KR102174466B1 - Method and apparatus for diagnosing error of operating equipment in smart farm - Google Patents

Abstract

Disclosed is a method and apparatus for diagnosing errors in smart farm operating equipment. The error diagnosis method performed by the error diagnosis device of the smart farm operating equipment includes receiving a control message that triggers error diagnosis, and when the control message is received, the data collected from the smart farm is diagnosed with a preset error. And determining an error of the operating equipment installed in the smart farm by analyzing based on rules, and providing the determination result to a user through a user interface. Therefore, errors in operating equipment can be immediately identified.

Description

Error diagnosis method and device of smart farm operating equipment {METHOD AND APPARATUS FOR DIAGNOSING ERROR OF OPERATING EQUIPMENT IN SMART FARM}

The present invention relates to a method and apparatus for diagnosing errors of a smart farm operating equipment, and more particularly, by quickly and accurately detecting errors such as controllers and sensors operated in a smart farm using collectible control data, It relates to how to improve operational efficiency.

Recently, interest in smart farms, an intelligent farm created by combining information and communication technology (ICT) with agricultural technology, is increasing.

Smart Farm aims to improve agricultural productivity and provide convenience for labor reduction through various monitoring and control such as growth of crops and diagnosis through environmental monitoring inside and outside the greenhouse using an automated facility and horticultural facility (greenhouse). Because of this goal, smart farms use agricultural ICT-related sensors (temperature, humidity, CO2, solar radiation, wind direction, wind speed, rainfall, light quantity, soil moisture, soil tension, soil EC, soil pH, geothermal temperature, etc.), controllers, etc. (Ceilings, side cabinets, insulation, curtains, ventilators, flow fans, irrigation motors, irrigation valves, air conditioners, etc.), communication equipment, etc. are in use.

However, many of these information and communication-related equipment are highly faulty due to high temperature and humidity inside the facility horticultural facility (greenhouse), poor exposure to the outside, and a lack of stable power supply. It is difficult to judge, and there is no way to know the presence or absence of operation other than visual confirmation through a camera during remote control, which is an obstacle to the spread of smart farms.

Therefore, there is a need for a method of accurately and quickly detecting errors of devices installed in a smart farm to identify problems and correct errors.

KR 10-2016-0118672 A

An object of the present invention for solving the above problems is to provide an error diagnosis method performed by an error diagnosis device of a smart farm operating equipment.

Another object of the present invention for solving the above problems is to provide an error diagnosis apparatus for a smart farm operating equipment.

An aspect of the present invention for achieving the above object provides an error diagnosis method performed by an error diagnosis apparatus of a smart farm operating equipment.

Here, the error diagnosis method performed by the error diagnosis device of the smart farm operating equipment includes receiving a control message that triggers error diagnosis, and when the control message is received, the data collected from the smart farm is preset error. Analyzing based on a diagnosis rule, outputting an error determination result of the operating equipment installed in the smart farm according to the analysis result, and providing the error determination result to a user through a user interface. I can.

Here, the control message may be a message requesting an error diagnosis of an external user terminal or a palm cloud, or may be a message periodically generated in the error diagnosis apparatus.

Here, the collected data may include at least one of operation data of the smart farm, control data of a controller among the operation equipment, and measurement data of a sensor among the operation equipment.

Here, the providing of the error determination result to the user may include transmitting the error determination result to a push server interworking with the user terminal of the user.

Here, the analyzing step is performed by referring to the state information of the operating equipment included in the collected data, and when the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor. , Determining that there is an error in the first sensor.

Here, the error determination result may include an error regarding the installation location of the first sensor.

In the analyzing, the first controller is identified as being driven by referring to the state information of the first controller included in the collected data, and environmental information controlled by the first controller using a second sensor. If the change amount of the measured data measured is less than the threshold value or the change direction is different from the state information of the first controller, determining that there is an error in the first controller or the second sensor.

Here, in the analyzing, the first measurement data among measurement data of the same type of sensors included in the collected data has a difference of more than a threshold value compared to other measurement data, or the change trend of the first measurement data is different from the other measurement data. If different from the measurement data, determining that there is an error in a sensor corresponding to the first measurement data may be included.

Here, the analyzing step may determine an error of the operating equipment based on a growth step based on a timeline of the crop.

