KR102612359B1 - Automated system for discharging microorganisms from livestock farms through remote control - Google Patents

Abstract

An automated system for discharging microorganisms from livestock farms through remote control is disclosed. The automated system for discharging microorganisms in livestock farms through remote control according to an embodiment of the present invention controls microbial raw materials to be ejected from the microbial storage tank at a preset cycle, and mixes the microbial raw materials with water discharged from the water supply facility to generate microbial water. and controls the discharge of microbial water.

Description

Automated system for discharging microorganisms from livestock farms through remote control {AUTOMATED SYSTEM FOR DISCHARGING MICROORGANISMS FROM LIVESTOCK FARMS THROUGH REMOTE CONTROL}

The examples below relate to an automated system for discharging microorganisms from livestock farms through remote control.

As background technology related to the embodiments, Korean Patent Publication KR 10-2019-0019161 A discloses an end fitting and device for sampling a colony of microorganisms, and a sampling process using the same. Specifically, the prior art document discloses a configuration regarding an end fitting that can be fitted to the body of a manual or automated device for sampling biological material derived from microorganisms.

Through this, the prior literature provides a device for sampling biological material that has a simple design and is easy to produce, especially with end fittings that enable accurate sampling and deposition of biological material when placed on a MALDI-TOF type mass spectrometer analysis plate. It has an effect.

Additionally, Republic of Korea Patent Publication KR 10-2009-0129072 A discloses a device for detecting airborne microorganisms. Specifically, the prior literature includes a collection unit that collects microorganisms floating in the air and accommodates a microbial mixture solution in which the microorganisms collected in a buffer solution contained therein are mixed; A sensing member equipped with a plurality of channels to which a plurality of specific antibodies are attached and which provides a microbial mixture with the plurality of specific antibodies is provided, and detects microorganisms present in the microbial mixture upon antigen-antibody reaction between the microbial mixture and the plurality of specific antibodies. wealth; and a first pump that provides a buffer solution to the collection unit, a plurality of mixed solution supply pumps that are respectively connected to the collection unit and a plurality of channels and individually supply the microbial mixture to the plurality of channels, and are connected to the discharge lines of the collection unit and the sensing unit. Disclosed is a configuration including a pump unit provided with a second pump for discharging a microbial mixture or buffer solution to the outside.

Through this, the prior literature has the effect of reducing detection time and manpower input compared to existing methods by automating a series of processes for collecting and detecting airborne microorganisms in liquid form to enable real-time detection.

However, prior literature controls spewing out microbial raw materials from a microbial storage tank at a preset cycle, controls to produce microbial water by mixing the microbial raw materials with water spouted from a water supply facility, and controls livestock farming through remote control to control the discharge of microbial water. The automated system for discharging microorganisms from farms is not disclosed.

Republic of Korea Patent Publication KR 10-2019-0019161 A Republic of Korea Patent Publication KR 10-2009-0129072 A

The embodiments are intended to provide a system for generating microbial water by mixing microbial raw materials and water from a water supply facility and supplying the same.

The embodiments are intended to provide a method of controlling the amount of microbial water discharged by considering the ammonia level of the microbial water discharge target.

The embodiments are intended to provide a method of tracking the ammonia level at predetermined times after the microbial raw material is ejected and doubling the amount of ammonia until it falls below the threshold value.

An automated system for discharging microorganisms from livestock farms through remote control according to an embodiment of the present invention includes a microbial storage tank for storing microbial raw materials; a server that controls the microbial raw material to be ejected from the microbial storage tank at a preset cycle, controls the microbial raw material to be mixed with water discharged from a water supply facility to generate microbial water, and controls the discharge of the microbial water; And it may include a database that stores information about the ejection cycle of the microbial raw material in the microbial storage tank and the water supply cycle of the water supply facility.

In one embodiment, it further includes an ammonia sensor that detects a first ammonia level of the microbial water discharge target, and the server performs a first ejection of the microbial raw material to reduce the first ammonia level to a first threshold. The amount can be calculated and controlled to eject the microbial raw material in a second ejection amount that is a predetermined ratio higher than the first ejection amount.

