NL2028971A - System and method for recognizing dynamic anomalies of multiple livestock equipment in smart farm system - Google Patents

System and method for recognizing dynamic anomalies of multiple livestock equipment in smart farm system Download PDF

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NL2028971A
NL2028971A NL2028971A NL2028971A NL2028971A NL 2028971 A NL2028971 A NL 2028971A NL 2028971 A NL2028971 A NL 2028971A NL 2028971 A NL2028971 A NL 2028971A NL 2028971 A NL2028971 A NL 2028971A
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state data
central server
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smart environment
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Park Hyeon
Han Kim Se
Heon Park Dae
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Electronics & Telecommunications Res Inst
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Abstract

Provided is a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system. The system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system includes a smart environment providing system configured to collect state data including environment information of a livestock house and driving information of driving equipment, to provide the collected state data to a central server, to select, when list information on predictive models for determining anomalies of the driving equipment from the central server, a predictive model matched to collected state data from the predictive model list, and to transfer the selected predictive model to the central server, and a central server configured to receive the state data collected by the smart environment providing system, to accumulatively store the received state data, to generate a predictive model based on the state data provided from each smart environment providing system, to store and manage the generated predictive models in a storage list pool, to provide a stored predictive model list to the smart environment providing system, to determine, when information on a prediction target predictive model from the smart environment providing system, anomalies of the driving equipment using the corresponding predictive model, and to provide a result to the corresponding smart environment providing system.

Description

System and method for recognizing dynamic anomalies of multiple livestock equipment in smart farm system CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of Korean Patent Application No. 10-2020-0102092, filed on August 13, 2020, the disclosure of which is incorporated herein by reference in its entirety.
TECHNICAL FIELD The present disclosure relates to a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system, and more particularly, to a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system to determine whether automation equipment required for building a smart farm system are broken down.
BACKGROUND Recently, livestock farms have significantly increased in size compared to the past, and thus, interest in automated livestock smart environment providing system has increased.
The smart environment providing system increases productivity of livestock by maintaining an environment suitable for livestock growth conditions in livestock houses.
A suitable environment here may be maintained by building and controlling many equipment inside and outside the livestock, that is, temperature, humidity, CO: ammonia sensors and control equipment such as exhaust fans, flow fans, cooling pads, radiators, and the like.
However, the sensors and control equipment of the smart environment providing system have may be easily broken down due to poor environments such as closed livestock spaces and lack of stable power supply, but is difficult to determine whether the equipment is broken down.
In general, operations of these control equipment is performed according to initial installation and setting of livestock farms, and afterwards, monitoring for control equipment is insufficient, and even if monitoring is performed, collected data may not be systematically managed and analyzed, so that it is not easy to accurately and rapidly determine an error of installed equipment, that is, it is not easy to detect a state or an error of a sensor, a state of a controller, an abnormal state such as an error, and malfunction.
Due to this, a suitable environment is not maintained, which significantly affects productivity of livestock.
Moreover, there are various types of livestock houses in the country, and various multiple equipment is installed in each livestock house.
There is a need for a method for quickly detecting anomalies adaptively to an environment of each livestock house at the same time using these various and many equipment.
SUMMARY Accordingly, the present disclosure provides a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system, to simultaneously construct anomalies analysis models of multiple livestock equipment through information collected from multiple equipment (environment sensor and driving equipment) and dynamically applying the anomalies analysis models of multiple livestock equipment to livestock houses to rapidly detect malfunction of equipment adaptively to each livestock farm.
In one general aspect, a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system includes: a smart environment providing system configured to collect state data including environment information of a livestock house and driving information of driving equipment, to provide the collected state data to a central server, to select, when list information on predictive models for determining anomalies of the driving equipment from the central server, a predictive model matched to collected state data from the predictive model list, and to transfer the selected predictive model to the central server; and a central server configured to receive the state data collected by the smart environment providing system, to accumulatively store the received state data, to generate a predictive model based on the state data provided from each smart environment providing system, to store and manage the generated predictive models in a storage list pool, to provide a stored predictive model list to the smart environment providing system, to determine, when information on a prediction target predictive model from the smart environment providing system, anomalies of the driving equipment using the corresponding predictive model, and to provide a result to the corresponding smart environment providing system.
The smart environment providing system may include: a plurality of sensors configured to collect state data including environment sensing information including at least one of a temperature, a humidity, CO», and ammonia information; a driving equipment configured to drive to form an environment of the livestock house and to provide state data including corresponding driving information; and a local server configured to store the state data provided from the sensors and the driving equipment in a data set database and to provide the state data stored in the data set database to the central server.