Another aspect of the present invention for achieving the above object is to provide an error diagnostic apparatus for a smart farm operating equipment.

Here, the error diagnosis apparatus of the smart farm operating equipment may include at least one processor and a memory storing instructions instructing the at least one processor to perform at least one step. have.

Here, the at least one step may include receiving a control message for triggering error diagnosis, and when the control message is received, analyzing the data collected from the smart farm based on a preset error diagnosis rule, analysis According to the result, outputting the error determination result of the operating equipment installed in the smart farm and providing the error determination result to the user through a user interface.

Here, the control message may be a message requesting an error diagnosis of an external user terminal or a palm cloud, or may be a message periodically generated in the error diagnosis apparatus.

Here, the collected data may include at least one of operation data of the smart farm, control data of a controller among the operation equipment, and measurement data of a sensor among the operation equipment.

Here, the analyzing may include converting the collected data into semantic web-based semantic data.

Here, the providing of the error determination result to the user may include transmitting the error determination result to a push server interworking with the user terminal of the user.

Here, the analyzing step is performed by referring to the state information of the operating equipment included in the collected data, and when the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor. , Determining that there is an error in the first sensor.

Here, the error determination result may include an error regarding the installation location of the first sensor.

Another aspect of the present invention for achieving the above object is to provide an error diagnosis method performed by an error diagnosis device of a smart farm operating equipment.

Here, the error diagnosis method includes receiving data collected from a smart farm, generating a data table for error diagnosis using the received data, and analyzing the data table based on a preset error diagnosis rule. And outputting an error determination result of the operating equipment installed in the smart farm according to the analysis result.

Here, the data table may include an operating equipment metadata table generated by using state information of the operating equipment included in the collected data.

The outputting of the error determination result may further include generating an error detection event table including at least one of occurrence time, type, content, and occurrence area of the determined error.

Here, the error diagnosis method may further include periodically referring to the error detection event table and transmitting error information according to the error detection event table to a push server interworking with the user terminal.

In the case of using the method and apparatus for diagnosing errors of the smart farm operating equipment according to the present invention as described above, errors in the operating equipment can be immediately identified, thereby preventing control of equipment based on incorrect data in advance.

In addition, there is an advantage in that smart farm operation can be stably performed and efficient management is possible.

In addition, since ontology-based error diagnosis is performed, accurate error diagnosis is possible according to various situations.

1 is a conceptual diagram illustrating an environment in which a method for diagnosing an error of a smart farm operating device according to an embodiment of the present invention is performed.
2 is a block diagram showing a functional module for an error diagnosis apparatus of an operating equipment in a smart farm according to an embodiment of the present invention.
3 is a data relationship diagram for explaining an example of implementing an error diagnosis apparatus of an operating device in a smart farm according to an embodiment of the present invention.
4 is an exemplary diagram illustrating a rule for diagnosing errors of operating equipment in a smart farm according to an embodiment of the present invention.
5 to 10 are data tables for diagnosing errors of operating equipment in a smart farm according to an embodiment of the present invention.
11 is a flowchart of a method for diagnosing errors of a smart farm operating equipment according to the first embodiment of the present invention.
12 is a block diagram of an apparatus for diagnosing errors of operating equipment in a smart farm according to an embodiment of the present invention.
13 is a flowchart of a method for diagnosing errors of a smart farm operating equipment according to a second embodiment of the present invention.

In the present invention, various modifications may be made and various embodiments may be provided, and specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to a specific embodiment, it is to be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the present invention. In describing each drawing, similar reference numerals have been used for similar elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. These terms are used only for the purpose of distinguishing one component from another component. For example, without departing from the scope of the present invention, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present application, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram illustrating an entire system in which an error diagnosis method of a smart farm operating equipment is performed according to an embodiment of the present invention.

Referring to Figure 1, the entire system in which the error diagnosis method of the smart farm operating equipment is performed is an error diagnosis device 100, a smart farm 200, a farm cloud 300, and/or a user terminal 400 of a smart farm operator. It can be composed of.

The smart farm 200 is a greenhouse in which various sensors and controllers are installed to grow crops, and various smart farm operating equipment 210 may be installed.

