Animal feeding method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides an animal feeding method, an electronic device, a storage medium and a program product, and the method comprises the steps: firstly determining a comprehensive feeding strategy as a general direction guide based on an expected feeding target of a period of time in the future, and then aiming at each subdivision time period in the period of time, according to the specific health behavior data of each animal, determining a comprehensive feeding strategy for the future period of time; the parameters of the feeding device are finely adjusted to ensure that the feeding of animals by the feeding device conforms to the established feeding strategy, so that the full-automatic process of animal feeding from planning to execution is realized, the dependence on manual decision making and manual operation of the feeding device is reduced, and the overall efficiency of breeding is remarkably improved while the feeding quality is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of breeding technology, and in particular to an animal breeding method, electronic equipment, storage medium and program product. Background Art

[0002] With the growth of population and the improvement of living standards, people's demand for animal products is increasing. In order to meet this demand, modern animal husbandry is constantly pursuing efficient, environmentally friendly and sustainable development models. This requires the animal husbandry industry to improve production efficiency, reduce production costs, and ensure the quality and safety of animal products. However, current animal husbandry is still highly dependent on manual judgment and operation, which restricts the modern animal husbandry industry from moving towards efficient and precise management. Therefore, it is particularly urgent to develop a method that can improve the level of automation and efficiency of animal husbandry. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide an animal breeding method, an electronic device, a storage medium and a program product to achieve the technical effect of improving the efficiency of animal breeding.

[0004] In a first aspect, an embodiment of the present application provides an animal breeding method, the method comprising: Acquire a feeding target, and generate a feeding strategy within a preset time period based on the feeding target; For each target time period within the preset time period, based on the historical feeding management data of the animal, determining feeding parameters that meet the feeding strategy within the target time period; the historical feeding management data is used to characterize the growth and health of the animal within the historical time period; the historical time period is before the target time period; The feeding device is controlled to feed the animals according to the feeding parameters.

[0005] In the above implementation process, firstly, based on the expected feeding goals for a period of time in the future, a comprehensive feeding strategy is determined as a general direction guide. Then, for each subdivided period within this time period, the parameters of the feeding device are finely adjusted according to the specific health behavior data of each animal to ensure that the feeding of animals by the feeding device complies with the established feeding strategy. This not only realizes the fully automated process of animal feeding from planning to execution, but also reduces the dependence on manual decision-making and manual operation of feeding devices, significantly improving the overall efficiency of breeding while ensuring the quality of feeding.

[0006] Furthermore, generating a feeding strategy within a preset time period based on the feeding target includes: Inputting the feeding target into a trained feeding strategy decision model to obtain the feeding strategy output by the feeding strategy decision model; The step of determining feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: The historical feeding management data and the feeding strategy are input into a trained feeding regulation model to obtain the feeding parameters output by the feeding regulation model.

[0007] In the above implementation process, the feeding target is input into the trained feeding strategy decision model to automatically generate the feeding strategy; then the historical feeding management data of the animal is combined with the trained feeding control model to accurately output the feeding parameters that meet the feeding strategy. This process realizes the intelligentization and automation of feeding management, and significantly improves the efficiency and accuracy of breeding.

[0008] Furthermore, the feeding regulation model and the feeding strategy decision model are trained by the following steps: Acquire a first training data set, where the first training data set includes first feeding management training data collected historically and a first feeding parameter label corresponding to the first feeding management training data; Using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model; Acquire a second training data set; the second training data set includes second feeding management training data collected historically and feeding target data input historically; Inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data; The feeding strategy decision model is supervisedly trained using the second training data set carrying the second feeding parameter label to obtain the trained feeding strategy decision model.

[0009] In the above implementation process, first, a feeding control model with feeding parameter inference capability is trained using a first training data set containing historical feeding management training data and its corresponding feeding parameters. Subsequently, by introducing a second training data set containing feeding management training data and feeding target data, and combining the feeding parameter labels output by the trained feeding control model, the feeding strategy decision model is further trained so that the feeding strategy decision model can generate a reasonable feeding strategy based on the feeding target. This process realizes the effective training of the two models, providing a solid foundation for the subsequent intelligent feeding strategy formulation and parameter control, thereby improving the automation level of breeding management and the accuracy of decision-making.

