KR102273008B1 - METHOD FOR GENERATING CORRELATION BETWEEN QUALITY AND ENVIRONMENT USING IoT SENSOR INTERFACE BOARD AND SYSTEM USING THE SAME - Google Patents

Abstract

According to an embodiment of the present invention, in a method for deriving a quality-environmental correlation based on IoT sensor data, IoT data for receiving environmental sensor data for collecting environmental information and PLC measurement data measured by PLC equipment It is possible to provide a method for deriving a quality-environmental correlation, comprising the step of collecting, generating environmental information based on environmental sensor data and PLC measurement data, and generating a quality-environmental correlation based on the environmental information. .

Description

A method for deriving quality-environment correlation using an IoT sensor interface board and a system using the same {METHOD FOR GENERATING CORRELATION BETWEEN QUALITY AND ENVIRONMENT USING IoT SENSOR INTERFACE BOARD AND SYSTEM USING THE SAME}

The present invention relates to a method for deriving a quality-environment correlation using an IoT sensor interface board and a system using the same. More specifically, the present invention relates to a method and system for acquiring IoT sensor data related to environmental information and quality information by using an IoT sensor interface board, and deriving a quality-environmental correlation for each process by time.

The automobile industry is the world's largest manufacturing industry, with global sales of more than $1 trillion and employment of more than 10,000 people. In addition, it is a very important industry that drives the national economic growth, and as a large number of materials and parts are used throughout the country, it is an important industry that helps the balanced national development as well as regional employment effects.

This important automotive industry encompasses a wide range of business areas of design, development, manufacturing and sales, but automakers and consumers demand perfect quality levels.

In addition, since the strengthening of manufacturing-based competitiveness, such as the white home appliance industry, in addition to the automobile industry, has to start and progress from quality, an easier, more convenient, and more precise measuring tool must be used from the manufacturing site to help establish a company-wide quality system. Not only can it increase efficiency, but it can also contribute to local industries and further enhance national competitiveness.

In particular, in order to achieve manufacturing cost reduction through measurement/measurement automation, process measurement management is required, and measuring devices are used as a method of obtaining data necessary for defect management and improvement plan establishment among data used for process measurement management.

However, there is only a demand for such measurement/measurement automation in small and medium-sized sites, but does not reach actual application, and only a measuring system system that can be easily introduced in the field is implemented with a relatively simple configuration.

Therefore, it is necessary to design a factory layout that enables measurement automation by linking IoT technology that can collect various sensor information, and it is necessary to develop a method and system capable of real-time analysis of measurement data by automating factory operations such as measurement management and operation. .

Republic of Korea Patent No. 10-1541652 (2015.07.28)

An object of the present invention is to provide a board for measurement data collection and sensor interface incorporating IoT technology, which can be used in an industrial field.

Another object of the present invention is to implement a method and system for deriving a correlation between the quality of a product or part and a corresponding manufacturing environment based on IoT sensor data collected using an IoT sensor interface board.

Another object of the present invention is to provide a method and system for deriving a quality-environmental correlation by measuring the quality of a product or part based on a measurement value of a gauge sensor.

In addition, the present invention collects and monitors data measured by environmental sensors, PLC sensors, and gauge sensors, predicts quality defects of products or parts by deriving quality-environmental correlation, and provides an alarm to the user terminal in case of abnormality detection It aims to provide a deliverable method and system.

Another object of the present invention is to provide a method for deriving a quality-environmental correlation that can be used in an apparatus for measuring runout of an engine shaft for a vehicle by connecting a gauge sensor to a cam bore and an oil seal bore disposed in an engine room of a vehicle.

The problems to be solved of the present invention are not limited to the above-mentioned contents, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to an embodiment of the present invention, in a method for deriving a quality-environmental correlation based on IoT sensor data, IoT data for receiving environmental sensor data for collecting environmental information and PLC measurement data measured by PLC equipment collection stage; generating environmental information based on the environmental sensor data and the PLC measurement data; and generating a quality-environmental correlation based on the environment information.

Here, the IoT data collection step may further include receiving gauge sensor data measured by a gauge sensor, and generating quality information from the gauge sensor data after the IoT data collection step.

In addition, the IoT data collection step may use LoRA communication and WiFi communication.

The method may further include associating timestamp information and location area information for each of the environmental sensor data, the PLC measurement data, and the gauge sensor data collected in the IoT data collection step.

