System for improving poultry productivity through artificial intelligence optimal environment control

Abstract

The present invention relates to a system for improving poultry productivity through artificial intelligence optimal environment control, and according to the present invention, there may be provided a system for improving poultry productivity through artificial intelligence optimal environment control, the system for improving poultry productivity comprising: an IoT scale (1) for measuring the weight of poultry and collecting internal air environment data; an external air sensor unit (2) for measuring external air environment data; a feeding amount measurement unit (3) for measuring a feeding amount; a water supply amount measurement unit (4) for measuring a water supply feeding amount; an automatic control apparatus (6) for controlling a poultry house apparatus (7); and a control server (8) for receiving weight data, environment data (the internal air environment data and the external air environment data), the feeding amount, the water supply amount, and control history data from the automatic control apparatus (6), deriving an average weight and rate of gain of the poultry, generating optimal environment control information through an artificial intelligence environment control model that has learned big data, and transmitting the same to the automatic control apparatus (6).

Description

Poultry productivity improvement system through artificial intelligence optimal environmental control

[0001] The present invention relates to a system for improving poultry productivity through artificial intelligence optimal environmental control, and more specifically, to a system for improving poultry productivity through artificial intelligence optimal environmental control, which can automatically control a poultry house in real time through an artificial intelligence environmental control model modeled through correlation analysis of productivity, water supply amount, feed amount, environmental data, and control history data collected in real time, thereby maintaining the environment of the poultry house in an optimal state and maximizing productivity (growth rate, egg laying rate).

[0002]

[0003] Generally, poultry such as chickens and ducks raised for laying eggs or for food are brought into poultry houses on farms as chicks and raised for a certain period of time before laying eggs or being shipped.

[0004] At this time, the weight of the poultry is included among the factors that determine the quality of the eggs or the brood, because the size of the eggs or the brood is determined by the weight of the livestock.

[0005] Accordingly, in the past, a method was used in which a person directly entered the poultry farm, caught each poultry, placed it on a scale, and measured its weight.

[0006] However, this method is labor-intensive, requires the hassle of sorting out each piece of poultry that has been measured, and has the disadvantage of making it difficult to accurately measure weight, especially since poultry tend to move around a lot.

[0007] Meanwhile, the breeding environment also has a great influence on the egg production rate or growth rate of poultry, so environmental management factors such as temperature, humidity, carbon dioxide, ammonia, ventilation, and various facilities (other control equipment) within the poultry house interact in a complex manner to affect the productivity (egg production rate or growth rate) of the poultry farm.

[0008] However, current poultry farms rely on manual control methods, making it difficult to maintain the condition of the poultry house in an optimal state at all times, consuming a lot of labor, and having limitations in preventing and responding to poultry diseases, making it difficult to secure high productivity.

[0009]

[0010] In order to solve the above problems, the present invention aims to provide a system for improving poultry productivity through artificial intelligence optimal environmental control, which can automatically control a poultry house in real time through an artificial intelligence environmental control model modeled through correlation analysis of productivity, water supply amount, feed amount, environmental data, and control history data collected in real time, thereby maintaining the environment of the poultry house in an optimal state and maximizing productivity (growth rate, egg laying rate).

[0011]

[0012] In order to solve the above problems, a system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention may provide a poultry productivity improvement system including an IOT scale that measures the weight of poultry and collects indoor environmental data; an outdoor sensor unit that measures outdoor environmental data; a feed amount measuring unit that measures the feed amount; a water amount measuring unit that measures the water amount; an automatic control device that controls a poultry house device, and a control server that receives weight data, environmental data (indoor environmental data and outdoor environmental data), feed amount, water amount, and control history data from the automatic control device, derives the average weight and growth rate of poultry, and generates optimal environmental control information through an artificial intelligence environmental control model that has learned big data and transmits the information to the automatic control device.

[0013] Here, the IOT scale may include a weight measuring unit for measuring the weight of poultry and a sensor unit provided on one side of the weight measuring unit for measuring the environment data.

[0014] In addition, the inside sensor unit may include at least one of a temperature sensor, a humidity sensor, a carbon dioxide sensor, and an ammonia sensor, and the outside sensor unit may include at least one of a temperature sensor, a humidity sensor, a carbon dioxide sensor, an ammonia sensor, a wind direction sensor, a wind speed sensor, and a fine dust sensor.