In one embodiment, the server controls the ammonia sensor to detect a second ammonia level of the microbial water discharge target after a first time elapses after ejecting the microbial raw material of the second ejection amount, and When the first threshold is greater than the ammonia level, the ejection of the microbial raw material is controlled to end, and when the second ammonia level is greater than the first threshold, the third ejection amount is twice as much as the second ejection amount. Controlling the microbial raw material to be ejected, controlling the ammonia sensor to detect a third ammonia level of the microbial water discharge target a second time after ejecting the microbial raw material of the third ejection amount, and controlling the ammonia sensor to detect the third ammonia level of the microbial water discharge target. When the first threshold is greater than the ammonia level, the ejection of the microbial raw material is controlled to end, and when the third ammonia level is greater than the first threshold, the fourth ejection amount is twice as much as the third ejection amount. Controlling to eject the microbial raw material, controlling the ammonia sensor to detect a fourth ammonia level of the microbial water discharge target a third time after ejecting the microbial raw material of the fourth ejection amount, and When the first threshold is greater than the ammonia level, the ejection of the microbial raw material can be controlled to end.

In one embodiment, the server controls the ammonia sensor to detect the fifth ammonia level of the microbial water discharge target after a fourth time has elapsed after ejecting the second ejection amount of the microbial raw material, and the fifth ejection amount is The difference between the ammonia level and the first ammonia level is divided by the fourth time to calculate the ammonia level reduction rate per unit time, and the ammonia level difference is calculated as the difference between the fifth ammonia level and the first threshold value, and the ammonia level difference is calculated. The expected ejection end time is calculated by dividing by the ammonia level reduction rate per unit time, and after the elapse of the elapsed expected ejection end time from the fourth time, the ejection of the microbial raw material can be terminated and a user alarm can be generated.

In one embodiment, the server calculates the storage height of the microbial raw material stored in the microbial storage tank, calculates a first height difference between the total height of the microbial storage tank and the storage height, and calculates the first height difference between the microbial raw material stored in the microbial storage tank and the storage height. Calculating a fifth time taken to decrease, dividing the first height difference by the fifth time to calculate a decrease rate per unit time, dividing the storage height by the decrease rate per unit time to calculate a storage expiration time of the microbial raw material, and , a microbial raw material supply notification signal may be formed at the sixth time, which is the storage expiration time plus the fermentation time of the microbial raw material.

The device according to one embodiment may be combined with hardware and controlled by a computer program stored in a medium to execute any one of the above-described methods.

Examples can prevent the activation of harmful bacteria, remove bad odors, and decompose organic matter by using microbial raw materials that are harmless to the human body. In addition, the ammonia level can be used to calculate the expected ejection end time and use this to end the ejection of microbial raw materials and generate a user alarm to increase user convenience and economic effectiveness.

Figure 1 is a diagram showing an automated system for discharging microorganisms from a livestock farm through remote control according to an embodiment.
Figure 2 is a diagram for explaining a server according to an embodiment.
Figure 3 is a diagram for explaining learning of a neural network according to an embodiment.
Figure 4 is a flowchart of a method for automating the discharge of microorganisms from livestock farms through remote control according to an embodiment.
Figure 5 is an exemplary diagram of the configuration of a device according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, various changes can be made to the embodiments, so the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes for the embodiments are included in the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

Embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

Figure 1 is a diagram showing an automated system for discharging microorganisms from a livestock farm through remote control according to an embodiment.

As shown in FIG. 1, the automated system 100 for discharging microorganisms from livestock farms through remote control may include a microbial storage tank 110, a server 120, a database 130, and an ammonia sensor 140. . According to one embodiment, the database 130 is shown as being configured separately from the server 120, but the present invention is not limited thereto, and the database 130 may be provided within the server 120. For example, the server 120 may include multiple artificial neural networks for performing machine learning algorithms. According to one embodiment, the microbial reservoir 110, server 120, database 130, and ammonia sensor 140 may be connected to communicate with each other through a network (N).