For example, the smart farm operating equipment 210, an external weather sensor that measures the environment (temperature, humidity, wind direction, wind speed, insolation, CO _2, etc.) outside the greenhouse, the growth environment at a location adjacent to the crop in the greenhouse (medium temperature , Moisture content, load cell, etc.), an environmental sensor that measures the environment (temperature, humidity, CO ₂ , insolation, etc.) inside the greenhouse, and the nutrient solution and drainage state (flow rate, EC, PH, etc.) It may include a nutrient solution/drainage sensor, a controller that controls the environment inside and outside the greenhouse (cooling and heating, ventilation windows, ventilation control, etc.), a power supply, and the like.

The error diagnosis device 100 is a device that monitors the status of operating equipment installed in the smart farm 101 in real time and detects errors. Although the error diagnosis device 100 is shown as a separate device in the drawing, it may be included in the palm cloud 300 and implemented.

The farm cloud 300 is a cloud server that provides cloud-based software and services for farms operating a smart farm, and can relay between the error diagnosis device 100 and the smart farm 200, and the error diagnosis device ( The software in which the function module according to 100) is implemented may be installed.

The user terminal 400 is a device that provides information to a smart farm operator in connection with the error diagnosis device 100 and receives a control command, and may be a mobile terminal, and software in which such a function module is implemented may be mounted. .

In the operation of the entire system in which the method for diagnosing errors of the smart farm operating equipment according to an embodiment of the present invention is performed, first, the palm cloud 300 transmits data of the operating equipment (sensors and controllers, etc.) installed in the smart farm 200. Upon receipt, the palm cloud 300 may transmit the received data to the error diagnosis apparatus 100. In this case, the palm cloud 300 may request the error diagnosis device 100 to inquire the error diagnosis result, and may edit (inquiry, register, change or delete) an error diagnosis rule built in the error diagnosis device.

The error diagnosis apparatus 100 may transmit the error diagnosis result to the palm cloud 300 upon receiving an inquiry request for the error diagnosis result from the palm cloud 300.

In this case, the user terminal 400 of the smart farm operator may also request an inquiry about the error diagnosis result from the error diagnosis device 100 and may receive the error diagnosis result from the error diagnosis device 100.

2 is a block diagram showing a functional module for an error diagnosis apparatus of an operating equipment in a smart farm according to an embodiment of the present invention.

Referring to FIG. 2, the error diagnosis apparatus 100 according to an embodiment of the present invention includes an error rule engine 101 that determines whether or not there is an error in the smart farm operating equipment, and various events occurring before and after the determination of the error. A knowledge-based error diagnosis scheduler 102 that manages and operates a semantic converter 105 that converts various externally collected data (sensing data, controller data, operation data, etc.) into semantic web-based data, and a semantic converter It may include a malfunction diagnosis database 103 that stores semantic data converted by 105 and/or several interfaces 104, 106, 107, 108, 109 for providing a service to an external device.

First, the data collector 104 may collect various data (environment data inside and outside the greenhouse, control data, operation data, etc.) from the smart farm 200 or the palm cloud 300 in FIG. 1. That is, data may be directly collected through the smart farm 200, or data may be collected from the farm cloud 300 in which the data collected by the smart farm 200 is stored.

The semantic converter 105 converts the data collected by the data collector 104 into semantic web-based data by performing pre-processing (filtering, data normalization/conversion, etc.), and converts the converted data into the error diagnosis database 103. Can be saved. At this time, the data stored in the error diagnosis database 103 may be periodically collected through the data collector 104 at each collection period determined according to a user setting, and preprocessed and stored through the semantic converter 105. As a plurality of data is accumulated in the error diagnosis database 103 according to periodic collection, errors can be more accurately diagnosed.

The error rule engine 101 may perform error diagnosis using a knowledge base capable of rule-based reasoning. At this time, by using the data converted into a semantic form according to the semantic converter 105, an error can be automatically inferred.

The error rule engine 101 is a data model unit that defines a data model for sensor and control states for error diagnosis, a schema model unit that is a logical structure of data, and an ontology that defines a relationship to data inside the schema model. , A diagnosis rule defining a rule for diagnosing an error, and a rule reasoning unit for diagnosing an error in the collected data model based on an inference model set by the diagnosis rule.

The knowledge-based error diagnosis scheduler 102 may query a result of diagnosis of a malfunction inferred through periodic queries.