[0015] In addition, the feed amount measuring unit is characterized in that it is equipped with a load cell to measure the amount of feed received in the feeding device, measures the amount of feed that changes according to feeding, and calculates the amount of feed according to the difference.

[0016] Additionally, the poultry farm device may include one or more of a feeding device, a watering device, a heating device, a ventilation device, a cooling device, a lighting device, and a humidifying device.

[0017] In addition, the control server may include a database that stores management reference data and stores the collected weight data, environmental data, feeding amount, water supply amount, and control history data; a weight derivation unit that derives an average weight using a plurality of stored weight data; a weight gain derivation unit that derives a weight gain rate using the feeding amount and average weight; and a control generation unit that generates optimal environmental control information using an artificial intelligence environmental control model that has learned big data and transmits the information to the automatic control device.

[0018] In addition, the artificial intelligence environmental control model is characterized in that it generates the optimal environmental control information according to the average weight, growth rate, water supply amount, feeding amount, and environmental data by modeling through a normalization step of normalizing the collected average weight, growth rate, water supply amount, feeding amount, and environmental data; a correlation analysis step of analyzing the correlation between productivity and water supply amount, feeding amount, and environmental data; and an optimization step of deriving a final model by optimizing the correlation.

[0019] In addition, the correlation analysis step may include a one-dimensional correlation analysis step for analyzing a 1:1 correlation between productivity and water supply amount, feeding amount, and environmental data, respectively; a quantification step for generating environmental change data by quantifying changes in environmental data according to the control history data; and an n-dimensional correlation analysis step for analyzing the correlation between productivity and one or more of water supply amount, feeding amount, environmental data, and control history data based on the one-dimensional correlation using the environmental change data.

[0020] In addition, the control server may further include an entry / exit judgment unit that generates predicted shipment information through the average weight and generates necessary entry / exit information based on the predicted shipment information.

[0021] In addition, the method may further include an administrator terminal that receives one or more of weight data, environmental data, feeding amount, water supply amount, control history data, average weight, weight gain rate, and optimal environmental control information from the control server and provides the information to the administrator.

[0022]

[0023] The system for improving poultry productivity through artificial intelligence optimal environmental control according to the embodiment of the present invention as described above automatically controls the poultry house in real time through an artificial intelligence environmental control model modeled through correlation analysis of productivity, water supply amount, feed amount, environmental data, and control history data collected in real time, thereby maintaining the environment of the poultry house in an optimal state and maximizing productivity (growth rate, egg laying rate).

[0024] Additionally, it can have the effect of reducing feed costs and strengthening disease prevention.

[0025]

[0026] Figure 1 is a diagram illustrating a system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention.

[0027] Fig. 2 is an exemplary diagram illustrating a weight measurement unit of the IOT scale of Fig. 1.

[0028] Figure 3 is an example diagram showing a weight measuring unit of the IOT scale of Figure 1 formed in a different shape.

[0029] Fig. 4 is a cross-sectional view of the weight measuring unit of Fig. 3.

[0030] Figure 5 is a block diagram illustrating the control server of Figure 1.

[0031] Figure 6 is a flowchart schematically illustrating the process of modeling an artificial intelligence environment control model of a poultry productivity improvement system through artificial intelligence optimal environment control according to an embodiment of the present invention.

[0032] Figure 7 is a flowchart sequentially illustrating S2 of Figure 6.

[0033]

[0034] A system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention may provide a poultry productivity improvement system including an IoT scale that measures the weight of poultry and collects indoor environmental data; an outdoor sensor unit that measures outdoor environmental data; a feed amount measuring unit that measures the feed amount; a water amount measuring unit that measures the water amount; an automatic control unit that controls a poultry house device, and a control server that receives weight data, environmental data (indoor environmental data and outdoor environmental data), feed amount, water amount, and control history data from the automatic control unit, derives an average weight and growth rate of poultry, and generates optimal environmental control information through an artificial intelligence environmental control model that has learned big data and transmits the information to the automatic control unit.

[0035]

[0036] The following description of the present invention with reference to the drawings is not limited to specific embodiments, and various modifications and embodiments may be made. Furthermore, the following description should be understood to encompass all modifications, equivalents, and alternatives within the spirit and technical scope of the present invention.