[0010] Further, the first feeding management training data and the second feeding management training data both include feeding management training data of preset types, each type of the first feeding management training data corresponds to a first feeding parameter label; each type of the second feeding management training data corresponds to a second feeding parameter label; the feeding device includes multiple types, each of the feeding devices is used to collect the first feeding management training data and the second feeding management training data of the corresponding type, the feeding control model includes the feeding control model corresponding to the preset type, and each of the feeding devices is equipped with the feeding control model of the corresponding type; The method of using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model includes: Inputting the first feeding management training data of each species and the corresponding first feeding parameter label into the feeding regulation model of the corresponding species for supervised training to obtain a trained feeding regulation model for each species; The step of inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data includes: Inputting each type of the second feeding management training data into the trained feeding regulation model of the corresponding type to obtain the second feeding parameter label corresponding to each type of the second feeding management training data; The method of using the second training data set carrying the second feeding parameter label to perform supervised training on the feeding strategy decision model to obtain the trained feeding strategy decision model includes: A plurality of the second feeding management training data and the corresponding second feeding parameter labels are input into the feeding strategy decision model for supervised training to obtain the trained feeding strategy decision model.

[0011] In the above implementation process, different types of feeding management training data are used to train the corresponding feeding control models respectively, and the data of these trained models are used to train the feeding strategy decision model, thereby achieving more accurate and efficient feeding strategy formulation. Compared with the method of directly using the data of a single feeding control model to train the feeding strategy decision model, this method can better adapt to the characteristics of different types of feeding management data and improve the generalization ability and accuracy of the feeding strategy decision model.

[0012] Furthermore, the determining of feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: If the historical feeding management data indicates that the growth and health of the animal is normal, determining a first feeding parameter that meets the feeding strategy within the target time period; the first feeding parameter indicates maintaining current feeding parameters and environmental control parameters; If the historical feeding management data indicates that the growth and health of the animal is abnormal, a second feeding parameter that meets the feeding strategy within the target time period is determined; the second feeding parameter indicates adjustment of feeding parameters and environmental control parameters.

[0013] In the above implementation process, based on the historical feeding management data of the animals, the feeding parameters that meet the feeding strategy within the target time period are intelligently determined. When the animal's growth and health are normal, the current feeding parameters and environmental control parameters are maintained; if abnormalities occur, the feeding parameters and environmental control parameters are adjusted in time to ensure the effectiveness of the feeding strategy and the health of the animals.

[0014] Furthermore, the feeding parameters include feeding adjustment parameters and environmental control parameters; the feeding device includes a feeding device and an environmental control device; and the controlling the feeding device to feed the animal according to the feeding parameters includes: Controlling the feeding device to feed according to the feeding adjustment parameters; The environmental control device is controlled to perform environmental control according to the environmental control parameters.

[0015] In the above implementation process, according to the feeding parameters (including feeding adjustment parameters and environmental control parameters), the feeding device is intelligently controlled to perform feeding operations, and the environmental control device is adjusted to optimize the feeding environment, thereby achieving precise feeding management of animals.

[0016] Furthermore, the historical feeding management data includes any one or more of growth stage data, feeding behavior data, water intake data, disease data, and environmental data of the breeding house where the animals are located.

[0017] In the above implementation process, historical feeding and management data comprehensively covers multi-dimensional information such as the animal's growth stage, eating behavior, water drinking amount, disease status and breeding house environment, which is used to comprehensively evaluate and guide animal feeding and management.

[0018] A second aspect of an embodiment of the present application provides an electronic device, the electronic device comprising: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instruction, any method described in the first aspect is implemented.

[0019] A third aspect of an embodiment of the present application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of any method described in the first aspect.

[0020] A fourth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, any method described in the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a process for raising animals provided in an embodiment of the present application; Figure 2 A schematic diagram of another method for raising animals provided in an embodiment of the present application; Figure 3 A schematic diagram of another method for raising animals provided in an embodiment of the present application; Figure 4 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0025] In the related technologies, animal breeding is highly dependent on manual judgment and operation, which restricts the progress of modern breeding industry towards efficient and precise management. Therefore, a method that can improve the automation level and breeding efficiency of animal breeding is urgently needed.

[0026] In response to any of the above-mentioned problems, the present application provides an animal breeding method, referring to Figure 1 , Figure 1 A schematic diagram of a process for raising animals provided in an embodiment of the present application.