The error trigger rule manager 106 receives a request to edit an error diagnosis rule from an external user terminal or palm cloud, and collects the data collected through the data collector 104, the state change according to the control, The diagnosis rule of the error rule engine 101 may be input or changed in consideration of a change in the application rule or the like.

The error diagnosis service provided by the error diagnosis device 100 may provide a diagnosis result at the request of a user, and may provide a diagnosis result in the form of a push or an alarm through periodic monitoring of an error. I can.

First, the error diagnosis inquiry manager 107 may support a service according to a user request. Specifically, the error diagnosis inquiry manager 107 may receive an error diagnosis request from the user terminal or the palm cloud, and transmit the error diagnosis result and/or the current state back to the user terminal or the palm cloud.

Next, the error diagnosis mobile manager 109 may support a diagnosis result providing service through periodic monitoring. Specifically, the error diagnosis mobile manager 109 may periodically monitor the error diagnosis database 103 to check an error diagnosis result. As a result of the verification, if there is an error in the smart farm operating equipment, the error diagnosis mobile manager 109 may transmit the error diagnosis result to an external user terminal through a push server or may transmit the error diagnosis result to the palm cloud.

The user who operates the smart farm can take quick action on the operating equipment installed in the smart farm by checking the error diagnosis result through the error diagnosis mobile manager 109 or the error diagnosis inquiry manager 107 as described above.

3 is a data relationship diagram for explaining an example of implementing an error diagnosis apparatus of a smart farm operating equipment according to an embodiment of the present invention.

Referring to FIG. 3, an error diagnosis device (MalFunction_Engine, 201) of a smart farm operating equipment may include a SmartBed 202, a SmartFarm 203, and a SmartFarmAir 204 as resources.

The SmartBed 202 is a set of sensors and controllers centered on the root zone installed inside the greenhouse, and the SmartFarm 203 is a greenhouse of a farm and can be identified by a greenhouse ID. In addition, SmartFarmAir 204 may be environmental information inside and outside the greenhouse. SmartFarmStatus (203-1) can be composed of greenhouse operating hours (accumulated time used for cultivation) and status (operation or not).

In addition, the error diagnosis device (MalFunction_Engine, 201) includes a device (Device, 205) installed in the greenhouse, and each device is a subclass of a sensor node (206) and a controller (ActuatorNode, 207) and the cumulative operating time ( The lifetime of the sensor and controller, Survival Range, 208), and a valid range having a maximum value and a minimum value for each sensor and controller may be included (Valid Range, 209).

The sensor node 206 may include a plurality of collection cycles (hasSensingInterval), a sensor type (hasSensorType) that is a sensor type, a sensing data type (hasDataType) for each sensor type, and a measurement value (hasSensingValue) of each sensor. .

The controller (ActuatorNode, 207) is composed of a controller status collection period (hasActuatorInterval), a controller type control type (hasActuatorType), a control command for each controller type, and a controller action (hasAction) indicating the controller status. ) And controller operation time (hasActionTime).

The error diagnosis apparatus according to FIG. 3 should be understood as an example of implementing the error diagnosis apparatus by a person skilled in the art belonging to the present invention, and should not be construed as limiting the components of the present invention.

4 is an exemplary diagram illustrating an error diagnosis rule of a smart farm operating equipment according to an embodiment of the present invention.

Referring to FIG. 4, the error diagnosis rules of the smart farm operating equipment may be classified into, for example, error diagnosis of a single device, error diagnosis based on a relationship between a plurality of devices, and error diagnosis based on time or environment.

An example of fault diagnosis rules for a single device is as follows.

First, since each sensor has an effective measurement range (refer to the identification symbol 209 in FIG. 3), if a value exceeding the effective measurement range is collected as measurement data, it can be diagnosed as having an error. In this case, as a result of error diagnosis, a result of'exceeding the valid range' may be provided to the user.

In addition, when the installation location information is collected as the sensor status information, and it is determined that the collected installation location information is inappropriate, it may be diagnosed as an error of the corresponding sensor. Specifically, when a temperature sensor is located near a controller that controls temperature such as a blower or a boiler, since the temperature measured by the corresponding temperature sensor is unreliable, it can be diagnosed that there is an error in the temperature sensor. Therefore, in other words, if it is determined that the installation location is adjacent to the controller that controls the environment measured by the sensor, referring to the installation location of the operating equipment belonging to the sensor, it can be diagnosed as an error of the operating equipment. In this case, as a result of error diagnosis, a result of'installation location error' may be provided to the user.