[0037] In the following description, terms such as first, second, etc. are used to describe various components, and are not limited in meaning in themselves, but are used only for the purpose of distinguishing one component from another.

[0038] Like reference numbers used throughout this specification represent like components.

[0039] As used herein, singular expressions include plural expressions unless the context clearly dictates otherwise. In addition, terms such as "comprise," "include," or "have" used herein should be interpreted to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, and should be understood to not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0040] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached FIGS. 1 to 7.

[0041]

[0042] FIG. 1 is a block diagram illustrating a system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention, FIG. 2 is an exemplary diagram illustrating a weight measurement unit of an IOT scale of FIG. 1, FIG. 3 is an exemplary diagram illustrating a shape in which the weight measurement unit of the IOT scale of FIG. 1 is formed in a different shape, FIG. 4 is a cross-sectional view of the weight measurement unit of FIG. 3, FIG. 5 is a block diagram illustrating a control server of FIG. 1, FIG. 6 is a flowchart schematically illustrating a process in which an artificial intelligence environmental control model of a system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention is modeled, and FIG. 7 is a flowchart sequentially illustrating S2 of FIG. 6.

[0043]

[0044] The system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention automatically controls the environment of a poultry house through artificial intelligence to maintain an optimal environment at all times, thereby improving the growth rate or laying rate.

[0045]

[0046] Referring to FIG. 1, a system for improving poultry productivity through artificial intelligence optimal environment control according to an embodiment of the present invention may include an IOT scale (1), an outdoor sensor unit (2), a feed amount measuring unit (3), a water amount measuring unit (4), a camera (5), an automatic control device (6), a poultry house device (7), a control server (8), and an administrator terminal (9).

[0047] One or more IoT scales (1) are installed within a poultry farm to measure the weight of poultry in real time, thereby acquiring a large amount of weight data. Furthermore, environmental data can be collected.

[0048] For this purpose, the IOT scale (1) may include a weight measurement unit (10) and a weight sensor unit (not shown).

[0049] The weight measuring unit (10) is installed in the poultry house to measure the weight of poultry, and the measured weight data can be transmitted to the control server (8).

[0050] The weight measuring unit (10) can be formed to reflect the behavioral habits of the poultry so that the poultry itself can climb up and measure its weight. For example, as shown in Fig. 2, it can include a cell plate (11a), a cell moving pipe (12a), a guide holder (13a), a cell control unit (14a), and a cell support (15a). However, this is only an embodiment of the present invention and is not limited thereto.

[0051] The cell plate (11a) is a space for accommodating poultry, and when the poultry climbs onto the cell plate (11a) of the weight measuring unit (10), its weight can be measured.

[0052] In addition, the cell plate (11a) may be formed in a circular plate shape, but this is only an embodiment of the present invention and is not limited thereto.

[0053] The cell moving pipe (12a) supports the cell plate (11a) and can be moved up and down. To this end, the lower end of the cell moving pipe (12a) is connected to the cell plate (11a) to fix the cell plate (11a), and the upper end of the cell moving pipe (12a) can be connected to the cell control unit (14a) so as to be able to move up and down.

[0054] Additionally, the upper part of the cell plate (11a) can be inserted into a guide holder (13a) and connected to a cell control unit (14a), with the guide holder (13a) acting as a guide for up-and-down movement.

[0055] That is, when poultry climbs onto the cell plate (11a), the cell moving pipe (12a) moves downward. At this time, the degree to which the cell moving pipe (12a) moves downward varies depending on the weight of the poultry.

[0056] Additionally, when the poultry is on the cell plate (11a) and then goes down, the cell moving pipe (12a) rises upward and is positioned in place.

[0057] The guide holder (13a) can guide the movement of the cell moving pipe (12a) and fix the cell moving pipe (1a) so that it does not shake.

[0058] In addition, the guide holder (13a) has a hole formed in the center, so that the cell moving pipe (12a) can move up and down along the hole.

[0059] The cell control unit (14a) is connected to a guide holder (13a) and a cell moving pipe (12a) on the lower side, and is equipped with a load cell to measure weight according to the lower movement of the cell moving pipe (12a) and transmit the measured weight data to the control server (8).