The local server may include: a data collecting unit configured to collect the state data and to store the collected state data in a database; a data preprocessing unit configured to preprocess the collected state data and to store the preprocessed state data in a refinement database; a data providing unit configured to provide the refined state data stored in the refinement database to the central server; a predictive model selecting unit configured to receive a list of learned predictive models from the central server, to select a predictive model from the list of the predictive models provided based on the collected stat data, and to provide the selected predictive model information to the central server; and a malfunction detecting unit configured to detect a compared malfunction of the driving equipment according to a state data comparison result through the selected predictive model in the central server.
The local server may be configured to dynamically receive a predictive model list from the central server, and the predictive model list may include state data instance information of the driving equipment and the sensors applied to the predictive model.
The local server may be configured to identify the predictive model of the driving equipment by an instance which is an entity for recognizing each equipment.
The central server may include: a data collecting unit configured to collect state data of the sensor and the driving equipment of each livestock house from the smart environment providing system of each livestock house; a predictive model generating unit configured to generate a predictive model for detecting anomalies using the collected state data; and a model list providing unit configured to dynamically distribute the generated predictive model to the smart environment providing system of a corresponding livestock farm.
The central server may be configured to store and manage the generated learning model in a learning model pool.
The central server may be configured to execute a predictive model based on state data corresponding to the identified instance, and to determine anomalies of each equipment through a corresponding predictive result value.
In another general aspect, a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system includes: collecting, by a smart environment providing system, state data including environment information of a livestock house and driving information of driving equipment and providing the collected state data to a central server; receiving, by the central server, the state data collected by the smart environment providing system, and accumulating and storing the received state data; generating, by the central server, a predictive model based on state data provided through each smart environment providing system, and storing and managing the generated predictive models in a storage list pool; providing, by the central server, the stored predictive model! list to the smart environment providing system; when list information on a predictive model for determining anomalies of driving equipment is received from the central server, selecting, by the smart environment providing system, a predictive model matched to the collected state data from the predictive model list and transferring the selected predictive model to the central server; and when information on a prediction target predictive model is received from the smart environment providing system, determining, by the central server, anomalies of the driving equipment using the corresponding predictive model and providing a result to the corresponding smart environment providing system.
The collecting of the state data and providing of the collected data to the central server by the smart environment providing system may include: collecting state data including one or more environment sensing information among a temperature, a humidity, CO», and ammonia information; driving to form an environment of a livestock house, and providing state data including the driving information; storing state data provided from the sensors and the driving equipment in a data set database; and providing the state data stored in the data set database to the central server.
In the selecting of the predictive model matched to the collected state data and transferring the selected predictive model to the central server, the predictive model list from the central server may include instance information matched to state data to be applied to each predictive model, and a predictive model to be applied may be selected by comparing collected state information with an instance of the predictive model by the smart environment providing system.
In the providing of the state data stored in the data set database to the 5 central server by the smart environment providing system, the collected state data may be preprocessed and preprocessed state data may be stored in a refinement database.
The accumulating and storing of the received state data by the central server may include: collecting state data of a sensor and driving equipment from a smart environment providing system of each livestock house; generating a predictive model for detecting anomalies using the collected state data; and storing the generated model in a pool and dynamically distributing a predictive model list sto9red in the pool to the smart environment providing system.
The generated predictive model may be stored and managed in a leaning model pool by the central server.
When state information is received from the smart environment providing system, it may be determined whether there is a predictive model stored in the learning model pool based on the state information as an instance by the central server, and when there is a predictive model stored in the learning model pool, the predictive model may be learned through the state information.
The method may further include: when the predictive model stored in the learning model pool is learned, dynamically providing, by the central server, a predictive model list stored in the learning model pool to each smart environment providing system.
According to an embodiment of the present disclosure, even with equipment of any livestock environment from collecting equipment information to recognition of anomalies, a model may be dynamically distributed each time a leaning model is updated from collecting and learning information related to the equipment of livestock house to extraction of model, and prediction and result information may be provided.
Other features and aspects will be apparent from the following detailed description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a functional block diagram illustrating a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to the present disclosure.
FIG. 2 is a conceptual diagram of a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to the present disclosure.
FIG. 3 is a functional block diagram illustrating a smart environment providing system of a livestock farm of FIG. 1.
FIG. 4 is a functional block diagram illustrating a detailed configuration of a local server of FIG. 3.
FIG. 5 is a functional block diagram illustrating a detailed configuration of a central server of FIG. 1.
FIG.6 is a signal flowchart illustrating a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure.
FIG. 7 is a mechanism flowchart illustrating a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure.
FIG. 8 is a flowchart illustrating detailed steps of a method for providing a smart environment according to an embodiment of the present disclosure.
FIG. 9 is a reference diagram illustrating a detailed block and message flowchart in a method for providing a smart environment according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS The advantages, features and aspects of the present invention will become apparent from the following description of the embodiments with reference to the accompanying drawings, which is set forth hereinafter. The present invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting of example embodiments. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a functional block diagram illustrating a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to the present disclosure, and FIG. 2 is a conceptual diagram of a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to the present disclosure.
