Laying hen production data management system based on cloud computing

Abstract

The invention discloses a laying hen production data management system based on cloud computing, which belongs to the technical field of cloud computing and comprises an environment acquisition module, a cloud computing platform and a production management mode adjustment module. Wherein the environment acquisition module is used for periodically acquiring environment parameters, feed ratio data and laying hen health state data in a chicken house and transmitting the environment parameters, the feed ratio data and the laying hen health state data to the cloud computing platform in a wireless or wired mode; the cloud computing platform is used for analyzing and evaluating the collected environment parameters, feed proportion data and laying hen health state data to obtain a comprehensive evaluation value of the laying hen production environment; and the mode adjusting module is used for selecting a management mode according to the comprehensive evaluation value and dynamically adjusting laying hen production data. The laying hen production data are collected, analyzed and optimized in real time through the cloud computing platform, accurate regulation and control of the production environment are achieved, and the production benefits of laying hens are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular 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. With its powerful data storage, processing and analysis capabilities, cloud computing technology provides a new solution for laying hen production data management. Through the cloud computing platform, a large amount of data generated during laying hen breeding can be collected in real time, efficiently transmitted and intelligently analyzed, thereby achieving accurate evaluation and dynamic management of the laying hen production environment.

[0003] In the current laying hen farming industry, production data management is a crucial link. With the expansion of farming scale and the complexity of farming environment, traditional manual management methods can no longer meet the needs of modern laying hen farming. Traditional management methods have many disadvantages, such as untimely and inaccurate data collection, inefficient data analysis and processing, and a single management model and lack of flexibility. At the same time, traditional laying hen farming usually adopts a fixed farming model and feed ratio, which cannot be flexibly adjusted according to factors such as the growth status of laying hens, market demand, and farming costs. This single management model often leads to low farming 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 collects, analyzes and optimizes laying hen production data in real time through a cloud computing platform, realizes precise control of the production environment, and improves the production efficiency of laying hens.

[0005] The above objectives can be achieved through the following solutions: A laying hen production data management system based on cloud computing comprises an environment collection module, a cloud computing platform and a production management mode adjustment module; wherein the environment collection module is used to periodically collect environmental parameters, feed ratio data and laying hen health status data in a chicken house, and transmit them to the cloud computing platform in a wireless or wired manner; 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.

[0006] Optionally, the cloud computing platform includes: an evaluation model establishing unit and a comprehensive evaluation value calculating unit; wherein the evaluation model establishing unit is used to establish a comprehensive evaluation model that characterizes the comprehensive evaluation value; the comprehensive evaluation value calculating 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.

[0007] Optionally, the establishment of a comprehensive evaluation model that characterizes the comprehensive evaluation value includes: collecting 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 taking the evaluation value of the laying hen production environment as output, and using the standard evaluation data set to establish and train a neural network model to obtain a comprehensive evaluation model.

[0008] 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 used to judge the size 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; 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; the economic mode adjustment unit is used to adjust the laying hen production data using the economic mode 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.

[0009] Optionally, the use of the production model 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 value and laying hen health status data as input, and environmental parameters and feed ratio data as output, using the first training set to establish and train a neural network model to predict optimal environmental parameters and feed ratio data to obtain a first environmental prediction model; collecting current egg production target values ​​and current laying hen health status data; inputting the current egg production target value and current laying hen health status data into the first environmental prediction model to obtain first target environmental parameters and first target feed ratio data; and adjusting the chicken house environment and feed ratio according to the first target environmental parameters and the first target feed ratio data.

[0010] Optionally, the method of adjusting laying hen production data using an economic model includes: using cost data and profit data to establish an egg quantity prediction function for characterizing egg quantity prediction values; collecting historical cost data and historical profit data and preprocessing them to obtain a second training set; using the second training set to optimize the parameters of the egg quantity prediction function by the least squares method to obtain a final egg quantity prediction function.

[0011] Optionally, the method of adjusting laying hen production data using an economic model also includes: collecting current cost data and profit data to obtain an economic data set; inputting the economic data set into a final egg quantity prediction function to obtain a 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; and adjusting the chicken house environment and feed ratio according to the second target environmental parameters and the second target feed ratio data.