In addition, when the measurement data (hasSensingValue in FIG. 3) value of the sensor is not collected or the measurement date and time (hasSensingTime in FIG. 3) is not updated, the sensor may be diagnosed as having an error. In this case, as a result of error diagnosis, a result of'communication failure or device failure' may be provided to the user.

In addition, since operating equipment such as sensors or controllers has a lifespan (refer to identification symbol 208 in Fig. 3), check the status information of the operating equipment and if the usage time exceeds the lifespan, the operating equipment is considered to have an error. Can be diagnosed. In this case, as a result of error diagnosis, a result of'over of life' may be provided to the user.

An error diagnosis rule based on a relationship between a plurality of devices may be classified according to whether or not the plurality of devices are the same.

An example of an error diagnosis rule based on the relationship between heterogeneous equipment is as follows.

First, if there is a correlation between the first measurement data and the second measurement data, and the change (specifically, the amount of change or the change direction) of the first measurement data and the second measurement data is different, the sensor for measuring the first measurement data and At least one of the sensors measuring the second measurement data may be diagnosed as having an error. Specifically, for example, since it is common for temperature and humidity to change in conjunction with each other, if there is a change in the temperature data measured by the temperature sensor, but there is no change in the humidity data measured by the humidity sensor, the humidity sensor will fail. It can be diagnosed as having. At this time, as a result of error diagnosis, a result of'sensor failure' may be provided to the user.

In addition, when it is confirmed that the controller is running when referring to the state information of the controller, if the amount of change in the measured data measured by the environmental information controlled by the controller using a sensor is small or the change direction is different, the controller or It can be diagnosed as a sensor error. In this case, as a result of error diagnosis, a result of'a failure of a corresponding controller or sensor' may be provided to the user.

Specifically, for example, if the temperature data measured by the temperature sensor decreases or does not change (or the amount of change is insignificant) while the boiler, which is one of the controllers, is running, it means that there is an error in the temperature sensor or the boiler. Can be diagnosed. In addition, when the measurement motor is controlled to open the measurement (smart farm window), and the temperature data measured by the temperature sensor does not change (or the amount of change is insignificant), there is an error in the temperature sensor or the measurement motor. Can be diagnosed.

In addition, as heterogeneous equipment, errors can be diagnosed based on a relationship between the power supply device and the operating equipment supplied with power by the power supply device. For example, if there is no change in voltage or current for the controller even though the controller is driven, it can be diagnosed that there is an error in the controller or the power supply. In addition, if the voltage and current supplied from the power supply exceed the allowable range (including upper and lower) compared to the commercial voltage and current of the power supply controller, sensor, and communication devices, such operating equipment is likely to malfunction. Therefore, it can be diagnosed that there is an error in the operating equipment.

An example of an error diagnosis rule based on the relationship between the same equipment is as follows.

First, if the difference between the measured data measured by the same kind of sensors of the smart farm is greater than or equal to the threshold value, or the change trend (or direction) of the measured data is different, it may be diagnosed that at least one of the corresponding sensors has an error. . For example, if there is a temperature sensor having temperature data that differs by more than a threshold value among temperature data measured by a plurality of temperature sensors, it may be diagnosed as having an error in the corresponding temperature sensor. In addition, even though the temperature data measured by the plurality of temperature sensors are continuously rising at a certain time interval, if any one of the temperature data is decreasing (if the change trend is different), the temperature corresponding to the decreasing temperature data It can be diagnosed as having an error in the sensor. In this case, as a result of error diagnosis, a result of'at least one sensor failure among the plurality of sensors' may be provided to the user.

Meanwhile, error diagnosis based on time or environment may correspond to a rule for diagnosing errors in operating equipment based on a growth stage based on a crop timeline. For example, according to the growth stage according to the timeline of crops managed by the smart farm, even though the temperature must be high on winter nights, when the boiler, which is the controller, is not running, when the air conditioner is running, or the measurement is performed by the measurement motor. If it is controlled in the open state, it can be diagnosed as a controller error.

5 to 10 are data tables for diagnosing errors of operating equipment in a smart farm according to an embodiment of the present invention.