The microbial storage tank 110 can store microbial raw materials. According to one embodiment, it can be excellent at removing bad odors by feeding on bacteria that cause harmful odors in humans or animals. For example, the microbial raw material may represent the collection of protobacteria by fermenting organic plant leaves, cultivating the collected protobacteria, multiplying the cultured protobacteria in large quantities, preventing the activation of harmful bacteria, removing bad odors, and removing organic matter. It can be disassembled. Microbial raw materials can be manufactured within a minimum of 3 to a maximum of 10 days, harmful bacteria can be completely decomposed within 1 day, and organic matter can be completely decomposed within a maximum of 15 days. According to one embodiment, microbial raw materials can be used for livestock waste odor removal, livestock waste reprocessing, food waste reprocessing, domestic sewage treatment plant odor removal and improvement, drinking water and feed additives, freshwater fish farm water purification, freshwater aquaculture feed addition, etc. . According to one embodiment, the microbial storage tank 110 may be configured as shown in Reference Figure 1, but is not limited thereto.

[Reference 1]

The server 120 may control the microbial raw material to be ejected from the microbial storage tank at a preset cycle. According to one embodiment, the server 120 may control the microbial raw material to be ejected by a predetermined amount from the microbial storage tank 110 at a preset cycle (e.g., 3 times a day, 6 hours, etc.).

The server 120 can control the microbial water to be generated by mixing microbial raw materials with water spouted from the water supply facility. According to one embodiment, the server 120 may control the microbial raw material spouted from the microbial storage tank 110 to be mixed with the water spouted from the water supply facility to generate microbial water. In the case of water supply facilities, facilities installed in existing livestock sheds and used for watering livestock can be used.

The server 120 can control the discharge of microbial water. According to one embodiment, the server 120 may control the discharge of microbial water mixed with microbial raw materials and water through the network N. For example, if the microbial water discharge port is blocked and the microbial water cannot be discharged, the server 120 may drive a needle to remove the blockage of the microbial water discharge port.

The ammonia sensor 140 may periodically or non-periodically detect the ammonia level of the microbial water discharge target. According to one embodiment, the ammonia sensor 140 may detect the first ammonia level of the microbial water discharge target (e.g., livestock farm, sewage treatment plant, food waste treatment plant, fish farm, etc.) under the control of the server 120. .

Additionally, the server 120 may calculate the first ejection amount of the microbial raw material to reduce the first ammonia level to the first threshold value. According to one embodiment, the server 120 sets the first ammonia level to a first threshold (e.g., 0ppm, 0ppm, The first ejection amount of the microbial raw material to reduce to 1ppm, etc.) can be calculated.

The server 120 can control the microbial raw material to be ejected at a second ejection amount that is a predetermined ratio higher than the first ejection amount. According to one embodiment, the server 120 controls to eject a second ejection amount of microbial raw material that is higher than the first ejection amount by a predetermined ratio (e.g., 5%, 10%, 20%, etc. of the first ejection amount). can do.

In addition, the server 120 may control the ammonia sensor to detect the second ammonia level of the microbial water discharge target after a first time has elapsed after ejecting the second ejection amount of microbial raw material. According to one embodiment, the server 120 determines the number of microorganisms after a first period of time (e.g., 6 hours, 1 day, 10 days, 1 week, 1 month, etc.) after ejecting the second amount of microbial raw material. The ammonia sensor 140 can be controlled to detect the second ammonia level of the discharge target (eg, livestock farm, sewage treatment plant, food waste treatment plant, fish farm, etc.).

The server 120 may control the ejection of the microbial raw material to end when the first threshold value is greater than the second ammonia level. According to one embodiment, when the first threshold value is greater than the second ammonia level detected by the ammonia sensor 140, the server 120 determines that the ammonia level of the microbial water discharge target has reached the target value, so that the microbial storage tank 110 It can be controlled to terminate the eruption of microbial raw materials. Thereafter, the server 120 controls the ammonia sensor 140 to periodically or aperiodically detect the ammonia level of the microbial water discharge target, and when the ammonia level of the microbial water discharge target becomes greater than the first threshold, the corresponding microorganism Raw materials can be controlled to be ejected from the microbial storage tank 110.