[0060] That is, the cell moving pipe (12a) moves downward according to the weight of the poultry, and the load cell is stretched and deformed as much as it moves downward, and the deformation amount is detected as an electric signal by a deformation measuring device and converted into a digital signal to derive the weight as a number, thereby measuring the weight of the poultry.

[0061] At this time, the weight data represents the weight value of the poultry transmitted from the weight measurement unit (10).

[0062] The cell support (15a) has its upper end connected to the cell control unit (14a) to support the cell control unit (14a), and can be installed on the ground inside the poultry house.

[0063] As another example, the weight measuring unit (10) may be formed as shown in FIGS. 3 and 4.

[0064] Referring to FIGS. 3 and 4, the weight measuring unit (10) may include a feed input unit (11b), a poultry receiving unit (12b), a feed trough (13b), a load cell (14b), a status detection module (15b), a sterilizing device (not shown), and a flexible duct (16b).

[0065] The feed input section (11b) can be formed in a cylindrical or polygonal shape as a central body forming a certain height, and a feed input path (110b) can be formed in a vertical direction inside.

[0066] Here, the feed input path (110b) can form a branch line according to the number of poultry receiving sections (12b) described later, and an opening / closing valve (111b) for opening and closing the line can be provided for each branch line.

[0067] The poultry receiving section (12b) can be provided with a space that can accommodate only one poultry. To this end, a support plate (120b) having an area where one poultry can be placed is provided, and side walls (121b) can be erected vertically on both sides of the support plate (120b).

[0068] In addition, a plurality of poultry receiving sections (12b) may be provided along the perimeter of the feed input section (11b), and a door (122b) that can be opened or closed inside may be provided.

[0069] That is, it is a form in which a plurality of poultry receiving portions (12b) are arranged at equal intervals and in the same shape along the perimeter of the feed input portion (11b), which is the central body, and each shape is a structure in which a door (122b) opens or closes a poultry receiving space formed by a support plate (120b) and two side walls (121b).

[0070] At this time, the door (122b) is preferably structured to open by rotating upward and close by rotating downward, and can be connected to the feed input section (11b) or both side walls (121b) by an axis, and a motor (not shown) can be connected to one side of the axis so that it can automatically rotate.

[0071] Additionally, the door (122b) may be made of a transparent material to facilitate observation of the inside of the housed poultry when closed.

[0072] The feed trough (13b) may be provided inside the poultry receiving section (12b) so as to be connected to the feed input trough (11b). That is, when feed is input into the feed input trough (110b), the feed may be supplied in divided portions to the connected feed troughs (13b).

[0073] The load cell (14b) measures the weight of the poultry received in the poultry receiving unit (12b), and the status detection module (15b) can detect the status of the poultry received in the poultry receiving unit (12b) in real time.

[0074] The sterilizing device (not shown) is a device that generates and supplies a sterilizing agent to prevent reactive oxygen species or other infectious diseases, and the flexible duct (17b) may be a connecting pipe connecting the sterilizing device (not shown) and the door (122b).

[0075] If the condition of the poultry housed through the sterilizer and flexible duct (17b) is abnormal and it is determined that it is due to a livestock disease, the control server (8) can control the supply of a sterilizing agent into the poultry house (12b) through the sterilizer while keeping the door (122b) closed.

[0076] In this way, when a weight measurement unit (10) is formed, it is possible to determine an unusual weight, such as when another animal enters, through the weight data of the load cell (14b) installed in the poultry receiving unit (12b) that can only accommodate one poultry. In this case, when an unusual weight is determined through the control server (8), the door (122b) can be forcibly closed to catch the animal, etc., and to prevent damage to the poultry.

[0077] In addition, even if the condition of the poultry received through the condition detection module (15b) is determined to be abnormal, the door (122b) can be forcibly closed to capture and confirm the poultry.

[0078] The betting sensor unit is installed on one side of the weight measurement unit (10) to measure the betting environment of the poultry house in real time, thereby obtaining betting environment data. At this time, a time cycle can be set so that measurements can be made at regular time intervals, but this can be easily changed according to the settings.

[0079] Such a sensor unit may include one or more of a temperature sensor, a humidity sensor, a carbon dioxide sensor, and an ammonia sensor, but is not limited thereto and may further include a wider variety of sensors.

[0080] Accordingly, the sensor unit can obtain one or more of temperature, humidity, carbon dioxide, and ammonia environment data and transmit them to the control server (8).