[0012] Optionally, the use of a health management model to adjust laying hen production data includes: collecting historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a third training set; using the laying hen health status data as input and environmental parameters and feed ratio data as output, using the third training set to establish and train a neural network model to predict optimal environmental parameters and feed ratio data to obtain a second environmental prediction model; collecting current laying hen health status data; inputting the current laying hen health status data into the second environmental prediction model to obtain third target environmental parameters and third target feed ratio data; and adjusting the chicken house environment and feed ratio according to the third target environmental parameters and the third target feed ratio data.

[0013] Optionally, the system further comprises: an optimization control module; wherein the optimization control module is used to switch to an economic mode and adjust the laying hen production data according to current laying hen health status data.

[0014] Optionally, 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, starting to obtain the current laying hen health status data in real time; judging whether the current laying hen health status data meets the preset requirements; if not, maintaining the current mode to adjust the laying hen production data; if so, switching to the economic mode to adjust the laying hen production data.

[0015] Compared with the prior art, the present invention has the following advantages: 1. Through the environment collection module, this system can periodically and comprehensively collect 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 decisions; 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 fully reflect the advantages and disadvantages of the laying hen production environment and provide managers with an intuitive and quantitative decision-making basis; 3. Based on the comprehensive evaluation value given by the cloud computing platform, the system can dynamically select the most appropriate management mode (production mode, economic mode or health management mode) to adjust the laying hen production data; this dynamic adjustment mechanism makes laying hen breeding management more flexible and efficient, and can adapt to changes in different production environments and market demands; 4. Through precise environmental control and feed ratio adjustment, this system can significantly improve the egg production and health of laying hens; at the same time, due to the use of cloud computing technology, the system's data processing and analysis capabilities have been greatly improved, allowing managers to respond more quickly to changes in the production environment and adjust management strategies in a timely manner, thereby improving breeding efficiency; 5. Under the economic mode, this system can effectively reduce the cost of laying hen farming by optimizing resource allocation and reducing unnecessary cost expenditure; at the same time, through precise environmental control and feed ratio adjustment, it reduces feed waste and disease incidence, and further reduces farming costs; 6. Through the health management model, this system can ensure the health and welfare of laying hens, reduce the incidence of diseases, and thus improve the quality and safety of eggs; in addition, through precise environmental control and feed ratio adjustment, it can also improve the egg-laying performance and egg quality of laying hens, and meet the market demand for high-quality eggs.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a framework diagram of a laying hen production data management system based on cloud computing according to an embodiment of the present invention.

[0019] Figure 2It is a structural schematic diagram of a laying hen production data management system based on cloud computing according to an embodiment of the present invention.

[0020] Figure 3 The present invention is a flowchart of an implementation of a cloud computing-based laying hen production data management system. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 An embodiment of the present invention proposes a laying hen production data management system based on cloud computing, which collects, analyzes and optimizes laying hen production data in real time through a cloud computing platform, realizes precise control of the production environment, and improves the production efficiency of laying hens.

[0023] The system of this embodiment specifically includes: an environment acquisition module, a cloud computing platform and a production management mode adjustment module; 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; Specifically, this module is responsible for periodically collecting key data in the chicken house, 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 status, etc.); the collected data is efficiently and accurately transmitted to the cloud computing platform for analysis via wireless (such as Wi-Fi, Bluetooth) or wired methods.

[0024] 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; Specifically, after receiving the data transmitted by the environment acquisition module, the cloud computing platform will analyze and evaluate the data using a pre-established comprehensive evaluation model. This model takes into account a variety of factors, can comprehensively reflect the quality of the laying hen production environment, and give a comprehensive evaluation value. The higher the value, the more ideal the laying hen production environment is, otherwise adjustments are needed.

[0025] 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.

[0026] Specifically, this module selects the most appropriate management mode to dynamically adjust laying hen production data based on the comprehensive evaluation value given by the cloud computing platform.

[0027] Alternatively, if Figure 2 As shown, 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; 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. Through a series of mathematical algorithms or machine learning techniques, multiple influencing factors (such as environmental parameters, feed ratios, laying hen health status, etc.) are comprehensively considered to output a comprehensive evaluation value; this comprehensive evaluation value can reflect the advantages and disadvantages of the laying hen production environment and provide managers with a basis for decision-making.

[0028] 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.

[0029] 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 indicating the overall quality of the production environment, or it can be a classification result, such as "excellent", "good", "average", "poor", etc.