As shown in FIGS. 1 and 2, a system for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure includes a plurality of smart environment providing system 100 and a central server 200.
The smart environment providing system 100 collects state data including environmental information of a livestock house and driving information of driving equipment and provides the collected state data and driving information to the central server 200.
The smart environment providing system 100 receives list information on a predictive model for determining anomalies of the driving equipment from the central server 200, selects a predictive model matched to the collected state data from the predictive model list, and transfers the selected predictive model to the central server 200.
The central server 200 receives the state data collected by the smart environment providing system 100, accumulates and stores the received state data, and generate a predictive model based on the state data provided through each smart environment providing system 100.
The central server 200 stores and manages the generated predictive models in a storage list pool, and provides the stored predictive model list to the smart environment providing system 100.
In addition, when information on a prediction target predictive model is received from the smart environment providing system 100, the central server 200 determines anomalies of the driving equipment using the predictive model and provides a result to the corresponding smart environment providing system 100. Therefore, according to an embodiment of the present disclosure, by quickly and adaptively processing determination of anomalies of the driving equipment for each livestock house, productivity of various types of livestock farms in which various and many equipment of a livestock house is built may be improved. FIG. 3 is a functional block diagram illustrating the smart environment providing system of the livestock farm of FIG. 1. As shown in FIG. 3, the smart environment providing system 100 according to an embodiment of the present disclosure includes a plurality of sensors 110, a plurality of driving equipment 120, a data set database (DB) 130, and a local server
140. The sensor 110 may be implemented in various manners, such as temperature, humidity, CO», and ammonia sensors, and collects state data including environmental sensing information, which is sensing information collected from each sensor 110. The driving equipment 120 is equipment for forming an environment of a livestock house, and may be equipment such as an exhaust fan, a flow fan, a cooling pad, and a radiator, but is not limited thereto. Such driving equipment 120 is driven to form an environment of the livestock house and provides state data including driving information. The data set DB 130 stores state data provided from the sensors 110 and the driving equipment 120. The local server 140 stores the state data provided from the sensors 110 and the driving equipment 120 in the data set DB 130, and provides the state data stored in the data set DB 130 in the central server 200. In this case, the local server 140 may perform preprocessing for data standardization when the state data is stored in the data set DB 130. Also, the local server 140 detects whether the driving equipment 120 and the sensors 110 abnormally operate by applying the state data collected to the predictive model dynamically provided from the central server 200. Hereinafter, a detailed configuration of the local server 140 according to an embodiment of the present disclosure will be described with reference to FIG. 4. FIG. 4 is a functional block diagram illustrating a detailed configuration of the local server of FIG. 3.
As shown in FIG. 4, the local server 140 includes a data collecting unit 141, a data preprocessing unit 142, a data providing unit 143, a predictive model selecting unit 144, and a malfunction detecting unit 145.
The data collecting unit 141 collects state data from the sensor 110 and the driving equipment 120. The state data collected in this manner may be stored and managed in an arbitrary database in the form of LOW data.
The data preprocessing unit 120 preprocesses the collected state data and stores the preprocessed state data in the data set DB 130.
The data providing unit 143 provides the preprocessed state data stored in the data set DB 130 to the central server 200.
The predictive model selecting unit 144 receives a list of learned predictive models from the central server 200, selects a predictive model from the list of predictive models provided based on the collected state data, and provides the selected predictive model information to the central server 200.
According to a result of comparing state data through the selected predictive model in the central server 200, the malfunction detecting unit 145 detects a malfunction of the compared driving equipment 120.
That is, in an embodiment of the present disclosure, the local server 140 selects a predictive model to be applied from the predictive model list provided from the central server 200, and the central server 200 detects a malfunction using the selected predictive model transfers a result to the local server 140.
Hereinafter, a detailed configuration of the central server 200 according to an embodiment of the present disclosure will be described with reference to FIG. 5.
FIG. 5 is a functional block diagram illustrating a detailed configuration of the central server of FIG. 1.
As shown in FIG. 5, the central server 200 includes a data collecting unit 210, a predictive model generating unit 220, a learning model storage unit 230, a model list providing unit 240, and an anomaly determining unit 250.
The data collecting unit 210 collects state data of the sensor 110 and the driving equipment 120 for each livestock house from the smart environment providing system 100 of each livestock house.
The predictive model generating unit 220 generates a predictive model for detecting anomalies using the collected state data.
The learning model storage unit 230 stores and manages the generated learning model. The learning model storage unit 230 stores the learned predictive model and the managed predictive model list information. Here, the predictive model list preferably includes predictive model information and entity instances (state data) applied to the predictive model. The model list providing unit 240 dynamically distributes a list of stored predictive models to the corresponding smart environment providing system 100. When the predictive model selected from the smart environment providing system 100 is provided, the anomaly determining unit determines whether the driving equipment is abnormal using the selected predictive model, and provides a result to the smart environment providing system 100.