[0081] In this way, by providing a weight measuring unit (10) installed in a poultry house with a sensor unit, the environment of the poultry house can be measured from the poultry's perspective, allowing the poultry house environment to be more accurately controlled to a suitable growing environment for the poultry.

[0082] In other words, because there is a significant difference in temperature, humidity, etc. measured according to height within a poultry house, temperature, humidity, etc. must be measured at the location where the poultry is, that is, from the poultry's perspective, in order to more accurately measure the growing environment that affects the poultry.

[0083] The outdoor air sensor unit (2) is installed in a poultry house to measure the outdoor air environment of the poultry house in real time, thereby obtaining outdoor air environment data. At this time, a time cycle can be set so that measurements can be made at regular time intervals, but this can be easily changed according to the settings.

[0084] This external sensor unit (2) may include one or more of a temperature sensor, a humidity sensor, a carbon dioxide sensor, an ammonia sensor, a wind direction sensor, a wind speed sensor, and a fine dust sensor, but is not limited thereto and may further include a wider variety of sensors.

[0085] Accordingly, the outdoor air sensor unit (2) can obtain one or more outdoor air environment data among temperature, humidity, carbon dioxide, ammonia, wind direction, wind speed, and fine dust and transmit them to the control server (8).

[0086] The feed amount measuring unit (3) is installed in the feeding device that supplies feed and can measure the feed amount.

[0087] At this time, the feed amount measuring unit (3) is equipped with a load cell to measure the amount of feed (feed weight) contained in the feeding device, and when a change in the feed weight is detected according to feeding, the changed feed amount (feed weight) is measured, and the difference between the feed amount before the change and the feed amount after the change is calculated to calculate the feed amount.

[0088] The feed amount measuring unit (3) can transmit the feed amount measured in this way to the control server (8).

[0089] The water supply amount measuring unit (4) is installed in a water supply device that supplies water and can measure the water supply amount and transmit the measured water supply amount to the control server (8).

[0090] The camera (5) can capture images of the poultry house and transmit the acquired image data to the control server (8). This not only allows the manager to monitor the status of the poultry house in real time, but also allows the control server (8) to detect the occurrence of diseases, emergencies, etc. and quickly notify the manager.

[0091] The automatic control device (6) controls the device (7) of the poultry farm, and can automatically control the device (7) of the poultry farm according to the control value set by the manager terminal (9) or the manager, or can automatically control the device (7) of the poultry farm according to the optimal environment control information received from the control server (8).

[0092] In addition, the automatic control device (6) can transmit control history data, which is information on the history of controlling the poultry farm device (7) as described above, to the control server (8).

[0093] Here, the poultry house device (7) refers to all devices installed in the poultry house, and the operation of which can be controlled by the automatic control device (6). For example, it may include one or more of a feeding device (feed pump, feeding motor, etc.), a watering device, a heating device, a ventilation device (fan, etc.), an air conditioning device, a lighting device, and a humidifying device (sprayer, etc.), but a wider variety of devices may be included.

[0094] The control server (8) receives weight data, environmental data (internal environmental data and external environmental data), feed amount, water amount, and control history data in real time, derives the average weight and growth rate of poultry, and generates optimal environmental control information through an artificial intelligence environmental control model that has learned big data, and transmits the information to the automatic control device (6).

[0095] At this time, environmental data, feeding amount, water supply amount, and control history data are collected in real time, which means they can be called hourly data (data according to time).

[0096] Meanwhile, it is desirable for the control server (8) to derive the average weight and weight gain rate at regular time intervals using the weight data and feeding amount collected over a certain period of time with a time period set, but it is not limited thereto.

[0097] Referring to FIG. 5, the control server (8) may include a database (80), a weight derivation unit (81), a weight gain derivation unit (82), a control generation unit (83), and an entry / exit judgment unit (84).

[0098] The database (80) can store management standard data, and can store collected weight data, environmental data, feeding amount, water supply amount, control history data, image data, etc.

[0099] Here, management standard data may include, but is not limited to, a specification management manual, shipping standard information, poultry house capacity information, disease judgment information, etc., and may include various types of information necessary for raising poultry.

[0100] Additionally, the database (80) can store average weight, weight gain rate, optimal environment control information, etc. in real time.