When the second ammonia level is greater than the first threshold, the server 120 may control to eject a third ejection amount of microbial raw material that is twice as much as the second ejection amount. According to one embodiment, when the second ammonia level is greater than the first threshold, the server 120 may control a third ejection amount of microbial raw material twice as much as the second ejection amount to be ejected from the microbial storage tank 110. there is.

The server 120 may control the ammonia sensor to detect the third ammonia level of the microbial water discharge target after a second time elapses after ejecting the third ejection amount of the microbial raw material. According to one embodiment, the server 120 determines the number of microorganisms after a second period of time (e.g., 6 hours, 1 day, 10 days, 1 week, 1 month, etc.) after ejecting the third amount of microbial raw material. The ammonia sensor 140 can be controlled to detect the third ammonia level of the discharge target.

The server 120 may control the ejection of the microbial raw material to end when the first threshold value is greater than the third ammonia level. According to one embodiment, when the first threshold value is greater than the third ammonia level detected by the ammonia sensor 140, the server 120 determines that the ammonia level of the microbial water discharge target has reached the target value, so that the microbial storage tank 110 It can be controlled to terminate the eruption of microbial raw materials. Thereafter, the server 120 controls the ammonia sensor 140 to periodically or aperiodically detect the ammonia level of the microbial water discharge target, and when the ammonia level of the microbial water discharge target becomes greater than the first threshold, the corresponding microorganism Raw materials can be controlled to be ejected from the microbial storage tank 110.

The server 120 may control to eject a fourth ejection amount of microbial raw material that is twice as large as the third ejection amount when the third ammonia level is greater than the first threshold. According to one embodiment, when the third ammonia level is greater than the first threshold, the server 120 may control a fourth ejection amount of microbial raw material twice as much as the third ejection amount to be ejected from the microbial storage tank 110. there is. That is, the server 120 increases the ejection amount of the microbial raw material by two times until the ammonia level of the microbial water discharge target becomes lower than the first threshold, thereby reducing the time for the ammonia level of the microbial water discharge target to decrease.

The server 120 may control the ammonia sensor to detect the fourth ammonia level of the microbial water discharge target a third time after ejecting the fourth ejection amount of the microbial raw material. According to one embodiment, the server 120 determines the number of microorganisms after a third time (e.g., 6 hours, 1 day, 10 days, 1 week, 1 month, etc.) after ejecting the fourth amount of microbial raw material. The ammonia sensor 140 can be controlled to detect the fourth ammonia level of the discharge target.

The server 120 may control the ejection of the microbial raw material to end when the first threshold value is greater than the fourth ammonia level. According to one embodiment, when the first threshold value is greater than the fourth ammonia level detected by the ammonia sensor 140, the server 120 determines that the ammonia level of the microbial water discharge target has reached the target value, so that the microbial storage tank 110 It can be controlled to terminate the eruption of microbial raw materials. Thereafter, the server 120 controls the ammonia sensor 140 to periodically or aperiodically detect the ammonia level of the microbial water discharge target, and when the ammonia level of the microbial water discharge target becomes greater than the first threshold, the corresponding microorganism Raw materials can be controlled to be ejected from the microbial storage tank 110.

In addition, the server 120 may control the ammonia sensor to detect the fifth ammonia level of the microbial water discharge target after the fourth time has elapsed after ejecting the second ejection amount of the microbial raw material. According to one embodiment, the server 120 determines the number of microorganisms after a fourth period of time (e.g., 6 hours, 1 day, 10 days, 1 week, 1 month, etc.) after ejecting the second amount of microbial raw material. The ammonia sensor 140 can be controlled to detect the fifth ammonia level of the discharge target.

The server 120 may calculate the ammonia level reduction rate per unit time by dividing the difference between the fifth ammonia level and the first ammonia level by the fourth time. According to one embodiment, the server 120 may calculate the ammonia level reduction rate per unit time using Equation 1.

[Equation 1]

The server 120 may calculate the ammonia level difference, which is the difference between the fifth ammonia level and the first threshold value. According to one embodiment, the server 120 may calculate the ammonia level difference using Equation 2.