[0030] Optionally, the establishing of a comprehensive evaluation model representing the comprehensive evaluation value includes: 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; 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 ratio of nutrients such as protein, energy, minerals, etc.), laying hen health status data (such as mortality, morbidity, egg production rate, etc.) and corresponding evaluation values ​​of the laying hen production environment; these evaluation values ​​can be obtained through expert scoring, historical records or other methods, and are used to indicate the advantages and disadvantages of different production environments.

[0031] Normalizing the data in the historical evaluation data set to obtain a standard evaluation data set; Specifically, since different data may have different dimensions and ranges, in order to improve the training effect and generalization ability of the model, the data in the historical evaluation data set needs to be normalized; normalization is to scale the data to a specific range (usually between 0 and 1) so that the model is easier to learn and process.

[0032] 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.

[0033] Specifically, a three-layer neural network model (input layer, hidden layer and output layer) can be used. The input layer contains nodes of 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 the experiment). The output layer contains one node, which represents the comprehensive evaluation value. The standard evaluation data set is used 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 iterations of training, the model will be able to accurately predict the comprehensive evaluation value of new data.

[0034] Alternatively, if Figure 2 As 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; wherein, 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; Specifically, the mode selection unit is responsible for judging the 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 needs, and are used to distinguish different production environment conditions.

[0035] 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; Specifically, when the comprehensive evaluation value is greater than the preset first threshold, the production mode adjustment unit will be started and the production mode will be used to adjust the laying hen production data; the production mode usually focuses on increasing output and economic benefits, and may take some more radical measures, such as increasing feed intake and increasing light intensity.

[0036] 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; Specifically, when the comprehensive evaluation value is less than or equal to the preset first threshold and greater than the second threshold, the economic model adjustment unit will be started, and the economic model will be used to adjust the laying hen production data; the economic model aims to balance output and cost, reduce production costs and improve economic benefits by optimizing resource allocation.

[0037] 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.

[0038] Specifically, when the comprehensive evaluation value is less than or equal to the preset second threshold, the health management mode adjustment unit will be started, and the health management mode will be used to adjust the laying hen production data; the health management mode focuses on protecting the health and welfare of laying hens, reducing the incidence of diseases by improving the production environment and feeding management, and improving the production performance and product quality of laying hens.

[0039] For example, Figure 3 As shown, it is assumed 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 pros and cons of the current production environment are judged according to the comprehensive evaluation value, and the appropriate production management mode is selected for adjustment to achieve the purpose of optimizing production, improving economic benefits and ensuring the health of laying hens.

[0040] Optionally, adjusting laying hen production data using the production mode 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; Specifically, in the production mode, in order to optimize the production environment and feed ratio of laying hens, it is first necessary to collect a large amount of historical data; these data include historical egg production target values ​​(i.e. expected egg production), historical laying hen health status data (such as mortality, morbidity, egg production rate, etc.), historical environmental parameters (such as temperature, humidity, light intensity, etc.) and historical feed ratio data (such as the ratio of nutrients such as protein, energy, and minerals); these data are organized into a data set, namely the first training set, for subsequent neural network model training.

[0041] 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; Specifically, using 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 the laying hen health status data are used as input, and the environmental parameters and feed ratio data are used as output; by training this neural network model, it can learn the complex relationship between the egg production target value and the laying hen health status data and the environmental parameters and feed ratio data; in this way, when given a new egg production target value and laying hen health status data, the model can predict the optimal environmental parameters and feed ratio data.

[0042] 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.

[0043] Specifically, the process of using the production model 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 ratios, collecting current data and inputting it into the model for prediction, and adjusting the chicken house environment and feed ratios based on the prediction results; through this process, the production environment of laying hens can be optimized, and egg production and the health of laying hens can be improved.

[0044] For example, assuming that the current egg production target is 220 eggs / 100 chickens / day, and the health status data of laying hens are 1% mortality, 4% morbidity, and 92% egg production rate; these data are input into the trained neural network model, and the model predicts that the optimal environmental parameters are temperature of 26°C, humidity of 62%, and light intensity of 16Lx, and the optimal feed ratio data are protein of 19%, energy of 3000kcal / kg, and minerals of 1.9%; based on the optimal environmental parameters and feed ratio data predicted by the model, the temperature, humidity, and light intensity of the chicken house were adjusted, and the corresponding feed was replaced; after a period of adjustment, it was observed that the egg production of laying hens increased and their health status improved.