[0101] The weight derivation unit (81) can derive an average weight using a plurality of stored weight data. Meanwhile, when preprocessing the collected weight data, weight data that has already been used is excluded from consideration.

[0102] For example, the weight derivation unit (81) can derive the average weight of poultry by densifying the collected weight data using a statistical method utilizing a histogram and normal distribution.

[0103] More specifically, the weight derivation unit (81) preprocesses weight data collected over a certain period of time in real time, accumulates the preprocessed weight data to create a histogram according to weight and frequency, estimates a smooth density function by applying kernel density estimation using a Gaussian filter to the histogram, and can derive an average weight through the estimated smooth density function.

[0104] At this time, the weight derivation unit (81) can generate a histogram through mathematical expression 1, and the x-axis of the generated histogram can be the weight, and the y-axis can be the frequency of the weight measured from the IOT scale (1).

[0105] [Mathematical Formula 1]

[0106]

[0107] Here, x is the weight, n is the number of collected weight data, h is the width value of the Gaussian filter, Count(x) is the frequency of x, and Width(X) is the size of the histogram bin. Also, h is preferably 1.

[0108] In addition, the weight derivation unit (81) can estimate the smooth density function through mathematical expression 2.

[0109] [Equation 2]

[0110]

[0111] Here, x is the weight, h D is the width value in the histogram, P KDE (x) is the density estimation result using the Gaussian filter corresponding to x, K is the Gaussian filter function, N is the Gaussian filter size, D is a parameter to reflect the size of the histogram bin in defining the width of the Gaussian filter, xx (i is the filter size to which a Gaussian filter is applied from filter size 1 to N based on the x to be calculated.

[0112] At this time, K is as shown in the following mathematical formula 3 and can be 1, and N is preferably 49.

[0113] [Equation 3]

[0114]

[0115] In this way, the reason why N is defined as 49 is that the kernel size is 49g, and the Gaussian mean of the reference x-24 to x+24 is obtained to P of x. KDE Because it is calculated as (x).

[0116] At this time, in the case of kernel density estimation, it can be considered as the sum of smooth bumps placed on the observation point (x-axis weight), and the estimated smooth density function determines the raised shape, h D is called the amplitude and determines the width.

[0117] The above amplitude determines the overall graph shape and can play an important role in estimating the smooth density function. h D 1 is preferable.

[0118] In addition, the weight derivation unit (81) determines a cluster, which is a section formed in a raised shape in a smooth density function, selects the highest cluster among the clusters (the cluster with the highest frequency), and derives the maximum value of the highest cluster as the average weight of the poultry.

[0119] As another example, the weight derivation unit (81) may derive an average weight by adding up the weight data measured from two or more IOT scales (1) and dividing the result by the sum of the number of poultry housed in the two or more IOT scales (1). This is one of the methods used when the IOT scale (1) is formed as a cage-type scale, but is not limited thereto.

[0120] The weight gain derivation unit (82) can derive the weight gain rate using the average weight derived from the weight derivation unit (81) and the amount of feed received. At this time, the weight gain rate can be obtained using the following mathematical formula 4.

[0121] [Equation 4]

[0122] Weight gain rate (%) = average weight (g) / feed amount (g) × 100

[0123] The control generation unit (83) can generate optimal environmental control information through an artificial intelligence environmental control model that has learned big data and transmit it to the automatic control device (6).

[0124] Here, the artificial intelligence environment control model is modeled based on machine learning, and more specifically, it is based on supervised learning, but is not limited to this.

[0125] Let us examine the modeling process of the artificial intelligence environmental control model in more detail with reference to Figures 6 and 7.

[0126] The artificial intelligence environmental control model can be modeled through the normalization stage (S1), correlation analysis stage (S2), and optimization stage (S3).

[0127] The normalization step (S1) normalizes the collected big data. This step can normalize collected data such as average weight, growth rate, water supply, feeding amount, and environmental data. This step aims to eliminate relative size differences between each data set, thereby improving the accuracy of correlation analysis.

[0128] The correlation analysis step (S2) can analyze the correlation with productivity using one or more of normalized water supply amount, feeding amount, and environmental data.

[0129] Here, productivity can be considered as a breeding rate or egg production rate, which in the present invention can be considered equivalent to a growth rate. Therefore, correlation analysis can be conducted using the collected growth rate as productivity.