Also, the corresponding season information may be derived based on the timestamp information.

In addition, corresponding process information may be derived based on the location area information.

In addition, the generating of the quality information may include deriving the reliability of the quality information based on environmental information at the time of generating the quality information from the gauge sensor data.

In addition, the gauge sensor data may be measured values of cylindricity, roundness, and concentricity for a plurality of cam bores mounted on the camshaft.

According to another embodiment of the present invention, in a system for deriving a quality-environmental correlation based on IoT sensor data, receiving environmental sensor data for collecting environmental information from a plurality of environmental sensors, and measuring an IoT sensor interface board configured to receive PLC measurement data; and a data correlation analysis processing unit configured to generate a quality-environmental correlation based on the environmental information, wherein the data correlation analysis processing unit generates environmental information based on the environmental sensor data and the PLC measurement data , a quality-environment correlation derivation system can be provided.

Here, the IoT sensor interface board may receive the gauge sensor data measured by the gauge sensor, and the data correlation analysis processing unit may be configured to generate quality information from the gauge sensor data.

In addition, the IoT sensor interface board may use LoRA communication and WiFi communication.

In addition, the IoT sensor interface board includes a timestamp processing unit for processing timestamp information and a location area processing unit for processing location area information for each of the environmental sensor data, the PLC measurement data, and the gauge sensor data can

In addition, the data correlation analysis processing unit may be configured to derive the corresponding season information based on the timestamp information.

In addition, the data correlation analysis processing unit may be configured to derive the corresponding process information based on the location area information.

In addition, the data correlation analysis processing unit may be configured to derive the reliability of the quality information based on the environment information at the time of generating the quality information from the gauge sensor data.

Also, the gauge sensor data may be measured values of cylindricity, roundness, and concentricity for a plurality of cam bores mounted on the camshaft.

According to the present invention, it is possible to provide a board for measurement data collection and sensor interface incorporating IoT technology, which can be used in industrial fields.

In addition, according to the present invention, it is possible to implement a method and system for deriving a correlation between the quality of a product or part and a corresponding manufacturing environment based on IoT sensor data collected using an IoT sensor interface board.

In addition, according to the present invention, it is possible to provide a method and system for deriving a quality-environmental correlation by measuring the quality of a product or part based on a measurement value of a gauge sensor.

In addition, according to the present invention, data measured by environmental sensors, PLC sensors, and gauge sensors are collected and monitored, quality-environmental correlation is derived to predict product or component quality defects, and in the case of abnormality detection, it is transmitted to the user terminal. A method and system capable of delivering an alarm may be provided.

In addition, according to the present invention, by connecting the gauge sensor to the cam bore and the oil seal bore disposed in the engine room of the vehicle, it is possible to provide a method for deriving a quality-environmental correlation that can be used in an apparatus for measuring runout of an engine shaft for a vehicle.

Effects of the present invention are not limited to the above-mentioned contents, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram illustrating the configuration of a system for deriving a quality-environmental correlation based on IoT sensor data according to an embodiment of the present invention.
2 is an exemplary diagram illustrating the configuration of a quality-environmental correlation deriving system using an IoT sensor interface board according to an embodiment of the present invention.
3 is a block diagram for explaining the configuration of the environmental sensor 100 according to an embodiment of the present invention.
4 is a block for explaining the configuration of the IoT sensor interface board 500 according to an embodiment of the present invention.
5 is a block for explaining the configuration of the data correlation analysis processing unit 600 according to an embodiment of the present invention.
6 is a flowchart illustrating a method for deriving a quality-environmental correlation based on IoT sensor data according to an embodiment of the present invention.
7 is an exemplary view for explaining the configuration of a gauge sensor connected to an automobile engine room cam bore and an oil seal bore according to an embodiment of the present invention.
8 is an exemplary diagram for explaining a configuration for measuring gauge sensor data using an air gauge according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, the present invention will be described in detail so that those of ordinary skill in the art can easily carry out the present invention. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase.

As used herein, “comprises”, “comprising” refers to the presence or absence of one or more other components, steps, operations and/or elements mentioned addition is not excluded.

Also, terms including ordinal numbers such as first, second, etc. used in the present invention may be used to describe the components, but the components should not be limited by the terms. These terms are used only for the purpose of distinguishing one component from another. In addition, in the description of the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

In addition, the components shown in the embodiment of the present invention are shown independently to represent different characteristic functions, and it does not mean that each component is composed of separate hardware or a single software component. That is, each component is listed as each component for convenience of description, and at least two components of each component are combined to form one component, or one component can be divided into a plurality of components to perform a function. Integrated embodiments and separate embodiments of each of these components are also included in the scope of the present invention without departing from the essence of the present invention.