As such, the central server 200 may identify a predictive model of the driving equipment 120 by the instance which is an entity for the recognition of each equipment through each predictive model.
According to an embodiment of the present disclosure, the local server 140 of the livestock farm stores the model list of each equipment to an analysis client (308), and the central server 200 performs prediction (39) through data input from a current data client using a predictive model selected by the local server 140, determines correlation between data of an actual value and a predicted value (312), and then determines whether equipment malfunctions (310) according to a result, so that, even with equipment of any livestock environment from collecting equipment information to recognition of anomalies, a model may be dynamically distributed each time a leaning model is updated from collecting and learning information related to the equipment of livestock house to extraction of model, and prediction and result information may be provided.
FIG.6 is a signal flowchart illustrating a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure, and FIG. 7 is a mechanism flowchart illustrating a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure.
Hereinafter, a method for recognizing dynamic anomalies of multiple livestock equipment in a smart farm system according to an embodiment of the present disclosure will be described with reference to FIG. 6.
First, state data including environment of a livestock house and driving information of the driving equipment 120 is collected by the smart environment providing system 100 and provided to the central server 200 in operation S100.
Subsequently, the central server 200 receives the state data collected by each smart environment providing system 100, and accumulates and stores the received state data in operation S200. The central server 200 may continuously learn and update the generated predictive model using the state data collected from each smart environment providing system 100. Thereafter, the central server 200 generates a predictive model based on the state data provided through each smart environment providing system 100, and stores and manages the generated predictive models in a storage list pool in operation S300. Also, even when the predictive model is updated, the central sever 200 may dynamically provide a predictive model list including the corresponding predictive model to the smart environment providing system 100.
Subsequently, the central server 200 provides the stored predictive model list to the smart environment providing system 100 in operation S400.
Thereafter, when the list information on the predictive model is received from the central server 200, the smart environment providing system 100 selects a predictive model matched to the collected state data from the predictive model list and transfers the selected predictive model to the central server 200 in operation S500. That is, it is preferable that the predictive model of the driving equipment 120 is identified by an instance which is an entity included in the collected state information by the smart environment providing system 100.
Thereafter, when information on a prediction target predictive model is received from the smart environment providing system 100, the central server 200 determines anomalies of the driving equipment 120 using the predictive model and provides a result to the corresponding smart environment providing system 100 in operation S600.
FIG. 8 is a flowchart illustrating detailed steps of a method for providing a smart environment according to an embodiment of the present disclosure, and FIG. 9 is a reference diagram illustrating a detailed block and message flowchart in a method for providing a smart environment according to an embodiment of the present disclosure.
Hereinafter, detailed steps of the operation S100 of collecting state data performed by the smart environment providing system 100 of the present disclosure and providing the collected state data to the central server 200 will be described with reference to FIGS. 8 and 9.
State data including environment sensing information such as temperature, humidity, CO:, and ammonia information in operation S110.
Driving equipment is driven to form an environment of a livestock house, and state data including driving information is provided in operation S120.
Next, state data provided from the sensors 110 and the driving equipment 120 is stored in the data set DB 130 in operation S130. In addition, the collected state data may be preprocessed and stored.
Thereafter, the state data stored in the data set DB 130 is provided to the central server 200 in operation S140.
Meanwhile, in an embodiment of the present disclosure, the smart environment providing system 100 and the central server 200 may each be configured to include a communication module, a memory and a processor.
The communication module transmits or receives data in the smart environment providing system 100 including the sensor 110, the driving equipment 120, the database 130, and the local server 140 and transmits or /receives data to and from the central server 200.
Such a communication module may include both a wired communication module and a wireless communication module. The wired communication module may be implemented as a power line communication device, a telephone line communication device, a cable home (MoCA), Ethernet, IEEE1294, an integrated wired home network, and the RS-485 driving equipment 120. In addition, the wireless communication module may be implemented by wireless LAN (WLAN), Bluetooth, HDR WPAN, UWB, ZigBee, Impulse Radio, 60GHz WPAN, Binary- CDMA, wireless USB technology, wireless HDMI technology, and the like.
The memory of the smart environment providing system 100 stores a program for collecting and transmitting state data, a program for selecting and providing a predictive model based on state data from a list of predictive models, and the processor executes the program stored in the memory.
Also, the memory of the central server 200 stores a program for storing and managing state data provided by each smart environment providing system 100, a program for generating a predictive model through the state data, a program for storing and managing the generated predictive models in a pool, and a program for determining whether driving equipment is abnormal using a predictive model selected by the smart environment providing system 100 and providing a result to the smart environment providing system 100, and the processor executes the program stored in the memory.
In this case, the memory refers to a non-volatile storage unit device and a volatile storage unit device that continuously maintain stored information even when power is not supplied.