[Equation 2]

The server 120 may calculate the expected ejection end time by dividing the ammonia level difference by the ammonia level reduction rate per unit time. According to one embodiment, the server 120 may calculate the expected ejection end time using Equation 3.

[Equation 3]

The server 120 may control the ejection of the microbial raw material to end after the expected ejection end time has elapsed from the fourth time and generate a user alarm. According to one embodiment, the server 120 sets a timer to check whether the expected ejection end time has elapsed from the fourth time, and after the ejection end expected time has elapsed from the fourth time, the microbial raw material from the microbial storage tank 110 The ejection may be terminated, and a user alarm regarding the end of ejection of the microbial raw material may be generated to notify the manager, etc., that the ejection of the microbial raw material has ended.

Additionally, the server 120 may calculate the storage height of the microbial raw material stored in the microbial storage tank. According to one embodiment, the microbial storage tank 110 may include a height detection sensor, etc., and the server 120 may calculate the storage height of the microbial raw material stored in the microbial storage tank 110 through a height detection sensor, etc. .

The server 120 may calculate the first height difference between the total height of the microbial storage tank and the storage height. According to one embodiment, the server 120 may calculate the first height difference by subtracting the storage height from the total height of the microbial storage tank 110 stored in the database 130.

The server 120 may calculate the fifth time taken for the microbial raw material to decrease to the storage level. According to one embodiment, the server 120 calculates the fifth time by calculating the difference between the time at which the microbial raw material was supplied to fill the microbial storage tank 110 to a height corresponding to the entire height and the time at which the storage height was calculated. You can.

The server 120 may calculate the reduction rate per unit time by dividing the first height difference by the fifth time. According to one embodiment, the server 120 may calculate the reduction rate per unit time using Equation 4.

[Equation 4]

The server 120 may calculate the storage expiration time of the microbial raw material by dividing the storage height by the reduction rate per unit time. According to one embodiment, the server 120 may calculate the storage expiration time of the microbial raw material using Equation 5.

[Equation 5]

The server 120 may form a microbial raw material supply notification signal at the sixth time, which is the storage expiration time plus the fermentation time of the microbial raw material. According to one embodiment, the server 120 forms a microbial raw material supply notification signal at the sixth time by adding the fermentation time of the microbial raw material to the storage expiration time, and forms a microbial raw material supply notification signal at the sixth time taking into account the fermentation time of the microbial raw material and the storage expiration time of the microbial raw material. By forming a microbial raw material supply notification signal on time, a manager, etc. can be notified that microbial raw material needs to be supplied to the microbial storage tank 110.

The database 130 can store various data. The data stored in the database 130 is data acquired, processed, or used by at least one component of the microbial reservoir 110, the server 120, the database 130, and the ammonia sensor 140, May contain software (e.g. programs). Database 130 may include volatile and/or non-volatile memory. As an example, the database 130 may store information on the ejection cycle and ejection amount of microbial raw materials in the microbial reservoir, the water supply cycle of the water supply facility, etc.

The network N may perform wireless or wired communication between the microbial reservoir 110, the server 120, the database 130, the ammonia sensor 140, etc. For example, the network (N) may be LTE (long-term evolution), LTE-A (LTE Advanced), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless BroadBand), WiFi (wireless fidelity) , wireless communication can be performed using methods such as Bluetooth, NFC (near field communication), GPS (Global Positioning System), or GNSS (global navigation satellite system). For example, the network (N) is configured to perform wired communication according to methods such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). You may.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements this with a computer, and includes concepts such as machine learning and symbolic logic. may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data. Repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model may learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN) model, etc. As an example, an artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, CNN is a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolution layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back propagation. CNNs have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

Figure 2 is a diagram for explaining a server according to an embodiment.

As shown in Figure 2, server 120 may include one or more processors 122, one or more memory 124, and/or transceiver 126. In one embodiment, at least one of these components of server 120 may be omitted, or other components may be added to server 120. Additionally or alternatively, some components may be integrated and implemented, or may be implemented as a single or plural entity. At least some of the components inside and outside the server 120 are connected to each other through a bus, general purpose input/output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI), Data and/or signals can be sent and received.