[0045] Optionally, the adjusting laying hen production data using an economic model includes: Using cost data and profit data, an egg quantity prediction function for representing the egg quantity prediction value is established; Specifically, in the economic model, the main concern is how to balance production and cost to achieve the maximum economic benefit; therefore, it is necessary to establish an egg quantity prediction function 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 costs, water and electricity costs, labor costs, etc.) and profit data (i.e. sales revenue minus costs); the form of the egg quantity prediction function can be selected according to specific needs and data characteristics, and can be linear, quadratic or more complex nonlinear forms. The goal of the function is to output a predicted egg production value based on the input cost and profit data.

[0046] For example, it is assumed that a linear egg yield prediction function is established: , In the formula, is the predicted value of egg production, is the feed cost, Other costs (such as water and electricity costs, labor costs, etc.), is the profit target, is the parameter to be optimized.

[0047] Collect historical cost data and historical profit data and perform preprocessing to obtain a second training set; Specifically, in order to optimize the parameters of the egg quantity prediction function, a large amount of historical cost data and historical profit data need to be collected; these data record the actual cost and profit situation in the egg-laying chicken breeding process, and are the basis for establishing and optimizing the egg quantity prediction function; after collecting these data, they need to be preprocessed to ensure the accuracy and reliability of the data; the preprocessing steps may include data cleaning (removing invalid or erroneous data), missing value filling (reasonable estimation and filling of missing data), outlier processing (identifying and processing outliers to avoid negative impact on model training), etc.; the preprocessed data will constitute the second training set for subsequent parameter optimization process.

[0048] 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.

[0049] Specifically, after obtaining the second training set, the least squares method can be used to optimize the parameters of the egg quantity prediction function; the least squares method is a mathematical optimization method, which finds the optimal parameter value by minimizing the sum of squared errors between the predicted value and the actual value; in the optimization process, the cost data and profit data in the second training set are substituted into the egg quantity prediction function, and the sum of squared errors between the predicted egg production and the actual egg production is calculated; then, the parameters of the egg quantity prediction function are adjusted to minimize the sum of squared errors, thereby obtaining the optimal parameter value.

[0050] Optionally, the adjusting laying hen production data using an economic model further comprises: 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.

[0051] Specifically, after obtaining the current egg production forecast value, this forecast 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. These parameters and data will help optimize the chicken house environment and feed ratio to increase egg production and economic benefits.

[0052] For example, assume that the cost data and profit data of the current month are collected to obtain an economic data set, which is {feed cost: 1400, other costs: 380, profit: 5220}; assume that the previously optimized egg quantity prediction function is , substituting the economic data set into the egg quantity prediction function, we get pieces / month, so the current egg quantity forecast value is 2636 pieces / month; the current egg quantity forecast value of 2636 pieces / month is substituted into the first environmental prediction model, and the second target environmental temperature is 25°C, and the second target feed ratio is 18% protein; according to the second target environmental temperature of 25°C, the temperature control system in the chicken house is adjusted to ensure that the temperature in the chicken house is maintained at around 25°C; at the same time, according to the second target feed ratio (protein ratio 18%), the feed formula is adjusted 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 quantity prediction function to obtain the current egg quantity forecast value, and then inputting this forecast 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 house environment and feed ratio according to these data, the economic model adjustment of laying hen production data can be realized. This process helps to increase egg production and economic benefits, and bring greater benefits to laying hen farms.

[0053] Optionally, adjusting laying hen production data using a health management model includes: Collect historical laying hen health status data, historical environmental parameters, and historical feed ratio data to obtain a third training set; Specifically, in order to establish a neural network model that can predict the optimal environmental parameters and feed ratios, a large amount of historical data needs to be collected; these data include laying hens' health status data (such as weight, feather status, mental state, etc.), environmental parameters (such as temperature, humidity, light intensity, etc.) and feed ratio data (such as protein, fat, cellulose content, etc.); by collecting these data, a third training set containing rich information can be obtained.

[0054] Taking the laying hen health status data as input and the environmental parameters and feed ratio data as output, the third training set is used to establish and train a neural network model to predict the optimal environmental parameters and feed ratio data, thereby obtaining a second environmental prediction model; 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 input and the environmental parameters and feed ratio data as output, and predicts the optimal environmental parameters and feed ratio data under a given health state by learning the rules in historical data.