[0130] More specifically, step S2 may include a one-dimensional correlation analysis step (S20), a quantification step (S21), and an n-dimensional correlation analysis step (S22).

[0131] The one-dimensional correlation analysis step (S20) can analyze the 1:1 correlation between productivity, water supply, feeding amount, and environmental data. At this time, correlations can be analyzed for each element included in the environmental data (e.g., temperature, humidity, etc.).

[0132] The 1:1 correlation mentioned here means that the correlation between productivity and water supply, productivity and feeding amount, etc. are analyzed 1:1.

[0133] The quantification step (S21) can generate environmental change data by quantifying changes in environmental data according to control history data. By quantifying how environmental data changes according to the control history of the poultry house device (7), correlation analysis can be performed more accurately, thereby enabling accurate judgment on how to control the poultry house device (7) depending on the desired environmental conditions.

[0134] The n-dimensional correlation analysis step (S22) can analyze the correlation between productivity, average weight, water supply amount, feeding amount, environmental data, and control history data based on a one-dimensional correlation using environmental change data.

[0135] The n-dimensional correlation discussed here means that the correlation between productivity, water supply amount, feeding amount, environmental data, and control history data is analyzed for each n-individual.

[0136] For example, it can be said that the correlation between productivity, environmental data, temperature, and control history data, productivity, water supply, environmental data, humidity, and control history data are each analyzed.

[0137] The optimization stage (S3) can derive a productivity correlation (final model) by synthesizing and optimizing the analyzed correlations.

[0138] The control generation unit (83) can generate optimal environmental control information based on average weight, weight gain rate, and environmental data through the artificial intelligence environmental control model modeled in this manner.

[0139] In addition, when the expected shipping date is received through the administrator terminal (9), the control generation unit (83) can generate optimal environmental control information through an artificial intelligence environmental control model based on the management standard data by reflecting the expected shipping date.

[0140] The stocking / shipping judgment unit (84) can generate predicted shipment information based on average weight using management standard data and generate required stocking information based on the predicted shipment information. The predicted shipment information and required stocking information generated in this manner can be transmitted to the manager terminal (9) and provided to the manager.

[0141] Additionally, the control server (8) may further include a management decision unit (not shown).

[0142] The management judgment unit can determine whether a disease has occurred by using the average weight and growth rate derived from the collected environmental data, image data, etc., and if a disease has occurred, a warning message can be sent to the management terminal (9).

[0143] This allows managers to respond quickly and early, minimizing the damage caused by the disease.

[0144] In addition, the management judgment unit can analyze the video data and provide a notification message to the manager terminal (9) if it determines that an emergency situation has occurred within the poultry farm.

[0145] In addition, if the management judgment unit determines that the amount of feed contained in the feeding device measured by the feed amount measurement unit (3) is below a certain standard, it can transmit a message such as ‘Feed supplementation required’ to the management terminal (9).

[0146] In addition, the management judgment unit analyzes the control history data and the environmental data within a certain time range after the control of the control history data, and if the change in the environmental data according to the control history data does not fall within a desirable range, a message such as 'device inspection required' for the device (7) of the poultry farm can be transmitted to the management terminal (9).

[0147] In addition, the management judgment unit can analyze the weight data and, if an unusual weight is detected, close the door (122b) of the weight measurement unit (1), and, if the condition of the poultry detected by the condition detection module (15b) is determined to be abnormal, close the door (122b) and operate the sterilization device.

[0148] The administrator terminal (9) may be a mobile terminal of an administrator, such as a poultry farm breeder, and may be easily applied to a PC, tablet, etc. in addition to a mobile terminal.

[0149] In this way, the administrator terminal (9) is implemented as a mobile terminal, so that mobility and convenience are easy, and the administrator can monitor and control remotely.

[0150] These administrator terminals (9) have the application provided by the system of the present invention downloaded, and can receive one or more of weight data, environmental data, feed amount, water amount, control history data, average weight, growth rate, and optimal environment control information from the control server (8) and provide them to the administrator. In addition, they can receive control values ​​from the administrator and remotely control the poultry farm device (7) through the control server (8).

