and the evaluation value of the laying hen production environment as the output, establish and train a neural network model using the standard evaluation dataset to obtain a comprehensive evaluation model.

[0048] Specifically, a three-layer neural network model (input layer, hidden layer, and output layer) can be used. The input layer contains the number of nodes for environmental parameters, feed ratio data, and laying hen health status data. The hidden layer can contain several nodes (the specific number needs to be adjusted according to experiments), and the output layer contains one node representing the comprehensive evaluation value; use the standard evaluation dataset to train this neural network model; during the training process, the model will continuously adjust its internal parameters (such as weights and biases) to minimize the error between the predicted evaluation value and the true evaluation value; after multiple iterative trainings, the model will be able to accurately predict the comprehensive evaluation value of new data.

[0049] Optionally, as Figure 2 shown, the mode adjustment module includes: a mode selection unit, a production mode adjustment unit, an economic mode adjustment unit, and a health management mode adjustment unit; among them,

[0050] The mode selection unit is used to judge the magnitudes of the comprehensive evaluation value and a preset first threshold and a preset second threshold, and select a production management mode according to the judgment result.

[0051] Specifically, the mode selection unit is responsible for judging the magnitude relationship between the comprehensive evaluation value and the preset first threshold and second threshold, and selecting the corresponding production management mode according to the judgment result; the preset first threshold and second threshold are set according to actual production experience and requirements, and are used to distinguish different production environment conditions.

[0052] The production mode adjustment unit is used to adjust the production data of laying hens using the production mode when the comprehensive evaluation value is greater than the first threshold.

[0053] Specifically, when the comprehensive evaluation value is greater than the preset first threshold, the production mode adjustment unit will be activated to adjust the production data of laying hens using the production mode. The production mode usually focuses on increasing production and economic benefits and may take some relatively radical measures, such as increasing the feed feeding amount and raising the light intensity.

[0054] The economic mode adjustment unit is used to adjust the production data of laying hens using the economic mode when the comprehensive evaluation value is less than or equal to the first threshold and greater than the second threshold.

[0055] Specifically, when the comprehensive evaluation value is less than or equal to the preset first threshold and greater than the second threshold, the economic mode adjustment unit will be activated to adjust the production data of laying hens using the economic mode. The economic mode aims to balance production and costs and reduce production costs and improve economic benefits by optimizing resource allocation.

[0056] The health management mode adjustment unit is used to adjust the production data of laying hens using the health management mode when the comprehensive evaluation value is less than or equal to the second threshold.

[0057] Specifically, when the comprehensive evaluation value is less than or equal to the preset second threshold, the health management mode adjustment unit will be activated to adjust the production data of laying hens using the health management mode. The health management mode focuses on ensuring the health and welfare of laying hens and reducing the disease incidence rate and improving the production performance and product quality of laying hens by improving the production environment and feeding management.

[0058] Exemplarily, as Figure 3 shown, assume that the preset first threshold is 80 points and the second threshold is 60 points. When the comprehensive evaluation value is 90 points, which is greater than the first threshold, the mode selection unit will select the production mode. When the comprehensive evaluation value is 70 points, which is less than or equal to the first threshold and greater than the second threshold, the economic mode will be selected. When the comprehensive evaluation value is 50 points, which is less than or equal to the second threshold, the health management mode will be selected. The quality of the current production environment is judged based on the comprehensive evaluation value, and a suitable production management mode is selected for adjustment to achieve the purpose of optimizing production, improving economic benefits, and ensuring the health of laying hens.

[0059] Optionally, the adjustment of the production data of laying hens using the production mode includes:

[0060] Collecting the historical egg production target value, historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain the first training set.

[0061] Specifically, in the production mode, in order to optimize the production environment and feed ratio of laying hens, a large amount of historical data needs to be collected first. This data includes historical egg production target values (i.e., the expected egg production), historical laying hen health status data (such as mortality rate, morbidity rate, egg production rate, etc.), historical environmental parameters (such as temperature, humidity, light intensity, etc.), and historical feed ratio data (such as the proportions of nutritional components such as protein, energy, minerals, etc.). Organize this data into a data set, namely the first training set, for subsequent neural network model training.

[0062] Using the egg production target value and laying hen health status data as inputs, and the environmental parameters and feed ratio data as outputs, establish and train a neural network model with the first training set to predict the optimal environmental parameters and feed ratio data, and obtain the first environmental prediction model.