Specifically, FIG. 5 is an error detection event table generated using the error analysis result of the operating equipment. 6 is a table of smart farm environment data generated using data obtained by observing the environment of a smart farm. 7 is a table of operating equipment metadata created using status information on operating equipment of a smart farm. 8 is a smart farm operation information table generated by using the operation status information of the smart farm. 9 is a data table for managing an error diagnosis rule. 10 is an ID management table of a push server providing error diagnosis results as a push service.

Referring to FIG. 5, the error detection event table is a data table containing the result of error analysis, and the error occurrence time, the error event identifier, the identifier of the farm where the error occurred, the area identifier, the facility building identifier, the smart bed identifier, the error event type. , At least one of the type or detailed type, measurement value, and error content of the device in which the error is detected may be included. Here, the error content may include violation of the installation location described above, exceeding the measurement range, and the like.

Referring to FIG. 6, the smart farm environment data table is a data table containing information on measurement data measured using various sensors installed in the smart farm, and includes measured time, measured equipment identifier, farm identifier, area identifier, It may include a facility building identifier, a smart bed identifier, and a measurement value.

Referring to FIG. 7, the operating equipment metadata table is a data table including status information of various operating equipment installed in a smart farm, and is an equipment identifier, farmer identifier, area identifier, smart bed identifier, and transducer type (sensor, controller Etc.), equipment type (detail types such as sensor type, controller type, etc.), measurement or control range (minimum, maximum), installation date and time, usage time, data transmission period, change time, etc.

Referring to FIG. 8, the smart farm operation information table is a data table containing the operation status of the smart farm, and includes operating hours, farm identifiers, installed operating equipment identifiers, area identifiers, facility building identifiers, smart bed identifiers, operation status or status. And the like.

Referring to FIG. 9, a data table for managing an error diagnosis rule may include a rule identifier, a rule type, an expression for a rule, a rule change time, and the like. Here, the error diagnosis rule may include a rule directly edited by a user or a rule inferred based on collected data.

Referring to FIG. 10, the ID management table of the push server may include an identifier for a smart farm user or a user terminal interworked with the push server, an identifier of the push server, and the like.

11 is a flowchart of a method for diagnosing an error of a smart farm operating equipment according to the first embodiment of the present invention.

Referring to FIG. 11, the error diagnosis method performed by the error diagnosis device of the smart farm operating equipment includes receiving a control message for triggering error diagnosis (S100), and when the control message is received, the smart farm Analyzing the collected data based on a preset error diagnosis rule (S110), outputting the error determination result of the operating equipment installed in the smart farm according to the analysis result (S120), and error through a user interface It may include a step (S130) of providing the determination result to the user.

Here, the control message may be a message requesting an error diagnosis of an external user terminal or a palm cloud, or may be a message periodically generated in the error diagnosis apparatus.

Here, the user interface is not only a display device directly coupled or interlocked with an error detection device, but also a user terminal or a palm cloud display device that is interlocked with the error detection device through a wired or wireless network, and an error detection device is requested by linking with a user terminal or palm cloud. It may include a software module for receiving the message and transmitting the result message.

The collected data may include at least one of operation data of a smart farm, control data of a controller among operating equipment, and measurement data of a sensor among operating equipment.

Here, the providing of the error determination result to the user (S130) may include transmitting the error determination result to a push server interworking with the user's user terminal. Here, the push server transmits the error determination result to the user terminal as a push message (alarm), so that the user terminal can check the error determination result. In this case, the push message may be transmitted when it is determined that there is an error in the smart farm as a result of periodically diagnosing an error in the smart farm, and as a result of the diagnosis.

In the analyzing step (S110), if the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor by referring to the state information of the operating equipment included in the collected data, It may include determining that there is an error in the first sensor.

Here, the error determination result may include an error regarding the installation location of the first sensor.

In the analyzing step (S110), it is confirmed that the first controller is running by referring to the state information of the first controller included in the collected data, and the environmental information controlled by the first controller using the second sensor is If the change amount of the measured measurement data is less than the threshold value or the change direction is different from the state information of the first controller, determining that there is an error in the first controller or the second sensor.