Hereinafter, an embodiment according to the present invention will be described in detail with reference to the accompanying drawings. The configuration of the present invention and the effects thereof will be clearly understood through the following detailed description.

1 is a block diagram illustrating the configuration of a system for deriving a quality-environmental correlation based on IoT sensor data according to an embodiment of the present invention.

The environmental sensor 100 includes a plurality of sensors for measuring manufacturing environment-related data such as temperature, humidity, vibration, noise, CO (carbon monoxide), CO2 (carbon dioxide), and location, and IoT sensor interface board 500 may receive environmental sensor data for collecting environmental information from the environmental sensor 100 through the wired/wireless communication network 400 . The environmental sensor 100 serves to collect environmental information related to a location or area in which a manufacturing process is performed, and a corresponding machine, and is configured to periodically or aperiodically measure environment-related data in real time.

PLC (Programmable Logic Controller) equipment 200 is equipment for controlling and automating various facilities and devices used in the manufacturing process, and the protocol provided by the vendor of the PLC equipment 200 is often non-standard, so a specialized communication method You may need to set it accordingly. The IoT sensor interface board 500 may be configured to receive PLC measurement data measured by the PLC device 200 through the wired/wireless communication network 400 .

The gauge sensor 300 may be composed of a plurality of gauge sensors for measuring a number related to quality information of a product or part related to the length, size, shape, accuracy, etc. of the manufactured product or part, and the IoT sensor interface board 500 ) may receive the gauge sensor data from the gauge sensor 300 through the wired/wireless communication network 400 .

The wired/wireless communication network 400 may support a wired communication network and a wireless communication network, and the wireless communication network may include cellular communication or short-range communication. In particular, short-range communication may include at least one such as Long Range (LoRA), Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee or Near Field Communication (NFC), and the communication method is limited thereto. it is not

The IoT sensor interface board 500 is configured to collect IoT sensor data measured from the environmental sensor 100 , the PLC equipment 200 , and the gauge sensor 300 through the wired/wireless communication network 400 , and environmental sensor data, PLC measurement For each of the data and the gauge sensor data, a timestamp processing unit for processing timestamp information and a location area processing unit for processing the location area information may be included.

The data correlation analysis processing unit 600 is configured to generate a quality-environmental correlation based on environmental sensor data, PLC equipment data, and gauge sensor data collected from the IoT sensor interface board 500 , a local server, a cloud server, etc. It may be composed of various computing devices and the like.

The data correlation analysis processing unit 600 may generate environment information based on environmental sensor data and PLC measurement data. Also, the data correlation analysis processing unit 600 may generate quality information from the gauge sensor data. In addition, quality-environmental correlation may be derived and generated based on the generated environment information and quality information.

In addition, the data correlation analysis processing unit 600 may derive corresponding year, month, day, season information, etc. based on timestamp information of IoT sensor data. Also, the data correlation analysis processing unit 600 may derive process information related to a corresponding location based on location area information of IoT sensor data.

In addition, the data correlation analysis processing unit 600 may be additionally configured to derive the reliability of the quality information based on the environment information at the time of generating the quality information from the gauge sensor data. For example, when the temperature or humidity conditions are outside the normal range in an environment that does not meet the normal operating environmental conditions of the gauge sensor or may have a large difference from the middle value of the normal range, such as summer or winter, the reliability of the quality information can be set low. .

The cloud server 700 is connected to the IoT sensor interface board 500 and the data correlation analysis processing unit 600 to transmit and store data to the upper cloud server, and the cloud server 700 can perform a role of analysis, If necessary, the data correlation analysis processing unit 600 may be implemented in the form of a cloud server.

2 is an exemplary diagram illustrating the configuration of a quality-environmental correlation deriving system using an IoT sensor interface board according to an embodiment of the present invention.

Referring to FIG. 2 , the IoT sensor interface board 500 may use LoRA communication to collect environmental sensor data for environmental information and gauge measurement sensor data.

In addition, it secures 2-port LAN (10/100 Ethernet) that can cover the poor environment of industrial sites such as factory environment and the wireless network environment that thoroughly internalizes security, so that it can be used for machine tools such as PLC and internal network environments. A reliable wired network environment can be established.