For example, memory may include a NAND flash memory such as a compact flash (CF) card, a secure digital (SD) card, a memory stick, a solid-state drive (SSD), and a micro SD card, a magnetic computer storage unit device such as a hard disk drive (HDD), and an optical disc drive such as a CD-ROM and a DVD-ROM.
For reference, the components according to an embodiment of the present disclosure may be implemented in the form of software or hardware such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), and may perform predetermined roles.
However, ‘components’ are not limited to software or hardware, and each component may be configured to reside in an addressable storage unit medium or to reproduce one or more processors.
Thus, the component may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
Components and functions provided in the components may be combined into a smaller number of components or further divided into additional components.
Here, it will be understood that each block of the flowchart diagrams and combinations of the flowchart diagrams may be performed by computer program instructions. These computer program instructions may be loaded on a processor of a general purpose computer, special purpose computer, or other programmable data processing equipment, so that the instructions performed by the processor of the computer or other programmable data processing equipment may creates a means to perform the functions described in the flowchart block{s). These computer program instructions may use a computer, which may be directed to a computer or other programmable data processing equipment to implement a function in a particular manner, or may be stored in a computer readable memory, so that it is also possible for the instructions which use the computer or which are stored in the computer-readable memory to produce an article of manufacture including instruction means for performing the function described in the flowchart block(s). The computer program instructions may also be loaded on a computer or other programmable data processing equipment, so that a series of operational steps may be performed on the computer or other programmable data processing equipment to create a computer-executed process and the instructions for performing the computer or other programmable data processing equipment provide steps for performing the functions described in the flowchart block(s).
Further, each block may represent a module, segment, or portion of a code that includes one or more executable instructions for executing specified logical function(s). It should also be noted that in some alternative implementations it is also possible for the functions recited in the blocks to occur out of order. For example, two blocks shown in succession may be actually performed substantially simultaneously or the blocks may be sometimes performed in a reverse order according to a corresponding function.
Here, the term '~ unit used in this embodiment refers to software or hardware components such as FPGA or ASIC, and '~ unit’ performs certain roles. However, ‘unit’ is not meant to be limited to software or hardware. '~ unit’ may be configured to reside on an addressable storage unit medium or may be configured to reproduce one or more processors. Thus, as an example, '~ unit’ includes components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, and procedures, subroutines, segments of a program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
The functions provided in the components and ‘~ unit’ may be combined into a smaller number of components or may further divided into additional components and ‘> units’. In addition, components and '~ units’ may be implemented to reproduce one or more CPUs in a device or secure multimedia card.
In the above, the configuration of the present invention has been described in detail with reference to the accompanying drawings, but this is only an example and variations and modifications may be made by those of skilled in the art to which the present invention pertains within the scope of the technical spirit of the present invention. Therefore, the scope of the present invention should not be limited to the embodiments described above and should be defined by the description of the following claims.

Claims (16)

ConclusiesConclusions 1. Een system voor het herkennen van dynamische afwijkingen van apparatuur voor vee in een slim boerderijsysteem, het systeem omvat: - een slim omgevingsverschatfend systeem die is ingericht voor het verzamelen van toestandsgegevens inclusief omgevingsinformatie van een veestal en aandrijfinformatie van aandrijfmiddelen, voor het verstrekken van verzamelde toestandsgegevens aan een centrale server, voor het selecteren, wanneer lijstinformatie over voorspellende modellen voor het bepalen van afwijkingen van de aandrijfmiddelen van de centrale server, van een voorspellend model uit de voorspellende modellen lijst gerelateerd aan verzamelde toestandsgegevens, en voor het overdragen van het geselecteerde voorspellende model naar de centrale server; en - een centrale server die is ingericht voor het ontvangen van door het slimme omgevingsverschaffende systeem verzamelde data voor het cumulatief opslaan van de ontvangen toestandsgegevens, voor het genereren van een voorspellend model gebaseerd op de toestandsgegevens die worden geleverd door elk slim omgevingsverschaffend systeem, voor het opslaan en beheren van de gegenereerde voorspellende modellen in een opslaglijstpool, voor het verstrekken van een opgeslagen lijst met voorspellende modellen voor het slimme omgevingsverschaffende systeem, voor het bepalen, afhankelijk van informatie over een voorspellingsdoel voorspellingsmodel van het slimme omgevingsverschaffende systeem, van afwijkingen van de aandrijfmiddelen gebruikmakend van het corresponderende voorspellende model, en voor het verstreken van een resultaat aan het corresponderende slimme omgevingsverschaffende systeem.1. A system for recognizing dynamic deviations