[0027] In this embodiment, the method includes: Step S10: obtaining a feeding target, and generating a feeding strategy within a preset time period based on the feeding target; It should be noted that the feeding target may include expected output, growth cycle, feed conversion rate, etc., and may also include index data such as animal reproduction rate and health status. Among them, the expected output refers to the number of animals planned to be slaughtered within a certain period of time (such as a quarter or a year); the growth cycle refers to the time required for an animal to reach the standard for slaughter from birth. Shortening the growth cycle can improve breeding efficiency, but it is also necessary to consider the health status and market acceptance of the animal; the feed conversion rate refers to the weight gain or economic benefits that an animal can generate for each certain amount of feed consumed. Improving the feed conversion rate can reduce breeding costs and improve profitability. The feeding target is determined based on factors such as the type, age, gender, health status, and market demand of the animal. This embodiment does not limit the data specifically included in the feeding target. The feeding target can be input by the animal breeding house manager. The feeding target is for the preset time period. The preset time period can refer to a quarter or a year. This embodiment does not limit this.

[0028] Feeding strategies need to ensure that feeding goals can be achieved within a preset time period. Feeding strategies can include appropriate feed types, feed quantities, feeding frequency, environmental control strategies, etc. The environmental control strategies include temperature and humidity of the breeding house and light control parameters that are set according to the growth habits and needs of the animals and meet the feeding goals.

[0029] Step S20: for each target time period within the preset time period, based on the historical feeding management data of the animal, determining feeding parameters that meet the feeding strategy within the target time period; the historical feeding management data is used to characterize the growth and health of the animal within the historical time period; the historical time period is before the target time period; It should be noted that the target time period is a subdivided time period within the preset time period, and may be a specific date or time period, such as a specific time period of each day, each week, or each month within the preset time period.

[0030] Historical feeding and management data refers to the feeding conditions of animals in the past period of time relative to the target time period, highlighting the growth and health of animals before the target time period. Animal feeding and management data can be collected in each target time period, so the historical feeding and management data can be the feeding and management data collected in one or more previous target time periods. Historical feeding and management data may include data such as the type and amount of feed fed, the amount of water, feeding environment parameters, and the illness and medication of animals. Historical feeding and management data is used to evaluate the response of animals to specific feeding conditions and determine which feeding parameters may be beneficial for animals to achieve feeding goals. As an example, historical feeding and management data is a comprehensive data set that covers multiple key indicators and detailed information from the birth of an animal to the entire feeding cycle. This includes but is not limited to the birth weight of animals, which reflects the health status and growth and development potential of animals at birth; weaning weight, which is an important indicator for evaluating the growth status of young animals during lactation; the number of live piglets, that is, the number of live piglets born by the mother animal each time, which is a key parameter for measuring reproductive performance; the number of stillbirths, which reveals possible reproductive problems of animals; the number of weaned piglets, which is used to evaluate the survival rate of young animals and the overall reproductive efficiency; feed formula, which records the details of the feeding formula adopted by the animals in the past feeding stage, and is used to evaluate the adaptability of the animals to a specific formula and the health status reflected thereby; the inbreeding coefficient is used to reflect the impact of the genetic closeness of the animals on their growth potential and health status; the breeding environment of the animals Environmental data of the house, such as temperature, humidity, viruses, light, etc., these factors have an important impact on the growth and development of animals; eating data and drinking water data, these two data provide an intuitive reflection of the daily feed intake and water requirements of animals; behavioral data, such as activity patterns, social interactions, etc., which reflect the vitality and health of animals; age and weight, these two data help track the growth curve of animals and affect the determination of feeding parameters; feed-to-meat ratio, that is, the ratio of feed consumption to animal weight gain, which is used to measure feeding efficiency and economic benefits; animal body condition, which can be a comprehensive evaluation score based on the manager's observation of the animal's body shape, muscle development, fat deposition, etc., which accurately reflects the current body condition of the animal. All these historical feeding and management data can be systematically recorded in a special archive module to form a comprehensive and detailed animal full feeding life cycle archive.

[0031] It is understood that specific feeding parameters are determined for each target time period based on historical feeding management data and feeding strategies. These parameters may include the type, quantity, feeding time of feed, and set values ​​of the feeding environment (such as temperature, humidity, etc.).

[0032] As an example, the feeding target is set for the next quarter, and the preset time period is a quarter. Each target time period refers to each day in a quarter. The historical time period can be one or more days before the target time period. Taking the historical time period as the previous target time period as an example, the feeding management data of the previous day (i.e., the historical feeding management data) is obtained, and the feeding management data is analyzed to evaluate the animal's response to the feeding conditions of the previous day, so as to determine the feeding parameters of the day that meet the feeding strategy.