One or more processors 122 may run software (eg, commands, programs, etc.) to control at least one component of the server 120 connected to the processor 122. Additionally, the processor 122 can perform various operations related to the present invention, such as calculation, processing, data generation, and processing. Additionally, the processor 122 may load data, etc. from one or more memories 124 or store them in one or more memories 124 .

One or more processors 122 may control the microbial raw material to be ejected from the microbial reservoir at a preset cycle. According to one embodiment, the processor 122 may control the microbial raw material to be ejected by a predetermined ejection amount from the microbial reservoir 110 at a preset cycle (e.g., 3 times a day, 6 hours, etc.).

One or more processors 122 may be controlled to generate microbial water by mixing microbial raw materials with water discharged from a water supply facility. According to one embodiment, the processor 122 may control the microbial raw material spouted from the microbial storage tank 110 to be mixed with the water spouted from the water supply facility to generate microbial water.

One or more processors 122 may control the discharge of microbial water. According to one embodiment, the processor 120 may control the discharge of microbial water mixed with microbial raw materials and water through the transceiver 126. For example, if the microbial water discharge port is blocked and the microbial water cannot be discharged, the processor 120 may drive a needle to remove the blockage of the microbial water discharge port.

One or more memories 124 may store various data. Data stored in the memory 124 is data acquired, processed, or used by at least one component of the server 120, and may include software (eg, commands, programs, etc.). Memory 124 may include volatile and/or non-volatile memory. In the present invention, a command or program is software stored in the memory 124, and is an operating system, application, and/or application for controlling the resources of the server 120 and various functions so that the application can utilize the resources of the server 120. It may include middleware provided to .

One or more memories 124 may store information on the ejection cycle and ejection amount of microbial raw materials in the above-described microbial reservoir, and the water supply cycle of the water supply facility. Additionally, one or more memories 124 may store instructions that, when executed by one or more processors 122, cause one or more processors 122 to perform operations.

As an example, the server 120 may further include a transceiver 126. The transceiver 126 may perform wireless or wired communication between the microbial reservoir 110, the server 120, the database 130, the ammonia sensor 140, and/or other devices. For example, the transceiver 126 may support enhanced Mobile Broadband (eMBB), Ultra Reliable Low-Latency Communications (URLLC), Massive Machine Type Communications (MMTC), long-term evolution (LTE), LTE Advance (LTE-A), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless Broadband), WiFi (wireless fidelity), Bluetooth, NFC ( Wireless communication can be performed using methods such as near field communication, GPS (Global Positioning System), or GNSS (global navigation satellite system). For example, the transceiver 126 may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard232 (RS-232), or plain old telephone service (POTS). there is.

As an example, one or more processors 122 may control the transceiver 126 to obtain information from the microbial reservoir 110, the server 120, the database 130, and the ammonia sensor 140. Information obtained from the microbial reservoir 110, server 120, database 130, and ammonia sensor 140 may be stored in one or more memories 124.

As an example, the server 120 may be a device of various types. For example, server 120 may be a portable communication device, a computer device, or a combination of one or more of the foregoing devices. The server 120 of the present invention is not limited to the above-described devices.

Various embodiments of the server 120 according to the present invention may be combined with each other. Each embodiment can be combined depending on the number of cases, and the embodiment of the server 120 created by combining the embodiments also falls within the scope of the present invention. Additionally, the internal/external components of the server 120 according to the present invention described above may be added, changed, replaced, or deleted depending on the embodiment. Additionally, the internal/external components of the aforementioned PLC may be implemented as hardware components.

Figure 3 is a diagram for explaining learning of a neural network according to an embodiment.

As shown in FIG. 3, the learning device can learn the neural network 123 to generate the amount of microbial raw material ejected according to the ammonia level using a machine learning algorithm. According to one embodiment, the learning device may be a separate entity from the server 120, but is not limited thereto.