of livestock equipment in a smart farm system, the system comprises: - a smart environment estimating system arranged to collect condition data including environment information of a livestock shed and drive information of drive means, for providing collected state data to a central server, for selecting when list information on predictive models for determining deviations of the drive means of the central server, a predictive model from the predictive models list related to collected state data, and for transmitting the selected predictive model to the central server; and - a central server arranged to receive data collected by the smart environment providing system for cumulatively storing the received state data, for generating a predictive model based on the state data provided by each smart environment providing system, for the storing and managing the generated predictive models in a storage list pool, for providing a stored list of predictive models for the smart environment providing system, for determining, depending on information about a prediction target prediction model of the smart environment providing system, deviations of the drive means using the corresponding predictive model, and for passing a result to the corresponding smart environment providing system. 2. Systeem volgens conclusie 1, waarin het slimme omgevingsverschaffende systeem omvat: - een aantal sensoren die ingericht zijn voor het verzamelen van toestandsgegevens inclusief omgevingswaarnemingsinformatie waaronder ten minste informatie van één gekozen uit: temperatuur, vochtigheid, Co2, en ammoniak;The system of claim 1, wherein the smart environment providing system comprises: - a plurality of sensors adapted to collect condition data including environmental sensing information including at least information of one selected from: temperature, humidity, CO2, and ammonia; - aandrijffmiddelen ingericht voor het vormen van een omgeving van de veestal en voor het voorzien van toestandsgegevens inclusief corresponderende aandrijfinformatie; en - een lokale server ingericht voor het opslaan van de sensoren en de aandrijfmiddelen verkregen toestandsgegevens in een datasetdatabase en om de toestandsgegevens die zijn opgeslagen in de datasetdatabase te leveren aan de centrale server.- drive means arranged for forming an environment of the cattle shed and for providing status data including corresponding drive information; and - a local server arranged to store the sensors and the drive means obtained state data in a data set database and to supply the state data stored in the data set database to the central server. 3. Systeem volgens conclusie 2, waarin de lokale server is ingericht voor het dynamisch ontvangen van een lijst met voorspellende modellen van de centrale server, en de lijst met voorspellende modellen omvat toestandsgegevensinstantie- informatie van de aandrijfmiddelen en de sensoren die zijn toegepast op het voorspellende model.The system of claim 2, wherein the local server is configured to dynamically receive a predictive model list from the central server, and the predictive model list includes state data instance information from the drive means and the sensors applied to the predictive model. fashion model. 4. Systeem volgens conclusie 2, waarbij de lokale server omvat: - een gegevensverzameleenheid ingericht voor het verzamelen van toestandsgegevens en voor het opslaan van verzamelde toestandsgegevens in een database; - een gegevensvoorbehandelingseenheid ingericht voor het voorbehandelen van de verzamelde toestandsgegevens en voor het opslaan van de voorbehandelde toestandsgegevens in een verfijningsdatabase; - een gegevensverschaffende eenheid die is ingericht om de vertijnde toestandsgegevens die zijn opgeslagen in de verfijningsdatabase te leveren aan de centrale server; - een voorspellende modelselecteereenheid ingericht voor het ontvangen van een lijst met geleerde voorspellende modellen van de centrale server, voor het selecteren van een voorspellend model uit de lijst van de verstrekte voorspellende modellen gebaseerd op de verzamelde toestandsgegevens, en voor het verstrekken van de geselecteerde voorspellende modelinformatie aan de centrale server te verstrekken; en - een storingdetectie-eenheid ingericht voor het detecteren van een vergeleken storing van de aandrijfmiddelen volgens een toestandsgegevensvergelijkingsresultaat door het geselecteerde voorspellende model in de centrale server.System according to claim 2, wherein the local server comprises: - a data collection unit arranged for collecting state data and for storing collected state data in a database; - a data pre-processing unit arranged to pre-process the collected state data and to store the pre-processed state data in a refinement database; - a data providing unit arranged to supply the refined state data stored in the refinement database to the central server; - a predictive model selector configured to receive a list of learned predictive models from the central server, select a predictive model from the list of provided predictive models based on the collected state data, and provide the selected predictive model information to provide to the central server; and - a malfunction detecting unit arranged to detect a compared malfunction of the drive means according to a state data comparison result by the selected predictive model in the central server. 5. Systeem volgens conclusie 3, waarbij de lokale server is ingericht voor het identificeren van het voorspellende model van de aandrijfmiddelen door een instantie, welke een entiteit is voor het herkennen van elk middel.The system of claim 3, wherein the local server is arranged to identify the predictive model of the drive resources by an instance, which is an entity for recognizing each resource. 