[0063] Specifically, with the first training set, a neural network model can be established to predict the optimal environmental parameters and feed ratio data. In this model, the egg production target value and laying hen health status data serve as inputs, and the environmental parameters and feed ratio data serve as outputs. By training this neural network model, it can learn the complex relationships between the egg production target value and laying hen health status data and the environmental parameters and feed ratio data. In this way, when given new egg production target values and laying hen health status data, the model can predict the optimal environmental parameters and feed ratio data.

[0064] Collect the current egg production target value and the current laying hen health status data.

[0065] Input the current egg production target value and the current laying hen health status data into the first environmental prediction model to obtain the first target environmental parameters and the first target feed ratio data.

[0066] Adjust the chicken house environment and feed ratio according to the first target environmental parameters and the first target feed ratio data.

[0067] Specifically, the process of using the production mode to adjust laying hen production data includes collecting historical data to construct the first training set, establishing and training a neural network model to predict the optimal environmental parameters and feed ratio, collecting current data and inputting it into the model for prediction, and adjusting the chicken house environment and feed ratio according to the prediction results. Through this process, the production environment of laying hens can be optimized, and the egg production and the health status of laying hens can be improved.

[0068] Exemplarily, assume that the current egg production target value is 220 eggs / 100 hens / day, and the health status data of laying hens is a mortality rate of 1%, an incidence rate of 4%, and a laying rate of 92%; input these data into the trained neural network model, and the model predicts the optimal environmental parameters as a temperature of 26°C, a humidity of 62%, and a light intensity of 16 Lx. The optimal feed ratio data is 19% protein, 3000 kcal / kg of energy, and 1.9% minerals; according to the optimal environmental parameters and feed ratio data predicted by the model, the temperature, humidity, and light intensity of the chicken house are adjusted, and the corresponding feed is replaced; after a period of adjustment, it is observed that the egg production of laying hens has increased and the health status has also improved.

[0069] Optionally, the adjustment of laying hen production data using the economic model includes:

[0070] Using cost data and profit data, establish an egg production prediction function for characterizing the predicted egg production value;

[0071] Specifically, in the economic model, the main concern is how to balance production and cost to achieve the maximum economic benefit; therefore, an egg production prediction function needs to be established to predict the egg production of laying hens, which is used to predict the egg production of laying hens under a given cost structure and profit target; this function can be established based on cost data (such as feed cost, water and electricity cost, labor cost, etc.) and profit data (i.e., sales revenue minus cost); the form of the egg production prediction function can be selected according to specific requirements and data characteristics, and it can be linear, quadratic, or more complex non-linear forms. The goal of the function is to output a predicted egg production value based on the input cost and profit data.

[0072] Exemplarily, assume that a linear egg production prediction function is established:

[0073] ,

[0074] In the formula, is the predicted egg production value, is the feed cost, is other costs (such as water and electricity cost, labor cost, etc.), is the profit target, is the parameter to be optimized.

[0075] Collect historical cost data and historical profit data and perform preprocessing to obtain the second training set;

[0076] Specifically, to optimize the parameters of the egg production prediction function, a large amount of historical cost data and historical profit data need to be collected; these data record the actual costs and profits during the laying hen farming process and serve as the basis for establishing and optimizing the egg production prediction function; after collecting these data, preprocessing is required to ensure the accuracy and reliability of the data; the preprocessing steps may include data cleaning (removing invalid or incorrect data), filling in missing values (reasonably estimating and filling in missing data), handling outliers (identifying and handling outliers to avoid negative impacts on model training), etc.; the preprocessed data will form the second training set for subsequent parameter optimization.

[0077] Using the second training set, optimize the parameters of the egg production prediction function by the least squares method to obtain the final egg production prediction function.

[0078] Specifically, after obtaining the second training set, the parameters of the egg production prediction function can be optimized using the least squares method; the least squares method is a mathematical optimization method that finds the optimal parameter values by minimizing the sum of the squared errors between the predicted values and the actual values; during the optimization process, substitute the cost data and profit data in the second training set into the egg production prediction function and calculate the sum of the squared errors between the predicted egg production and the actual egg production; then, by adjusting the parameters of the egg production prediction function, minimize the sum of the squared errors to obtain the optimal parameter values.