[0033] Step S30: controlling the feeding device to feed the animals according to the feeding parameters.

[0034] It should be noted that the feeding device may include a feeding device, a watering device, an environmental control device (such as a temperature control device, a humidity control device, a ventilation device), etc. These devices are used to feed animals according to preset feeding parameters, including automatic feeding of feed, automatic feeding of drinking water, and adjustment of feeding environment parameters.

[0035] In addition, this embodiment also provides an interactive layer for providing an operation interface for users to display real-time or historical feeding management data, feeding strategies and implementation status. At the same time, the interactive layer can also display the determined feeding strategies and feeding parameters so that users can adjust them to optimize the feeding plan.

[0036] In this embodiment, by obtaining feeding goals and formulating feeding strategies for a longer period of time, and using historical data to optimize the feeding parameters of each sub-period within the time period, refined regulation and management of the feeding process is achieved, thereby improving feeding efficiency and animal health levels.

[0037] On the basis of any of the above embodiments, the step of generating a feeding strategy within a preset time period based on the feeding target includes: Inputting the feeding target into a trained feeding strategy decision model to obtain the feeding strategy output by the feeding strategy decision model; It should be noted that the feeding strategy decision model can be a machine learning or artificial intelligence model, and the feeding strategy decision model can specifically be a BP neural network or a recurrent neural network (RNN). It can generate a corresponding feeding strategy based on the input feeding target through internal calculation.

[0038] The step of determining feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: The historical feeding management data and the feeding strategy are input into a trained feeding regulation model to obtain the feeding parameters output by the feeding regulation model.

[0039] It is understandable that the feeding control model is also a machine learning or artificial intelligence model, which can output specific feeding parameters based on the input historical feeding management data and feeding strategies. This model includes complex algorithms and data processing processes to analyze and optimize feeding parameters to meet the requirements of feeding strategies. In a specific implementation, the feeding control model is mounted on a feeding device to form a micro decision-making agent (MDMA). MDMA is equivalent to an intelligent system that integrates an algorithm model and a feeding hardware device for quickly making decisions on feeding parameters. The system can include a variety of neural network models as its core components, and its core components can be, for example: neural network model, decision tree, reinforcement learning model, genetic algorithm.

[0040] In this embodiment, by effectively integrating feeding objectives and historical feeding management data, and utilizing a feeding strategy decision model and a feeding regulation model, specific feeding strategies and feeding parameters are generated, thereby achieving refined management of the animal feeding process and improving the scientificity and accuracy of feeding.

[0041] Based on any of the above embodiments, the feeding regulation model and the feeding strategy decision model are as follows: Figure 2 Steps S01 to S05 are shown for training: Step S01: obtaining a first training data set, wherein the first training data set includes first feeding management training data collected historically, and a first feeding parameter label corresponding to the first feeding management training data; It should be understood that the first feeding management training data refers to the feeding management data collected historically, which refers to the feeding conditions of the animals in the historical time period and highlights the growth and health of the animals in the period. The first feeding management training data may include data such as the type and quantity of feed, feeding environment parameters, and the medication conditions of the animals when they are sick. The first feeding management training data is used to evaluate the response of animals to specific feeding conditions and determine which feeding parameters may be beneficial for the animals to achieve feeding goals.

[0042] The first feeding parameter label corresponding to the first feeding management training data may be a feeding parameter for the first feeding management training data set by the user based on experience.

[0043] Step S02: using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model; Understandably, supervised training is a machine learning technique that uses labeled datasets to train a model so that the model can predict labels for new data.

[0044] Specifically, the first feeding management training data is input into the feeding control model, and the difference between the feeding parameters output by the model and the first feeding parameter label is compared. By adjusting the model parameters (such as weights and biases), the difference is gradually reduced until the model achieves satisfactory performance, and finally a trained feeding control model is obtained, which can predict feeding parameters based on the feeding management data.

[0045] Step S03: obtaining a second training data set; the second training data set includes second feeding management training data collected historically and feeding target data input historically; It should be noted that the second feeding management training data collected historically may be different from the specific data included in the first training data set, but the format and data type are the same or similar.

[0046] The historically input feeding target data may include expected output, growth cycle, feed conversion rate, etc., and may also include indicator data such as animal reproduction rate and health status.