In addition, the serial communication (RS-232, RS-485) port enables interface with various devices, including older PLCs, and supports CAN bus interface to realize high-speed communication in fields such as power management. You can develop interfaces and apply I2C, SPI, GPIO ports, etc.

In addition, it is possible to make a plurality of USB host ports and use it with various industrial devices and measuring instruments, sensors, etc., and support a single USB device port that supports new interface applications to enable system work such as debugging.

The IoT sensor interface board 500 may include, for example, a 7-inch or 10-inch LCD display, and may output images, audio, etc. to an external monitor through an HDMI terminal without a display.

3 is a block diagram for explaining the configuration of the environmental sensor 100 according to an embodiment of the present invention.

Referring to FIG. 3 , the environmental sensor 100 includes a temperature sensor 101 for measuring the temperature of the corresponding location or area, the humidity sensor 102 for measuring the humidity of the corresponding location or area, and vibration of the corresponding location or area. Vibration sensor 103 for measuring the noise sensor 104 for measuring the noise of the location or area, the CO sensor 105 for measuring the amount of CO in the location or area, the location of the location or area It may include a location sensor 106 such as a GPS sensor for identifying, and may include additional sensors for acquiring environmental information in addition to these.

4 is a block for explaining the configuration of the IoT sensor interface board 500 according to an embodiment of the present invention.

Referring to FIG. 4 , the IoT sensor interface board 500 may include a communication unit 510 for wired/wireless communication and a data processing unit 520 for processing time information and spatial information of the collected IoT data.

The communication unit 510 may include a LoRA module 511 , a WiFi module 512 , a Bluetooth module 513 , a wired communication module 514 , and the like to support various wired and wireless communication protocols. In particular, LoRA communication is mainly used for Internet of Things (IoT) communication in which simple information is exchanged, and has the advantage of having a wider communication range compared to existing low-power wireless communication protocols such as Bluetooth or Zigbee.

The WiFi module 512 may support not only WiFi 802.11a/b/g/n/ac 2.4GHz and 5GHz but also next-generation WiFi protocol communication. The Bluetooth module 513 may support next-generation Bluetooth protocol communication as well as Bluetooth 3.0, 4.0/4.1/4.2, and 5.0.

Also, the wired communication module 514 may include a wired communication interface for supporting interfaces such as RS-232 and RS-485.

In addition, the data processing unit 520 includes a timestamp processing unit 521 for processing timestamp information and a location area processing unit 522 for processing the location area information for each of the environmental sensor data, the PLC measurement data, and the gauge sensor data. may include. In the IoT sensor interface board 500 , a task of adding temporal information and spatial information to sensor data information received and collected through the communication unit 510 through the data processing unit 520 is processed. Such spatiotemporal information of IoT sensor data can be utilized when deriving a quality-environmental correlation.

The timestamp processing unit 521 may add information related to the time at which the sensor data is collected, for example, year, month, day, time information, etc. in association with the corresponding sensor data, and based on these information, Month/day, quarter, period, season information, etc. can be derived. In addition, the location area processing unit 522 may add information related to a location where sensor data is collected in association with the corresponding sensor data, and utilize GPS sensor information or a location database (DB) of the corresponding sensor to provide location or area information. can be derived, and process information related to the location or area of the corresponding sensor can be derived based on the location or area information. Based on the location area information, process information related to a value measured by the corresponding sensor may be identified, and environmental information and quality information for each process may be derived.

Although not shown in FIG. 4 , the IoT sensor interface board 500 may additionally include a display unit. The display unit is, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a micro LED, a micro electro mechanical system (MEMS) It may be configured in the form of a display or a touch screen, but is not limited thereto.

5 is a block for explaining the configuration of the data correlation analysis processing unit 600 according to an embodiment of the present invention.

The environment information generation unit 601, the quality information generation unit 602, the quality-environment correlation generation unit 603, the quality defect prediction unit 604 and the abnormal detection alarm constituting the data correlation analysis processing unit 600 Unit 605 may include programs or program modules that may be executed by one or more processors. The programs or program modules included in the data correlation analysis processing unit 600 may be configured in the form of an operating system, an application program, or a program, and are stored on various types of widely used storage devices. It can be physically stored. Such programs or program modules may include one or more routines, subroutines, programs, objects, components, instructions, data structures, and specific tasks ( task) or may include, but are not limited to, various forms for executing specific data types.