[0151]

[0152] As described above, the system for improving poultry productivity through artificial intelligence optimal environmental control according to an embodiment of the present invention automatically controls the poultry house in real time through an artificial intelligence environmental control model modeled through correlation analysis of productivity, water supply amount, feed amount, environmental data, and control history data collected in real time, thereby maintaining the environment of the poultry house in an optimal state and maximizing productivity (growth rate, egg laying rate).

[0153] Additionally, it can have the effect of reducing feed costs and strengthening disease prevention.

[0154]

[0155] The embodiments of the present invention described above are not implemented only through devices and / or methods, but may also be implemented through programs for realizing functions corresponding to the configurations of the embodiments of the present invention, recording media on which the programs are recorded, etc., and such implementations can be easily implemented by an expert in the technical field to which the present invention pertains based on the description of the embodiments described above.

[0156] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present invention defined in the following claims also fall within the scope of the present invention.

Claims

1. In a system for improving poultry productivity through artificial intelligence optimal environmental control, IOT scales that measure the weight of poultry and collect environmental data; An outdoor sensor unit that measures outdoor environmental data; Feeding amount measuring unit for measuring the feeding amount; A water supply measuring unit that measures the water supply amount; Automatic control device for controlling the poultry farm equipment and A poultry productivity improvement system including a control server that receives weight data, environmental data (internal environmental data and external environmental data), feed amount, water amount, and control history data from the automatic control device, derives the average weight and growth rate of poultry, and generates optimal environmental control information through an artificial intelligence environmental control model that has learned big data and transmits the information to the automatic control device.

2. In paragraph 1, The above IOT scale is, A weight measuring unit for measuring the weight of poultry and A poultry productivity improvement system including a sensor unit provided on one side of the above weight measuring unit for measuring environmental data.

3. In paragraph 2, The above betting sensor part, Containing at least one of a temperature sensor, a humidity sensor, a carbon dioxide sensor and an ammonia sensor, The above external sensor part, A poultry productivity improvement system comprising one or more of a temperature sensor, a humidity sensor, a carbon dioxide sensor, an ammonia sensor, a wind direction sensor, a wind speed sensor, and a fine dust sensor.

4. In paragraph 1, The above feed amount measuring unit is, A poultry productivity improvement system characterized by having a load cell installed to measure the amount of feed received in a feeder, measuring the amount of feed that changes depending on the feed, and calculating the amount of feed based on the difference.

5. In paragraph 1, The above poultry farm device is, A poultry productivity improvement system comprising at least one of a feeding device, a watering device, a heating device, a ventilation device, a cooling device, a lighting device, and a humidifying device.

6. In paragraph 1, The above control server, A database that stores management standard data and stores the collected weight data, environmental data, feeding amount, water supply amount, and control history data; A weight derivation unit that derives an average weight using a plurality of stored weight data; A weight gain rate derivation section that derives the weight gain rate through the above-mentioned feeding amount and average weight, and A poultry productivity improvement system including a control generation unit that generates optimal environmental control information through an artificial intelligence environmental control model that has learned big data and transmits the information to the automatic control device.

7. In paragraph 6, The above artificial intelligence environmental control model is, Normalization step to normalize the collected average weight, growth rate, water supply amount, feeding amount and environmental data; Correlation analysis step to analyze the correlation between productivity, water supply, feeding amount and environmental data. It is modeled through an optimization step that derives the final model by optimizing the correlation. A poultry productivity improvement system characterized by generating the optimal environmental control information according to the average weight, weight gain rate, and environmental data.

8. In paragraph 7, The above correlation analysis step is, A one-dimensional correlation analysis step that analyzes the 1:1 correlation between productivity, water supply amount, feeding amount, and environmental data; A quantification step for generating environmental change data by quantifying changes in environmental data according to the above control history data, and A poultry productivity improvement system including an n-dimensional correlation analysis step for analyzing correlations between productivity and one or more of water supply amount, feeding amount, environmental data, and control history data based on one-dimensional correlations using the above environmental change data.

9. In paragraph 6, The above control server, A poultry productivity improvement system further comprising an entry / exit judgment unit that generates predicted entry information through the above average weight and generates necessary entry / exit information based on the predicted entry information.

10. In paragraph 1, A poultry productivity improvement system further comprising an administrator terminal that receives one or more of weight data, environmental data, feed amount, water amount, control history data, average weight, weight gain rate, and optimal environmental control information from the control server and provides the same to the administrator.