In the analyzing step (S110), among the measurement data of the same type of sensors included in the collected data, the first measurement data has a difference of more than a threshold value compared to other measurement data, or the change trend of the first measurement data is different. If it is different from the measurement data, it may include determining that there is an error in a sensor corresponding to the first measurement data.

In the analyzing step (S110), an error of the operating equipment may be determined based on the growth stage based on the timeline of the crop.

Here, in the step of outputting the error determination result (S120), the specific content of the error determined according to the analyzing step (S110) (type, occurrence time, identifier of the equipment in error, the farm where the error occurred, the identifier of the area, etc.) ) May include determining.

Here, the outputting the error determination result (S120) may include providing the error determination result to the user interface.

Here, the step of outputting the error determination result (S120) may include storing the error determination result in a data table.

12 is a block diagram of an error diagnosis apparatus of a smart farm operating equipment according to an embodiment of the present invention.

Referring to FIG. 12, the error diagnosis apparatus 100 of the smart farm operating equipment includes instructions for instructing at least one processor 110 and at least one processor 110 to perform at least one step. It may include a memory (memory, 120) for storing.

Here, the error diagnosis apparatus 100 of the smart farm operating equipment may further include a communication module 130 capable of communicating with an external palm cloud or a user terminal of a smart farm operator through a wired or wireless network.

Here, the error diagnosis apparatus 100 of the smart farm operating equipment may further include a storage 140 for storing data (or data table) on the data or intermediate data transmitted and received during the error determination process, and the error determination result. have.

Here, at least one step includes receiving a control message for triggering an error diagnosis, when the control message is received, analyzing the data collected from the smart farm based on a preset error diagnosis rule, and the analysis result Accordingly, it may include outputting the error determination result of the operating equipment installed in the smart farm and providing the error determination result to the user through a user interface.

Here, the control message may be a message requesting an error diagnosis of an external user terminal or a palm cloud, or may be a message periodically generated in the error diagnosis apparatus.

The collected data may include at least one of operation data of a smart farm, control data of a controller among operating equipment, and measurement data of a sensor among operating equipment.

The analyzing may include converting the collected data into semantic web-based semantic data.

Here, the providing of the error determination result to the user may include transmitting the error determination result to a push server interworking with the user terminal of the user.

In the analyzing step, if the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor, referring to the state information of the operating equipment included in the collected data, the first sensor It may include the step of determining that there is an error.

Here, the error determination result may include an error regarding the installation location of the first sensor.

Here, for example, the error diagnosis device 100 is a communication capable desktop computer, a laptop computer, a notebook, a smart phone, a tablet PC, and a mobile phone. mobile phone), smart watch, smart glass, e-book reader, portable multimedia player (PMP), portable game console, navigation device, digital camera, digital multimedia (DMB) broadcasting) player, digital audio recorder, digital audio player, digital video recorder, digital video player, PDA (Personal Digital Assistant), etc.

13 is a flowchart illustrating a method for diagnosing an error of a smart farm operating equipment according to a second embodiment of the present invention.

Referring to FIG. 13, the error diagnosis method performed by the error diagnosis device of the smart farm operating equipment includes the step of receiving data collected from a smart farm (S200), and a data table for error diagnosis using the received data. Generating (S210), analyzing the data table based on a preset error diagnosis rule (S220), and outputting the error determination result of the operating equipment installed in the smart farm (S230) according to the analysis result. I can.

Here, the data table may include an operating equipment metadata table created by using state information of operating equipment included in the collected data.

The step of outputting the error determination result (S230) may further include generating an error detection event table including at least one of occurrence time, type, content, and occurrence area of the determined error.

Here, the error diagnosis method may further include transmitting error information according to the error detection event table to a push server interworking with the user terminal by periodically referring to the error detection event table.

The methods according to the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the computer-readable medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in computer software.

Examples of computer-readable media may include hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions may include high-level language codes that can be executed by a computer using an interpreter as well as machine language codes such as those produced by a compiler. The above-described hardware device may be configured to operate as at least one software module to perform the operation of the present invention, and vice versa.

In addition, the above-described method or apparatus may be implemented by combining all or part of its configuration or function, or may be implemented separately.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the following claims. You will understand that you can do it.