[0079] Optionally, the adjustment of laying hen production data using the economic model further includes:

[0080] Collect the current cost data and profit data to obtain an economic data set;

[0081] Input the economic data set into the final egg production prediction function to obtain the current egg production prediction value;

[0082] Input the current egg production prediction value into the first environmental prediction model to obtain the second target environmental parameters and the second target feed ratio data;

[0083] Adjust the chicken house environment and feed ratio according to the second target environmental parameters and the second target feed ratio data.

[0084] Specifically, after obtaining the current egg production prediction value, this prediction value can be input into the previously established environmental prediction model; this model will output the second target environmental parameters and the second target feed ratio data, which will help optimize the chicken house environment and feed ratio to increase egg production and economic benefits.

[0085] Exemplarily, assume that the cost data and profit data for the current month are collected to obtain an economic data set, and the economic data set is {feed cost: 1400, other costs: 380, profit: 5220}; assume that the previously optimized egg production prediction function is , substitute the economic data set into the egg production prediction function to obtain eggs / month. Therefore, the current predicted egg production value is 2636 eggs / month; substitute the current predicted egg production value of 2636 eggs / month into the first environmental prediction model to obtain a second target environmental temperature of 25°C and a second target feed ratio of 18% protein ratio; according to the second target environmental temperature of 25°C, adjust the temperature control system in the chicken coop to ensure that the temperature in the chicken coop remains around 25°C; at the same time, according to the second target feed ratio (18% protein ratio), adjust the feed formula to ensure that the protein ratio in the feed reaches 18%; by collecting the current cost data and profit data, inputting them into the egg production prediction function to obtain the current predicted egg production value, and then inputting this predicted value into the environmental prediction model to obtain the second target environmental parameters and the second target feed ratio data, and finally adjusting the chicken coop environment and feed ratio according to these data, the economic mode adjustment of the laying hen production data can be realized. This process helps to increase egg production and economic benefits, bringing greater benefits to the laying hen farm.

[0086] Optionally, the adjustment of the laying hen production data using the health management mode includes:

[0087] Collect historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a third training set;

[0088] Specifically, in order to establish a neural network model that can predict the optimal environmental parameters and feed ratio, a large amount of historical data needs to be collected; this data includes the health status data of laying hens (such as body weight, feather status, mental state, etc.), environmental parameters (such as temperature, humidity, light intensity, etc.), and feed ratio data (such as the content of protein, fat, cellulose, etc.); by collecting this data, a third training set containing rich information can be obtained.

[0089] Using the laying hen health status data as the input and the environmental parameters and feed ratio data as the output, use the third training set to establish and train a neural network model to predict the optimal environmental parameters and feed ratio data, and obtain a second environmental prediction model;

[0090] Specifically, after obtaining the third training set, these data can be used to establish and train a neural network model; this model uses the health status data of laying hens as the input and the environmental parameters and feed ratio data as the output, and by learning the laws in the historical data, predicts the optimal environmental parameters and feed ratio data under a given health status.

[0091] Exemplarily, it is assumed that a multi-layer perceptron (MLP) is used as the neural network model. The input layer of the model contains multiple neurons corresponding to different dimensions of the health status data; the hidden layer contains a certain number of neurons for learning the non-linear relationships in the data; the output layer contains two neurons corresponding to the predicted values of the environmental parameters and the feed ratio data respectively. Through multiple iterations of training, the model gradually learns to predict the optimal environmental parameters and feed ratio data based on the health status data, thereby obtaining the second environmental prediction model.

[0092] Collect the current health status data of laying hens;

[0093] Specifically, in actual production, it is necessary to continuously collect the current health status data of laying hens in order to timely adjust the environmental parameters and feed ratio; these data can be obtained by regularly checking, recording, and analyzing indicators such as the body weight, feather status, and mental state of laying hens.

[0094] Input the current health status data of laying hens into the second environmental prediction model to obtain the third target environmental parameters and the third target feed ratio data;

[0095] Specifically, after obtaining the current health status data of laying hens, this data can be input into the second environmental prediction model; the model will predict the optimal environmental parameters and feed ratio data under the current health status based on the input data.

[0096] Adjust the chicken house environment and feed ratio according to the third target environmental parameters and the third target feed ratio data.