[0047] Step S04: inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data; Specifically, the trained feeding control model is used to predict the second feeding management training data to obtain a second feeding parameter label. Each second feeding management training data obtains a corresponding second feeding parameter label.

[0048] Step S05: Performing supervised training on the feeding strategy decision model using the second training data set carrying the second feeding parameter label to obtain the trained feeding strategy decision model.

[0049] It should be noted that the feeding strategy decision model is trained with the input and output data of the feeding regulation model for the following purposes: Data consistency: The feeding strategy decision model needs to understand the possible growth performance and health status of animals under different feeding conditions in order to formulate effective feeding strategies. The feeding regulation model provides this information.

[0050] Knowledge inheritance: Feeding regulation models are usually built based on a large amount of scientific research and experimental data, and they contain in-depth knowledge and experience about animal feeding. By training the feeding strategy decision model with the output data of the feeding regulation model, this knowledge and experience can be passed on to the feeding strategy decision model, improving the training efficiency of the feeding strategy decision model and enabling it to more accurately predict and formulate feeding strategies.

[0051] Specifically, supervised training is also used to input the second feeding management training data, the second feeding parameter label, and the feeding target data into the feeding strategy decision model, and the model learns how to generate an effective feeding strategy according to the feeding target. By adjusting the model parameters, the feeding strategy output by the model is more closely matched with the feeding target, and finally a trained feeding strategy decision model is obtained, which can generate the best feeding strategy according to the feeding target and feeding parameters.

[0052] In this embodiment, the feeding control model is first trained to predict feeding parameters, and then the feeding strategy decision model is trained using these prediction results and feeding targets. This method ensures that the model can accurately reflect the relationship between feeding management and feeding results, and generate effective feeding strategies and feeding parameters.

[0053] On the basis of any of the above embodiments, the first feeding management training data and the second feeding management training data both include feeding management training data of preset types, each type of the first feeding management training data corresponds to a first feeding parameter label; each type of the second feeding management training data corresponds to a second feeding parameter label; the feeding device includes multiple types, each of the feeding devices is used to collect the first feeding management training data and the second feeding management training data of the corresponding type, the feeding control model includes the feeding control model corresponding to the preset type, and each of the feeding devices is equipped with the feeding control model of the corresponding type; The method of using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model includes: Inputting the first feeding management training data of each species and the corresponding first feeding parameter label into the feeding regulation model of the corresponding species for supervised training to obtain a trained feeding regulation model for each species; It should be noted that there are multiple types of feeding devices, each of which is used to collect the first feeding management training data and the second feeding management training data of its corresponding type, and each of which is equipped with a feeding control model of its corresponding type, which is used to determine the feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the corresponding type.

[0054] Specifically, the first feeding management training data of each type and the corresponding feeding parameters are input into the feeding regulation model of the corresponding type to obtain the trained feeding regulation model of each type.

[0055] As an example, the feeding device includes a feeding device, a watering device, and an environmental control device (taking a temperature and humidity adjustment device as an example). The feeding device is used to collect the types and quantities of feed in the feeding management data. The feeding device is equipped with a trained feeding control model specifically used to process feeding-related data. The model is used to generate feeding parameters for adjusting the types and amounts of feed according to the types and amounts of feed in the historical feeding management data; the watering device is used to collect the amount of water in the feeding management data. The watering device is equipped with a trained feeding control model specifically used to process animal drinking water-related data. The model is used to generate feeding parameters for adjusting the amount of water in the watering device according to the amount of water in the historical feeding management data; the environmental control device is used to collect temperature and humidity data in the feeding management data. The environmental control device is equipped with a trained feeding control model specifically used to process temperature and humidity related data. The model is used to generate feeding parameters for adjusting the temperature and humidity of the breeding house where the animal is located according to the temperature and humidity data in the historical feeding management data.

[0056] The step of inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data includes: Inputting each type of the second feeding management training data into the trained feeding regulation model of the corresponding type to obtain the second feeding parameter label corresponding to each type of the second feeding management training data; Specifically, the second feeding management training data is processed using the trained feeding regulation model: each type of second feeding management training data is input into the trained feeding regulation model of the corresponding type, and the model outputs the second feeding parameter label corresponding to each type of second feeding management training data.

[0057] The method of using the second training data set carrying the second feeding parameter label to perform supervised training on the feeding strategy decision model to obtain the trained feeding strategy decision model includes: A plurality of the second feeding management training data and the corresponding second feeding parameter labels are input into the feeding strategy decision model for supervised training to obtain the trained feeding strategy decision model.