6. Systeem volgens conclusie 1, waarbij de centrale server omvat: - een gegevensverzameleenheid ingericht voor het verzamelen van toestandsgegevens van de sensor en de aandrijfmiddelen van elke veestal van het systeem die een slimme omgeving biedt van elke veestal; - een voorspellendmodelgenereereenheid ingericht voor het genereren van een voorspellend model voor het detecteren van afwijkingen gebruik makend van de verzamelde toestandsgegevens; en - een modellenlijstgenereereenheid ingericht voor het dynamisch distribueren van het gegenereerde voorspellende model naar het slimme omgevingsverschaffende systeem van een overeenkomstige veestal.System according to claim 1, wherein the central server comprises: - a data collection unit arranged for collecting state data of the sensor and the drive means of each livestock shed of the system providing a smart environment of each livestock shed; - a predictive model generating unit configured to generate a predictive model for detecting anomalies using the collected condition data; and - a model list generating unit adapted to dynamically distribute the generated predictive model to the smart environment providing system of a corresponding livestock shed. 7. Systeem volgens conclusie 6, waarbij de centrale server is ingericht voor het opslaan en beheren van een gegenereerd trainingsmodel in een trainingsmodel pool.System according to claim 6, wherein the central server is arranged to store and manage a generated training model in a training model pool. 8. Systeem volgens conclusie 7, waarbij de centrale server is ingericht voor het uitvoeren van een voorspellend model gebaseerd op toestandsgegevens overeenkomend met de geïdentificeerde instantie, en voor het bepalen van afwijkingen van elk middel door een overeenkomstig voorspellende resultaatwaarde.The system of claim 7, wherein the central server is configured to perform a predictive model based on state data corresponding to the identified instance, and to determine deviations of each resource by a corresponding predictive result value. 9. Een werkwijze voor het herkennen van dynamische afwijkingen van meerdere veestalmiddelen in een slim boerderijsysteem, de werkwijze omvat: - het verzamelen, door een slim omgevingsverschaffend systeem, omgevingsdata inclusief omgevingsinformatie van een veestal en aandrijfinformatie van aandrijfmiddelen en het voorzien van verzamelde toestandsgegevens aan een centrale server; - het ontvangen, door de centrale server, van door het slimme omgevingsverschaffende syteem verzamelde toestandsgegevens, en verzamelen en opslaan van de ontvangen toestandsgegevens.A method for recognizing dynamic deviations of a plurality of livestock resources in a smart farm system, the method comprises: collecting, by a smart environment providing system, environmental data including environment information of a livestock shed and drive information of drive means and providing collected condition data to a central server; - receiving, by the central server, state data collected by the smart environment providing system, and collecting and storing the received state data. - het genereren, door de centrale server, van een voorspellend model gebaseerd op de door elk slim omgevingsverschaffende systeem verschafte toestandsgegevens, en opslaan en beheren van de gegenereerde voorspellende modellen in een opslaglijstpoot; - het voorzien, door de centrale server, van de opgeslagen voorspellende modellen lijst aan het slimme omgevingsverschaffende systeem; - het door het slimme omgevingsverschaffende systeem selecteren van een voorspellend model gerelateerd aan de verzamelde toestandsgegevens van de voorspellende modellijst, wanneer lijstinformatie over een voorspellend model voor het bepalen van afwijkingen van aandrijfmiddelen is ontvangen van de centrale server, en het overdragen van het geselecteerde voorspellende model naar de centrale server; en - het door de centrale server bepalen van afwijkingen van de aandrijfmiddelen gebruikmakend van het corresponderende voorspellende model en het voorzien vaneen resultaat aan het corresponderende slimme omgevingsverschaffende systeem, wanneer informatie van voorspellend model voor een voorspellingsdoel is ontvangen van een slim omgevingsverschaffend systeem.- generating, by the central server, a predictive model based on the state data provided by each smart environment providing system, and storing and managing the generated predictive models in a storage list leg; - providing, by the central server, the stored predictive model list to the smart environment providing system; - selecting a predictive model related to the collected state data from the predictive model list by the smart environment providing system, when list information on a predictive model for determining drive means deviations is received from the central server, and transmitting the selected predictive model to the central server; and - determining by the central server deviations of the driving means using the corresponding predictive model and providing a result to the corresponding smart environment providing system when information of predictive model for a prediction purpose is received from a smart environment providing system. 10. Werkwijze volgens conclusie 9, waarbij, het verzamelen, door een slim omgevingsverschaffend systeem, omgevingsdata en het voorzien van verzamelde toestandsgegevens aan een centrale server omvat; - het verzamelen van toestandsgegevens inclusief één of meer omgevingsdata gekozen uit: temperatuur, vochtigheid, CO en ammoniak; - het vormen van een omgeving voor een veestal en het verschaffen van toestandsgegevens inclusief de informatie over het vormen van de omgeving; - het opslaan van door sensoren en aandrijfmiddelen geleverde toestandsgegevens in een dataset database; en - het verstrekken van de toestandsgegevens die zijn opgeslagen in de dataset database aan de centrale server.The method of claim 9, wherein collecting, by a smart environment providing system, comprises environment data and providing collected state data to a central server; - collecting state data including one or more environmental data selected from: temperature, humidity, CO and ammonia; - creating an environment for a livestock shed and providing condition data including the information about shaping the environment; - storing state data supplied by sensors and drive means in a data set database; and - providing the state data stored in the dataset database to the central server. 