The neural network 123 includes an input layer 121 through which training samples are input and an output layer 125 through which training outputs are output, and can be learned based on the difference between training outputs and labels. Here, the first labels may be defined based on ammonia levels. The neural network 123 is connected to a group of a plurality of nodes, and is defined by weights between connected nodes and an activation function that activates the nodes.

The learning device may learn the neural network 123 using a gradient descent (GD) technique or a stochastic gradient descent (SGD) technique. The learning device can use a loss function designed by the outputs and labels of the neural network.

The learning device can calculate the training error using a predefined loss function. The loss function may be predefined with a label, output, and parameter as input variables, where the parameter may be set by weights in the neural network 123. For example, the loss function may be designed in the form of MSE (Mean Square Error), entropy, etc., and various techniques or methods may be employed in embodiments in which the loss function is designed.

The learning device can use the backpropagation technique to find weights that affect the training error. Here, the weights are relationships between nodes in the neural network 123. The learning device can use the SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device can update the weights of the loss function defined based on the labels, outputs, and weights using the SGD technique.

According to one embodiment, the learning device acquires first labels that are the ejection amount of a predefined microbial raw material corresponding to the training ammonia level from the training ammonia level, and applies the predefined ejection amount of the microbial raw material to the first neural network. Thus, training outputs corresponding to the training ammonia levels are generated, and the first neural network can be learned based on the training outputs and first labels.

According to one embodiment, the learning device may generate training feature vectors based on size features, period features, and pattern features of the training ammonia level. Various methods can be employed to extract features.

According to one embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123. The learning device may learn the algorithm for calculating the ejection amount of microbial raw materials of the neural network 123 based on the training outputs and the first labels. The server 120 can calculate the ejection amount of microbial raw material corresponding to the ammonia level using the first neural network on which learning has been completed.

According to one embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123. The learning device may calculate the ejection amount of the microbial raw material from the ammonia level of the neural network 123 based on the training outputs and the first labels.

Figure 4 is a flowchart of a method for automating the discharge of microorganisms from livestock farms through remote control according to an embodiment.

Although the process steps, method steps, algorithms, etc. are described in the flow chart of FIG. 4 in a sequential order, such processes, methods, and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods and algorithms described in various embodiments of the invention do not need to be performed in the order described herein. Additionally, although some steps are described as being performed asynchronously, in other embodiments, some such steps may be performed concurrently. Additionally, the illustration of a process by depiction in the drawings does not mean that the illustrated process excludes other variations and modifications thereto, and that any of the illustrated process or steps thereof may be incorporated into any of the various embodiments of the invention. It does not imply that more than one is required, nor does it imply that the illustrated process is preferred.

As shown in Figure 4, in step S410, ammonia levels are detected. For example, referring to FIGS. 1 to 3, the server 120 of the microbial discharge automation system 100 for livestock farms through remote control controls the ammonia sensor 140 to detect the ammonia level of the microbial water discharge target. You can do it.

In step S420, the amount of microbial raw material ejected is calculated. For example, referring to FIGS. 1 to 3, the server 120 of the microbial discharge automation system 100 for livestock farms through remote control calculates the discharge amount of the microbial raw material using the ammonia level detected in step S410. can do. According to one embodiment, the server 120 may calculate the ejection amount of microbial raw material corresponding to the ammonia level using a machine learning algorithm.

In step S430, the ejection of microbial raw materials is controlled. For example, referring to FIGS. 1 to 3, the server 120 of the automated system 100 for discharging microorganisms from a livestock farm through remote control dispenses from the microbial storage tank 110 the amount of microbial raw material ejected as calculated in step S420. It is possible to control the ejection of microbial raw materials.

In step S440, microbial water production is controlled. For example, referring to FIGS. 1 to 3, the server 120 of the microbial discharge automation system 100 for livestock farms through remote control mixes the microbial raw material ejected in step S430 with the water ejected from the water supply facility. It can be controlled to generate microbial water.