[0058] It should be noted that the purpose of using a variety of second feeding management training data and their corresponding second feeding parameter labels to train the feeding strategy decision model includes: since the main function of the feeding strategy decision model is to generate feeding strategies including feeding strategies, watering strategies, and environmental control strategies, and the diversity of feeding management training data can ensure that the model can learn the broad laws of animal growth and development under different feeding conditions, improve the generalization ability of the model, and generate more accurate feeding strategies, a variety of second feeding management training data are used to train the feeding strategy decision model. In addition, using the input and output data of a variety of trained feeding control models (i.e., the second feeding management training data and their corresponding second feeding parameter labels) to train the feeding strategy decision model is also to improve the training efficiency of the feeding strategy decision model, so that the feeding strategy decision model can better inherit the reasoning ability of a variety of feeding control models.

[0059] It should be understood that in addition to using the second feeding management training data and its corresponding second feeding parameter label to train the feeding strategy decision model, it is also necessary to use the historical input feeding target data to train the feeding strategy decision model to ensure that the feeding strategy decision model has the ability to infer the feeding strategy based on the feeding target.

[0060] In this embodiment, by using preset types of feeding management training data and their corresponding feeding parameter labels, supervised training is performed on the feeding control models carried by different types of feeding devices, and then these trained models are used to generate second feeding parameter labels for the second feeding management training data, and finally a feeding strategy decision model is trained, thereby ensuring the reasoning ability of each model, thereby improving the intelligence and efficiency of feeding management.

[0061] On the basis of any of the above embodiments, the step of determining feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: If the historical feeding management data indicates that the growth and health of the animal is normal, determining a first feeding parameter that meets the feeding strategy within the target time period; the first feeding parameter indicates maintaining current feeding parameters and environmental control parameters; If the historical feeding management data indicates that the growth and health of the animal is abnormal, a second feeding parameter that meets the feeding strategy within the target time period is determined; the second feeding parameter indicates adjustment of feeding parameters and environmental control parameters.

[0062] It is understandable that if the historical feeding and management data reflect that the animal's growth and health are normal, that is, there are no abnormal conditions such as disease, malnutrition, anorexia, etc., then the current feeding strategy will be maintained within the target time period, that is, the feeding parameters and environmental control parameters will not be changed.

[0063] If the historical feeding and management data reflects abnormal growth and health conditions of the animals, such as illness, medication, anorexia, and other abnormal conditions, the feeding strategy will be adjusted within the target time period, that is, the feeding parameters and environmental control parameters will be changed.

[0064] As an example, taking the animal as a pig, the historical feeding management data indicates the pig's eating habits, wherein the number of times the pig eats is determined based on the number of interactions between the ear tag worn on the pig's ear and the receiver installed on the feeding device. If the historical feeding management data reflects that the pig's eating frequency and food intake have decreased, the number of feedings, the number of waterings, the water-to-feed ratio, the feed formula (such as increasing the proportion of certain nutrients, changing the type of feed, etc.), and the environmental control parameters (such as adjusting the temperature and humidity to provide a more comfortable environment) are adjusted to improve the health of the animals.

[0065] In this embodiment, the historical feeding management data of the animal is used to predict and determine the feeding parameters in a short period of time in the future. The advantage of this method is that it can flexibly adjust the feeding strategy according to the actual situation of the animal (such as health status), thereby improving feeding efficiency.

[0066] Based on any of the above embodiments, the feeding parameters include feeding adjustment parameters and environmental control parameters; the feeding device includes a feeding device and an environmental control device; step S30 includes: Figure 3 Steps S31-S32 shown: Step S31: controlling the feeding device to feed according to the feeding adjustment parameters; It should be noted that feeding parameters include feeding adjustment parameters and environmental control parameters, and of course, other parameters may also be included. Among them, feeding adjustment parameters involve feed type, nutrient ratio, feeding amount, feeding frequency, water-to-feed ratio, water input, etc. Environmental control parameters involve temperature, humidity, ventilation, etc. of the feeding environment, which affect the growth, development and health of animals.

[0067] Step S32: Control the environment control device to perform environment control according to the environment control parameters.