The environmental information generator 601 may generate environmental information based on the environmental sensor data collected from the environmental sensor 100 and the PLC measurement data collected from the PLC equipment 200 . The environmental sensor data and PLC measurement data may be received from the IoT sensor interface board 500, and the time and location area information processed through the timestamp processing unit 521 and the location area processing unit 522 are received together, and time and Environment information may be generated based on the location area information. In particular, the environment of the manufacturing environment based on operation data such as manufacturing environment data derived from environmental sensor data such as temperature, humidity, vibration, noise, and CO, and control and operation information of manufacturing facilities and equipment derived from PLC measurement data Both environmental factors and equipment operation control factors can be considered.

The quality information generating unit 602 may generate quality information from the gauge sensor data. For example, the gauge sensor 300 may measure the length, size, shape, accuracy, etc. of a manufactured product or part, and the quality of the product or part based on the gauge sensor measurement value measured through the gauge sensor 300 . information can be generated.

The quality-environmental correlation generating unit 603 is configured to derive a correlation between the environmental information generated from the environmental information generating unit 601 and the quality information generated from the quality information generating unit 602 , and machine learning and deep Correlations can be derived using learning techniques.

For example, in the case of supervised learning, a kNN (k-Nearest Neighbor) model or a support vector machine or a learning model of a decision tree can be used, and in the case of unsupervised learning, a k-means clustering algorithm, etc. can be used. can be utilized

The quality-environmental correlation generating unit 603 may derive the quality-environmental correlation for each process based on time and location area information, based on time information and location area information processed for IoT sensor data.

Also, the quality-environment correlation generating unit 603 may be configured to dynamically apply the reliability of the quality information based on the environment information at the time of generating the quality information from the gauge sensor data. For example, an environment that does not meet the normal operating environmental conditions of the gauge sensor or that may differ significantly from the mid-range of the normal range, such as summer or winter, or temperature, humidity, vibration, and noise conditions in the measurement location of the gauge sensor, is ideal for measurement. The farther away from the condition, the lower the reliability of the quality information can be set. In this case, it is possible to induce accurate quality information measurement by changing the environmental conditions of the gauge sensor data based on the abnormal environmental information of the gauge sensor data.

The quality defect prediction unit 604 is based on the quality-environmental correlation derived through machine learning, such as machine learning, at a corresponding time, for example, a corresponding year, month, day, season, etc., and a corresponding location area, for example, a location where a specific process is performed. If the product of quality judged to be within the normal range is out of the environmental range, it is predicted that the quality defect rate of products or parts manufactured in the relevant time and location area may increase, and the change through real-time quality-environmental correlation deduction It may be configured to perform bad prediction while monitoring whether or not there is. In addition, daily, monthly, quarterly, and seasonal analysis and failure prediction are possible based on timestamp information of IoT sensor data.

The abnormality detection alarm unit 605 may be configured to deliver an abnormality detection related alarm to a corresponding terminal or a corresponding user when a defect of a product or part is predicted or a defect rate is predicted to be higher than or equal to a predetermined standard.

6 is a flowchart illustrating a method for deriving a quality-environmental correlation based on IoT sensor data according to an embodiment of the present invention.

First, a plurality of types of IoT sensor data may be generated through various sensors, and environmental sensor data is generated from the environmental sensor 100 to collect environmental information (S610), and PLC measured through the PLC equipment 200 Measurement data may be generated (S620), and gauge sensor data may be generated from the gauge sensor 300 to collect measurement information of products and parts (S630).

The generated environmental sensor data, PLC measurement data, and gauge sensor data may be collected in real time as IoT sensor data through the IoT sensor interface board 500 ( S640 ). The measurement and collection cycle of each sensor data may be performed as needed. can be varied.

In addition, timestamp processing and location area processing for associating time-related timestamp information and space-related location area information are performed for each IoT sensor data including environmental sensor data, PLC measurement data, and gauge sensor data (S650). Based on the timestamp information and location area information of the IoT sensor data, quality-environment correlations for each process based on time and location area information can be derived.

The data correlation analysis processing unit 600 may generate quality information from the gauge sensor data received from the IoT sensor interface board 500 ( S660 ). Here, the quality information is related to the defect rate and accuracy derived from the gauge sensor data. can be a value.