In step S450, discharge of microbial water is controlled. For example, referring to FIGS. 1 to 3, the server 120 of the automated system 100 for discharging microorganisms in livestock farms through remote control discharges microbial water mixed with microbial raw materials and water through the network N. can be controlled. According to one embodiment, when the microbial water outlet is blocked and the microbial water cannot be discharged, the server 120 may drive a needle to remove the blockage of the microbial water outlet.

Figure 5 is an exemplary diagram of the configuration of a device according to an embodiment.

Device 501 according to one embodiment includes a processor 502 and memory 503. The device 501 according to one embodiment may be the server or terminal described above. The processor may include at least one device described above with reference to FIGS. 1 to 4 or may perform at least one method described with reference to FIGS. 1 to 4 . The memory 503 may store information related to the above-described method or store a program implementing the above-described method. Memory 503 may be volatile memory or non-volatile memory.

The processor 502 can execute programs and control the device 501. The code of the program executed by the processor 502 may be stored in the memory 503. The device 501 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and can exchange data.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the following claims.

Claims (5)

An automated system for discharging microorganisms from livestock farms through remote control,
A microbial storage tank for storing microbial raw materials;
a server that controls the microbial raw material to be ejected from the microbial storage tank at a preset cycle, controls the microbial raw material to be mixed with water discharged from a water supply facility to generate microbial water, and controls the discharge of the microbial water; and
It includes a database that stores information on the ejection cycle of the microbial raw material in the microbial storage tank and the water supply cycle of the water supply facility,
Further comprising an ammonia sensor that detects the first ammonia level of the microbial water discharge target,
The server is,
Calculating a first ejection amount of the microbial raw material for reducing the first ammonia level to a first threshold value, and controlling to eject a second ejection amount of the microbial raw material that is a predetermined ratio higher than the first ejection amount,
The server is,
Controlling the ammonia sensor to detect a second ammonia level of the microbial water discharge target after a first time has elapsed after ejecting the second ejection amount of the microbial raw material, and the first threshold value is greater than the second ammonia level. If the second ammonia level is greater than the first threshold, control to terminate the ejection of the microbial raw material, and control to eject a third ejection amount of the microbial raw material twice as much as the second ejection amount, Control the ammonia sensor to detect the third ammonia level of the microbial water discharge target a second time after ejecting the microbial raw material of the third ejection amount, and the first threshold value is greater than the third ammonia level. Controlling to terminate the ejection of the microbial raw material when the third ammonia level is greater than the first threshold, controlling to eject a fourth ejection amount of the microbial raw material twice as much as the third ejection amount, Controlling the ammonia sensor to detect a fourth ammonia level of the microbial water discharge target after a third time has elapsed after ejecting the fourth ejection amount of the microbial raw material, and the first threshold value is greater than the fourth ammonia level. Controlling to terminate the eruption of the microbial raw material when large,
An automated system for discharging microorganisms from livestock farms through remote control. delete delete According to paragraph 1,
The server is,
Controlling the ammonia sensor to detect the fifth ammonia level of the microbial water discharge target after a fourth time has elapsed after ejecting the second ejection amount of the microbial raw material, the fifth ammonia level and the first ammonia level are The difference is divided by the fourth time to calculate the ammonia level reduction rate per unit time, the ammonia level difference is calculated as the difference between the fifth ammonia level and the first threshold, and the ammonia level difference is divided by the ammonia level reduction rate per unit time. Calculating an expected ejection end time, and controlling to end ejection of the microbial raw material after the elapse of the ejection end expected time from the fourth time and generate a user alarm,
An automated system for discharging microorganisms from livestock farms through remote control. According to paragraph 1,
The server is,
Calculate the storage height of the microbial raw material stored in the microbial storage tank, calculate the first height difference between the total height of the microbial storage tank and the storage height, and calculate the fifth time taken for the microbial raw material to decrease to the storage height. Dividing the first height difference by the fifth time to calculate a reduction rate per unit time, dividing the storage height by the reduction rate per unit time to calculate a storage expiration time of the microbial raw material, and calculating the storage expiration time of the microbial raw material at the storage expiration time Forming a microbial raw material supply notification signal at the 6th hour plus the fermentation time of
An automated system for discharging microorganisms from livestock farms through remote control.