[0068] In this embodiment, the feeding device includes a feeding device and an environmental control device. Of course, in practical applications, the feeding device can be provided with more types of devices according to needs, such as the feeding device can also include a watering device. Among them, the feeding device is responsible for feeding the feed according to the feeding adjustment parameters, and the feeding device can include an automatic feeding machine, a water material mixer, etc. The environmental control device is responsible for adjusting the feeding environment according to the environmental control parameters, and the environmental control device can include a temperature controller, a humidity regulator, a lighting device, etc.

[0069] In this embodiment, the entire feeding management process is a closed-loop control system, which realizes precise feeding of animals by controlling the feeding device and the environmental control device according to the requirements of feeding parameters.

[0070] Based on any of the above embodiments, the historical feeding management data includes any one or more of growth stage data, feeding behavior data, water intake data, disease data, and environmental data of the breeding house where the animals are located.

[0071] It should be noted that historical feeding and management data is a comprehensive data set that covers a variety of information generated by animals during the feeding process. This information is very important for evaluating the health status and growth performance of animals and formulating and optimizing feeding strategies. Among them, growth stage data: such as juvenile stage, growth stage, mature stage, etc. The growth stage has an important impact on the nutritional needs, feeding management methods and disease prevention and control strategies of animals. By analyzing the growth stage data, we can understand the growth and development of animals, so as to adjust feeding parameters, feeding environment, etc. Feeding behavior data includes the amount of food, feeding time, feeding frequency, etc. of animals. These data reflect the appetite and digestion ability of animals, and are meaningful for judging the health status and nutritional needs of animals. For example, if the amount of food consumed by animals suddenly decreases, it may mean that they have health problems or the feed is not to their taste or they are about to give birth. Water intake data reflects the amount of water and the time of water intake of animals. Water intake is related to factors such as the physiological state of animals, ambient temperature and humidity. By analyzing the water intake data, we can understand the water needs of animals and adjust the water supply in time to ensure that animals get enough water. Disease data reflects whether an animal has a certain disease, the type of disease, the time of onset, and the medication used for treatment. Disease data is one of the important indicators for assessing the health status of animals. By analyzing disease data, we can promptly discover health problems of animals and adjust the type and amount of feed accordingly. The environmental data of the animal breeding house includes the temperature, humidity, and light of the breeding house. Environmental factors have an impact on the growth performance, health status, and reproductive capacity of animals. By analyzing environmental data, we can understand whether the environmental conditions of the breeding house are suitable, so as to adjust the environmental conditions and provide a good living environment for animals.

[0072] As an example, historical feeding and management data is a comprehensive data set that covers a number of key indicators and detailed information from the birth of an animal to the entire feeding cycle. This includes, but is not limited to, the birth weight of the animal, which reflects the health and growth potential of the animal at birth; weaning weight, which is an important indicator for evaluating the growth of young animals during the lactation period; the number of live births, which is the number of live piglets born by the mother animal at each delivery, which is a key parameter for measuring reproductive performance; the number of stillbirths, which reveals possible reproductive problems of the animal; the number of weaned piglets, which is used to evaluate the survival rate of young animals and the overall reproductive efficiency; feed formula, which records the details of the feeding formula used by the animal in the past feeding stage, used to evaluate the animal's adaptability to a specific formula and the health status reflected thereby; the inbreeding coefficient, which is used to reflect the impact of the animal's genetic proximity on its growth potential and health status; the breeding of the animal Environmental data of the house, such as temperature, humidity, viruses, light, etc., these factors have an important impact on the growth and development of animals; eating data and drinking water data, these two data provide an intuitive reflection of the daily feed intake and water requirements of animals; behavioral data, such as activity patterns, social interactions, etc., which reflect the vitality and health of animals; age and weight, these two data help track the growth curve of animals and affect the determination of feeding parameters; feed-to-meat ratio, that is, the ratio of feed consumption to animal weight gain, which is used to measure feeding efficiency and economic benefits; animal body condition, which can be a comprehensive evaluation score based on the manager's observation of the animal's body shape, muscle development, fat deposition, etc., which accurately reflects the current body condition of the animal. All these historical feeding and management data can be systematically recorded in a special archive module to form a comprehensive and detailed animal full feeding life cycle archive.

[0073] In this embodiment, the historical feeding management data is a comprehensive data set containing a variety of information. By analyzing and utilizing this data, we can fully understand the health status, growth performance and feeding environment of the animals, so as to formulate and optimize feeding parameters and improve feeding efficiency.