Claims (20)

It is an error diagnosis method performed by the error diagnosis device of the smart farm operating equipment,
Receiving a control message for triggering error diagnosis from a user terminal or a palm cloud;
Analyzing the data collected from the smart farm based on a preset error diagnosis rule when the control message is received;
Outputting an error determination result of the operating equipment installed in the smart farm according to the analysis result; And
Including the step of providing the error determination result to the user terminal or the palm cloud through a user interface (User Interface),
In the analyzing step, referring to the state information of the operating equipment included in the collected data, if the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor, Determining that there is an error in the first sensor,
The preset error diagnosis rule,
An error diagnosis method comprising at least one of an error diagnosis rule of a single device, an error diagnosis rule based on a relationship between a plurality of devices, and an error diagnosis rule based on time or environment. delete In claim 1,
The collected data includes at least one of operation data of the smart farm, control data of a controller among the operation equipment, and measurement data of a sensor among the operation equipment. In claim 1,
Providing an error determination result to a user terminal or a palm cloud through the user interface,
And transmitting the error determination result to a push server interworking with the user terminal of the user. delete In claim 1,
The error determination result,
A method for diagnosing errors, including an error in the installation location of the first sensor. In claim 1,
The analyzing step,
The amount of change in measurement data obtained by measuring environmental information controlled by the first controller by using a second sensor and confirming that the first controller is running with reference to the state information of the first controller included in the collected data And determining that there is an error in the first controller or the second sensor when the state information of the first controller and the direction of change are different from the threshold value. In claim 1,
The analyzing step,
Among the measurement data of the same type of sensors included in the collected data, if the first measurement data has a difference of more than a threshold value compared to other measurement data, or the change trend of the first measurement data is different from the other measurement data, And determining that there is an error in a sensor corresponding to the first measurement data. In claim 1,
The analyzing step,
An error diagnosis method for determining an error of the operating equipment based on a growth stage based on a crop timeline. As an error diagnosis device for smart farm operating equipment,
At least one processor; And
Including a memory (memory) for storing instructions (instructions) instructing the at least one processor to perform at least one step,
The at least one step,
Receiving a control message for triggering error diagnosis from a user terminal or a palm cloud;
Analyzing the data collected from the smart farm based on a preset error diagnosis rule when the control message is received;
Outputting an error determination result of the operating equipment installed in the smart farm according to the analysis result; And
Including the step of providing the error determination result to the user terminal or the palm cloud through a user interface (User Interface),
In the analyzing step, referring to the state information of the operating equipment included in the collected data, if the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor, Determining that there is an error in the first sensor,
The preset error diagnosis rule,
An error diagnosis apparatus comprising at least one of an error diagnosis rule of a single device, an error diagnosis rule based on a relationship between a plurality of devices, and an error diagnosis rule based on time or environment. delete In claim 10,
The collected data includes at least one of operation data of the smart farm, control data of a controller among the operation equipment, and measurement data of a sensor among the operation equipment. In claim 10,
The analyzing step,
And converting the collected data into semantic web-based semantic data. In claim 10,
Providing the error determination result to a user terminal or a palm cloud through the user interface,
And transmitting the error determination result to a push server interlocked with the user terminal of the user. delete In claim 10,
The error determination result,
Error diagnosis apparatus including an error in the installation location of the first sensor. It is an error diagnosis method performed by the error diagnosis device of the smart farm operating equipment,
Receiving data collected from a smart farm;
Generating a data table for error diagnosis using the received data;
Analyzing the data table based on a preset error diagnosis rule; And
In accordance with the analysis result, including the step of outputting the error determination result of the operating equipment installed in the smart farm,
In the analyzing step, referring to the state information of the operating equipment included in the collected data, if the installation location of the first sensor among the operating equipment is adjacent to the controller that controls the environment measured by the first sensor, Determining that there is an error in the first sensor,
The preset error diagnosis rule,
An error diagnosis method comprising at least one of an error diagnosis rule of a single device, an error diagnosis rule based on a relationship between a plurality of devices, and an error diagnosis rule based on time or environment. In claim 17,
The data table,
An error diagnosis method comprising an operating equipment metadata table generated using state information of the operating equipment included in the collected data. In claim 17,
Outputting the error determination result,
The method further comprising generating an error detection event table including at least one of occurrence time, type, content, and occurrence zone for the determined error. In claim 19,
Further comprising the step of periodically referring to the error detection event table, transmitting error information according to the error detection event table to a push server interworking with a user terminal.

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