[0097] Specifically, after obtaining the third target environmental parameters and the third target feed ratio data, the chicken house environment and feed ratio will be adjusted according to these data; by adjusting environmental factors such as temperature, humidity, and ventilation in the chicken house, and changing the feed formula, a more suitable growth environment can be provided for laying hens, thereby promoting their healthy growth and increasing egg production.

[0098] Exemplarily, assume that a second environmental prediction model is established, and the collected health status data of the current laying hens are: body weight: 1.8 kg, feather status: score 4, mental state: score 4. These data will be used as inputs for subsequent prediction and adjustment processes; the health status data of the current laying hens are input into the second environmental prediction model. After calculation, the model outputs the third target environmental temperature: 24°C, and the third target feed ratio: protein ratio 19%, fat ratio 5%, cellulose ratio 8%; according to the third target environmental temperature of 24°C, the temperature control system in the chicken house is adjusted to ensure that the temperature in the chicken house remains around 24°C; at the same time, according to the third target feed ratio (protein ratio 19%, fat ratio 5%, cellulose ratio 8%), the feed formula is adjusted to ensure that the proportion of each component in the feed meets the requirements.

[0099] Optionally, the system further includes: an optimization control module; wherein,

[0100] The optimization control module is used to switch to the economic mode according to the current health status data of the laying hens to adjust the production data of the laying hens.

[0101] Specifically, the optimization control module is an important part of the laying hen breeding management system. Its main function is to intelligently switch to different management modes (such as health management mode or economic mode) according to the current health status data of the laying hens to adjust the production data of the laying hens; this intelligent switching mechanism helps to minimize the breeding cost and maximize the economic benefits while ensuring the health of the laying hens.

[0102] Optionally, as Figure 3 shown, the switching to the economic mode according to the current health status data of the laying hens to adjust the production data of the laying hens includes:

[0103] When switching to the health management mode to adjust the production data of the laying hens, start to obtain the current health status data of the laying hens in real time;

[0104] Specifically, when the system switches to the health management mode to adjust the production data of the laying hens, the optimization control module will start to obtain the current health status data of the laying hens in real time; these data may include multiple indicators such as body weight, feather status, mental state, and egg production rate; by monitoring these data in real time, the system can comprehensively understand the health status of the laying hens.

[0105] Judge whether the current health status data of the laying hens meet the preset requirements;

[0106] Specifically, after obtaining the current health status data of the laying hens, the optimization control module will compare these data with the preset health standards; the preset health standards may be formulated based on historical data, industry standards or expert experience, aiming to ensure that the laying hens are in good health.

[0107] If not, maintain the current mode to adjust the production data of laying hens;

[0108] Specifically, if the health status data of the current laying hens does not meet the preset requirements, it indicates that the laying hens may be in a sub-healthy or unhealthy state; at this time, the optimization control module will maintain the current health management mode and continue to adjust the production data of the laying hens (such as environmental parameters, feed ratio, etc.) to ensure that the laying hens can recover health as soon as possible.

[0109] If so, switch to the economic mode to adjust the production data of laying hens.

[0110] Specifically, if the health status data of the current laying hens meets the preset requirements, it indicates that the laying hens are in a good health state; at this time, in order to reduce the breeding cost and improve the economic benefit, the optimization control module will switch to the economic mode and adjust the production data of the laying hens (such as reducing the feed cost, optimizing the environmental control strategy, etc.).

[0111] It should be noted that the electrical connections between the above-mentioned various units do not necessarily mean direct connection of the circuits. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above are only exemplary embodiments of the present invention and cannot be used to limit the scope of the present invention.

[0112] That is, any equivalent changes and modifications made in accordance with the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will easily think of other implementation schemes of the present invention after considering the specification and the disclosure of the practice. This application aims to cover any variations, uses or adaptive changes of the present invention, and these variations, uses or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not recorded in the present invention.