[0074] Based on the method described in any of the above embodiments, the present application also provides Figure 4 A schematic diagram of the structure of an electronic device is shown in FIG. Figure 4 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the method described in any of the above embodiments.

[0075] Based on the method described in any of the above embodiments, the present application also provides a computer storage medium, which stores a computer program. When the computer program is executed by a processor, it can be used to execute the method described in any of the above embodiments.

[0076] Based on the method described in any of the above embodiments, the present application also provides a computer program product, which includes one or more computer programs or instructions. The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. When the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0077] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0078] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0079] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0080] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for raising animals, characterized in that: The method comprises: Acquire a feeding target, and generate a feeding strategy within a preset time period based on the feeding target; For each target time period within the preset time period, based on the historical feeding management data of the animal, determining feeding parameters that meet the feeding strategy within the target time period; the historical feeding management data is used to characterize the growth and health of the animal within the historical time period; the historical time period is before the target time period; The feeding device is controlled to feed the animals according to the feeding parameters.

2. The method according to claim 1, characterized in that The step of generating a feeding strategy within a preset time period based on the feeding target includes: Inputting the feeding target into a trained feeding strategy decision model to obtain the feeding strategy output by the feeding strategy decision model; The step of determining feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: The historical feeding management data and the feeding strategy are input into a trained feeding regulation model to obtain the feeding parameters output by the feeding regulation model.

3. The method according to claim 2, characterized in that The feeding regulation model and the feeding strategy decision model are trained by the following steps: Acquire a first training data set, where the first training data set includes first feeding management training data collected historically and a first feeding parameter label corresponding to the first feeding management training data; Using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model; Acquire a second training data set; the second training data set includes second feeding management training data collected historically and feeding target data input historically; Inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data; The feeding strategy decision model is supervisedly trained using the second training data set carrying the second feeding parameter label to obtain the trained feeding strategy decision model.

4. The method according to claim 3, characterized in that The first feeding management training data and the second feeding management training data both include feeding management training data of preset types, each type of the first feeding management training data corresponds to a first feeding parameter label; each type of the second feeding management training data corresponds to a second feeding parameter label; The feeding devices include multiple types, each of which is used to collect the first feeding management training data and the second feeding management training data of a corresponding type, the feeding control model includes a feeding control model corresponding to the preset type, and each of the feeding devices is equipped with the feeding control model of the corresponding type; The method of using the first training data set to perform supervised training on the feeding regulation model to obtain a trained feeding regulation model includes: Inputting the first feeding management training data of each species and the corresponding first feeding parameter label into the feeding regulation model of the corresponding species for supervised training to obtain a trained feeding regulation model for each species; The step of inputting the second feeding management training data into the trained feeding regulation model to obtain a second feeding parameter label corresponding to the second feeding management training data includes: Inputting each type of the second feeding management training data into the trained feeding regulation model of the corresponding type to obtain the second feeding parameter label corresponding to each type of the second feeding management training data; The method of using the second training data set carrying the second feeding parameter label to perform supervised training on the feeding strategy decision model to obtain the trained feeding strategy decision model includes: A plurality of the second feeding management training data and the corresponding second feeding parameter labels are input into the feeding strategy decision model for supervised training to obtain the trained feeding strategy decision model.

5. The method according to claim 1, characterized in that The step of determining feeding parameters that meet the feeding strategy within the target time period based on the historical feeding management data of the animal includes: If the historical feeding management data indicates that the growth and health of the animal is normal, determining a first feeding parameter that meets the feeding strategy within the target time period; the first feeding parameter indicates maintaining current feeding parameters and environmental control parameters; If the historical feeding management data indicates that the growth and health of the animal is abnormal, a second feeding parameter that meets the feeding strategy within the target time period is determined; the second feeding parameter indicates adjustment of feeding parameters and environmental control parameters.

6. The method according to claim 1, characterized in that The feeding parameters include feeding adjustment parameters and environmental control parameters; the feeding device includes a feeding device and an environmental control device; the controlling the feeding device to feed the animal according to the feeding parameters includes: Controlling the feeding device to feed according to the feeding adjustment parameters; The environmental control device is controlled to perform environmental control according to the environmental control parameters.

7. The method according to claim 1, characterized in that The historical feeding management data includes any one or more of growth stage data, feeding behavior data, water intake data, disease data, and environmental data of the breeding house where the animals are located.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instruction, the method described in any one of claims 1-7 is implemented.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of any method described in claims 1-7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images