11. Werkwijze volgens conclusie 9, waarbij het selecteren van een voorspellend model gerelateerd aan de verzamelde toestandsgegevens en het overdragen van het geselecteerde voorspellende model naar de centrale server, de voorspellende modellijst van de centrale server omvat:The method of claim 9, wherein selecting a predictive model related to the collected state data and transferring the selected predictive model to the central server, the central server's predictive model list comprises: - Instantie-informatie die is gerelateerd aan de toestandsgegevens die moeten worden toegepast op elk voorspellend model, en een voorspellend model om toe te passen wordt geselecteerd door het vergelijken van verzamelde toestandsgegevens met een instantie van het voorspellende model door het slimme omgevingsverschaffende systeem.Instance information related to the state data to be applied to each predictive model, and a predictive model to apply is selected by comparing collected state data with an instance of the predictive model by the smart environment providing system. 12. Werkwijze volgens conclusie 10, waarbij, bij het verstrekken van de toestandsgegevens die zijn opgeslagen in de dataset database aan de centrale server door het slimme omgevingsverschaffende systeem, de verzamelde toestandsgegevens voorbehandeld zijn en de voorbehandelde toestandsgegevens zijn opgeslagen in een verfijningsdatabase.The method of claim 10, wherein, upon providing the state data stored in the dataset database to the central server by the smart environment providing system, the collected state data is pre-processed and the pre-processed state data is stored in a refinement database. 13. Werkwijze volgens conclusie 9, waarbij het verzamelen en opslaan van de ontvangen toestandsgegevens door de centrale server omvat: - het verzamelen van toestandsgegevens van een sensor en aandrijfmiddelen van een slim omgevingsverschaffend systeem van elke veestal; - het genereren van een voorspellend model voor het detecteren van afwijkingen gebruik makend van de verzamelde toestandsdata; en - het opslaan van het gegenereerde model in een pool en het dynamisch distribueren van een voorspellende modellijst die is opgeslagen in de pool aan het slimme omgevingsverschaffende systeem.A method according to claim 9, wherein the collecting and storing of the received state data by the central server comprises: - collecting state data from a sensor and drive means of a smart environment providing system of each livestock shed; - generating a predictive model for detecting abnormalities using the collected condition data; and - storing the generated model in a pool and dynamically distributing a predictive model list stored in the pool to the smart environment providing system. 14. Werkwijze volgens conclusie 13, waarin het gegenereerde voorspellende model is opgeslagen en beheerd in een trainingsmodelpool door de centrale server.The method of claim 13, wherein the generated predictive model is stored and managed in a training model pool by the central server. 15. Werkwijze volgens conclusie 14, waarbij, wanneer toestandsgegevens zijn ontvangen van het slimme omgevingsverschaffende systeem, gebaseerd op de toestandsgegevens als een instantie wordt bepaald of er een voorspellend model is opgeslagen in de trainingsmodelpool; en wanneer er een voorspellend model is opgeslagen in de trainingsmodelpool het voorspellende model wordt getraind door de toestandsgegevens.The method of claim 14, wherein, when state data is received from the smart environment providing system, it is determined as an instance based on the state data whether a predictive model is stored in the training model pool; and when a predictive model is stored in the training model pool, the predictive model is trained by the state data. 16. Werkwijze volgens conclusie 15, verder omvattende:The method of claim 15, further comprising: - wanneer het in de modeltrainingspool opgeslagen voorspellende model is getraind, het door de centrale server dynamisch verstrekken van een in de trainingsmodelpool opgeslagen voorspellende modellijst aan elk slim omgevingsverschaffend systeem.- when the predictive model stored in the model training pool has been trained, dynamically providing a predictive model list stored in the training model pool by the central server to each smart environment providing system.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160120144A1 (en) * 2014-10-30 2016-05-05 Electronics And Telecommunications Research Institute Livestock house management system and management method thereof
US20190187681A1 (en) * 2016-05-09 2019-06-20 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection in downstream oil and gas environment with haptic feedback and continuously monitored alarm

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101815531B1 (en) * 2016-02-15 2018-01-31 계명대학교 산학협력단 Smart greenhouse control system
KR102174466B1 (en) * 2016-11-28 2020-11-04 한국전자통신연구원 Method and apparatus for diagnosing error of operating equipment in smart farm
KR20180105451A (en) * 2017-03-15 2018-09-28 한국전자통신연구원 Apparatus for controlling livestock breeding environment and method for the same
KR102091126B1 (en) * 2018-10-24 2020-04-23 전자부품연구원 EDGE-CLOUD COLLABORATION SYSTEM FOR ANALYZING IoT DATA AND OPERATION METHOD THEREOF

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160120144A1 (en) * 2014-10-30 2016-05-05 Electronics And Telecommunications Research Institute Livestock house management system and management method thereof
US20190187681A1 (en) * 2016-05-09 2019-06-20 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection in downstream oil and gas environment with haptic feedback and continuously monitored alarm

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