Claims

1. A laying hen production data management system based on cloud computing, characterized in that: The system includes: an environment acquisition module, a cloud computing platform, an optimization control module and a production management mode adjustment module, wherein the mode adjustment module includes a mode selection unit, a production mode adjustment unit, an economic mode adjustment unit and a health management mode adjustment unit; wherein, The environment collection module is used to periodically collect environmental parameters, feed ratio data and laying hen health status data in the chicken house, and transmit them to the cloud computing platform wirelessly or wiredly; The cloud computing platform is used to analyze and evaluate the collected environmental parameters, feed ratio data and laying hen health status data to obtain a comprehensive evaluation value of the laying hen production environment; The mode selection unit is used to determine the magnitude of the comprehensive evaluation value and the preset first threshold and the preset second threshold, and select the production management mode according to the determination result; The production mode adjustment unit is used to adjust the laying hen production data using the production mode when the comprehensive evaluation value is greater than the first threshold value; The economic model adjustment unit is used to adjust the laying hen production data using the economic model when the comprehensive evaluation value is less than or equal to the first threshold and greater than the second threshold; The health management mode adjustment unit is used to adjust the laying hen production data using the health management mode when the comprehensive evaluation value is less than or equal to the second threshold; The optimization control module is used to switch to the economic mode and adjust the laying hen production data according to the current laying hen health status data; Wherein, the use of the health management model to adjust laying hen production data includes: Collect historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a third training set; Taking the laying hen health status data as input and the environmental parameters and feed ratio data as output, a neural network model is established and trained using the third training set to obtain a second environmental prediction model; Collecting current laying hen health status data and inputting it into the second environmental prediction model to obtain third target environmental parameters and third target feed ratio data; The chicken house environment and feed ratio are adjusted according to the third target environmental parameters and the third target feed ratio data.

2. A cloud computing-based laying hen production data management system according to claim 1, characterized in that: The cloud computing platform includes: an evaluation model building unit and a comprehensive evaluation value calculation unit; wherein, The evaluation model building unit is used to build a comprehensive evaluation model that represents the comprehensive evaluation value; The comprehensive evaluation value calculation unit is used to substitute environmental parameters, feed ratio data and laying hen health status data into the comprehensive evaluation model to calculate the comprehensive evaluation value of the laying hen production environment.

3. A cloud computing-based laying hen production data management system according to claim 2, characterized in that: The establishing of a comprehensive evaluation model representing the comprehensive evaluation value comprises: Collect historical environmental parameters, historical feed ratio data, historical laying hen health status data and historical evaluation values ​​of laying hen production environment to construct a historical evaluation data set; Normalizing the data in the historical evaluation data set to obtain a standard evaluation data set; Taking environmental parameters, feed ratio data and laying hen health status data as input and the evaluation value of laying hen production environment as output, a neural network model is established and trained using the standard evaluation data set to obtain a comprehensive evaluation model.

4. The cloud computing-based laying hen production data management system according to claim 1, characterized in that: The method of adjusting laying hen production data by using the production model includes: Collect historical egg production target values, historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain the first training set; Taking the egg production target value and laying hen health status data as input, and taking the environmental parameters and feed ratio data as output, using the first training set to establish and train a neural network model to predict the optimal environmental parameters and feed ratio data, and obtain a first environmental prediction model; Collect current egg production target value and current laying hen health status data; Inputting the current egg production target value and the current laying hen health status data into the first environmental prediction model to obtain the first target environmental parameter and the first target feed ratio data; The chicken house environment and feed ratio are adjusted according to the first target environmental parameters and the first target feed ratio data.

5. The cloud computing-based laying hen production data management system according to claim 4, characterized in that: The use of economic models to adjust laying hen production data includes: Using cost data and profit data, an egg quantity prediction function for representing the egg quantity prediction value is established; Collect historical cost data and historical profit data and perform preprocessing to obtain a second training set; The second training set is used to optimize the parameters of the egg quantity prediction function by the least square method to obtain a final egg quantity prediction function.

6. The cloud computing-based laying hen production data management system according to claim 5, characterized in that: The use of economic models to adjust laying hen production data also includes: Collect current cost data and profit data to obtain economic data sets; Inputting the economic data set into the final egg quantity prediction function to obtain the current egg quantity prediction value; Inputting the current egg quantity prediction value into the first environmental prediction model to obtain second target environmental parameters and second target feed ratio data; The chicken house environment and feed ratio are adjusted according to the second target environmental parameters and the second target feed ratio data.

7. The cloud computing-based laying hen production data management system according to claim 1, characterized in that: The switching to the economic mode to adjust the laying hen production data according to the current laying hen health status data includes: When switching to the health management mode to adjust the laying hen production data, start to obtain the current laying hen health status data in real time; Determine whether the current laying hen health status data meets the preset requirements; If not, maintain the current mode and adjust the laying hen production data; If yes, switch to economic mode to adjust laying hen production data.

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