[0055] Exemplarily, it is assumed that a multi-layer perceptron (MLP) is used as a neural network model, the input layer of the model contains multiple neurons, corresponding to different dimensions of health status data; the hidden layer contains a certain number of neurons for learning nonlinear relationships in the data; the output layer contains two neurons, corresponding to the predicted values ​​of environmental parameters and feed ratio data respectively; through multiple iterative training, the model gradually learns to predict the optimal environmental parameters and feed ratio data based on the health status data, thereby obtaining a second environmental prediction model.

[0056] Collect current laying hen health status data; Specifically, in actual production, it is necessary to continuously collect data on the current health status of laying hens in order to adjust environmental parameters and feed ratios in a timely manner; these data can be obtained by regularly checking, recording and analyzing indicators such as the weight, feather status, and mental state of laying hens.

[0057] Inputting the current laying hen health status data into the second environmental prediction model to obtain third target environmental parameters and third target feed ratio data; Specifically, after obtaining the current laying hen health status data, 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.

[0058] The chicken house environment and feed ratio are adjusted according to the third target environmental parameters and the third target feed ratio data.

[0059] 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 the temperature, humidity, ventilation and other environmental factors in the chicken house, as well as changing the feed formula, a more suitable growth environment can be provided for laying hens, thereby promoting their healthy growth and increasing egg production.

[0060] For example, suppose that the second environment prediction model is established, and the health status data of the current laying hens are collected as follows: weight: 1.8kg, feather status: score 4, mental state: score 4. These data will be used as input for subsequent prediction and adjustment processes; the health status data of the current laying hens are input into the second environment prediction model. After calculation, the model outputs the third target environment temperature: 24°C, the third target feed ratio: protein ratio 19%, fat ratio 5%, cellulose ratio 8%; according to the third target environment temperature of 24°C, adjust the temperature control system in the chicken house to ensure that the temperature in the chicken house is maintained at around 24°C; at the same time, according to the third target feed ratio (protein ratio 19%, fat ratio 5%, cellulose ratio 8%), adjust the feed formula to ensure that the ratio of each component in the feed meets the requirements.

[0061] Optionally, the system further comprises: an optimization control module; wherein, 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.

[0062] Specifically, the optimization control module is an important component 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) to adjust the production data of laying hens according to the current health status data of laying hens. This intelligent switching mechanism helps to minimize breeding costs and maximize economic benefits while ensuring the health of laying hens.

[0063] Alternatively, if Figure 3 As shown, the switching to the economic mode according to the current laying hen health status data to adjust the laying hen production 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; Specifically, when the system switches to the health management mode to adjust the laying hen production data, the optimization control module will begin to obtain the current laying hen's health status data in real time; these data may include multiple indicators such as weight, feather status, mental state, and egg production rate; by real-time monitoring of these data, the system can fully understand the health status of the laying hen.

[0064] Determine whether the current laying hen health status data meets the preset requirements; 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 based on historical data, industry standards or expert experience, and are intended to ensure that the laying hens are in good health.

[0065] If not, maintain the current mode and adjust the laying hen production data; Specifically, if the current health status data of the laying hen does not meet the preset requirements, it means that the laying hen 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 hen (such as environmental parameters, feed ratios, etc.) to ensure that the laying hen can recover as soon as possible.

[0066] If yes, switch to economic mode to adjust laying hen production data.

[0067] Specifically, if the current health status data of the laying hen meets the preset requirements, it means that the laying hen is in good health; at this time, in order to reduce breeding costs and improve economic benefits, the optimization control module will switch to the economic mode and adjust the production data of the laying hen (such as reducing feed costs, optimizing environmental control strategies, etc.).

[0068] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean direct connection of the lines, and the indirect connection mode can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0069] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptation of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described 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 and a production management mode adjustment module; 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 adjustment module is used to select a management mode according to the comprehensive evaluation value and dynamically adjust the laying hen production 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 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 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.

5. The cloud computing-based laying hen production data management system according to claim 4, 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.

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 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.

7. The cloud computing-based laying hen production data management system according to claim 6, 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.

8. The cloud computing-based laying hen production data management system according to claim 4, characterized in that: 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, the third training set is used to establish and train a neural network model to predict the optimal environmental parameters and feed ratio data, thereby obtaining a second environmental prediction model; Collect current laying hen health status data; Inputting the current laying hen health status data 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.

9. The cloud computing-based laying hen production data management system according to claim 1, characterized in that: The system further comprises: an optimization control module; wherein, 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.

10. The cloud computing-based laying hen production data management system according to claim 9, 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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