In addition, manufacturing environment information may be generated based on environmental sensor data and PLC measurement data, and a quality-environmental correlation between quality and environment may be generated (S670). For example, kNN (k-Nearest Neighbor) or support vector. A quality-environment correlation can be derived by using a support vector machine model, a k-means clustering algorithm, etc.

Based on the generated quality-environmental correlation information, it is possible to monitor continuously collected IoT sensor data and predict a quality defect of a product or part. (S680) For example, a corresponding time based on the derived quality-environmental correlation And when the environmental condition determined as a range in which products (parts) of normal quality are manufactured in the location region, the possibility of quality defects may be predicted (S680).

In addition, when real-time quality information measured from gauge sensor data or quality failure derived from quality-environmental correlation is predicted, an abnormality may be detected and an alarm may be transmitted to a related user terminal (S690).

7 is an exemplary view for explaining the configuration of a gauge sensor connected to an automobile engine room cam bore and an oil seal bore according to an embodiment of the present invention.

The quality-environment correlation derivation system according to the present invention is applied to a runout measurement device of an engine shaft for a vehicle, and it can be applied to measurement data of a cam bore and an oil seal bore mounted and disposed on a cam shaft in an automobile engine room. In addition, the gauge sensor data can be used as measurement values of cylindricity, roundness, and concentricity for cam bores and oil seal bores. Here, the runout is a quantity indicating a defective state such as roundness and verticality existing on the rotating camshaft shaft of an automobile engine, and is one of the matters to be checked before shaft alignment.

Referring to FIG. 7 , for example, by automatically rotating the cam bores arranged at four points in the longitudinal direction of the camshaft by 360 degrees based on the oil seal bore, the cylindricity, roundness, and concentricity of the plurality of cam bores around the oil seal bore are measured. A gauge system to measure can be configured. Here, the cylindricity refers to the amount of change in the total radius when the measured object is rotated around the center as the reference axis and the measurement area is moved, and the roundness is a value indicating the degree of whether it is a roundness, and the concentricity ( concentricity) is a value measuring the degree of whether a plurality of cylinders have the same center, and in a cylindrical part that should have an axial center on the same straight line as the reference axial center, it means the size of the error between the axial center of the cylindrical part and the reference axial center.

8 is an exemplary diagram for explaining a configuration for measuring gauge sensor data using an air gauge according to an embodiment of the present invention.

By using an air gauge using an air nozzle as a gauge sensor, it is possible to measure rotating parts. Referring to FIG. 8, the air pressure changes according to the gap of the measuring part in contact with the cam bore and the oil seal bore, and the analog signal is converted to digital through the pressure sensor through the flow valve according to the air pressure, so that the gauge measurement data may be generated, and corresponding data may be displayed on the IoT sensor interface board 500 or an external computer.

The method and system according to the embodiment of the present invention have been described as specific various embodiments, but this is only an example, and the present invention is not limited thereto, and it is interpreted as having the widest scope according to the basic idea disclosed in the present specification. should be A person skilled in the art may implement a pattern of a shape not specified by combining or substituting the disclosed embodiments, but this also does not depart from the scope of the present invention. In addition, those skilled in the art can easily change or modify the disclosed embodiments based on the present specification, and it is clear that such changes or modifications also fall within the scope of the present invention.

100: environmental sensor
200: PLC equipment
300: gauge sensor
400: wired and wireless communication network
500: IoT sensor interface board
600: data correlation analysis processing unit
700: cloud server

Claims (16)

A method for deriving a quality-environmental correlation based on IoT sensor data, the method comprising:
IoT data collection step of receiving environmental sensor data for collecting environmental information and PLC measurement data measured by PLC equipment;
generating environmental information based on the environmental sensor data and the PLC measurement data; and
generating a quality-environmental correlation based on the environmental information
including,
The IoT data collection step receives the gauge sensor data measured by the gauge sensor, and the IoT data collection step uses LoRA communication and WiFi communication,
Further comprising the step of generating quality information from the gauge sensor data after the IoT data collection step,
Further comprising the step of associating timestamp information and location area information for each of the environmental sensor data, the PLC measurement data, and the gauge sensor data collected in the IoT data collection step,
The step of generating the quality information includes:
and deriving the reliability of the quality information based on the environmental information at the time of generating the quality information from the gauge sensor data.
delete delete delete The method of claim 1 , wherein the corresponding season information is derived based on the timestamp information.
The method of claim 1, wherein the process information is derived based on the location area information.
delete The method according to claim 1, wherein the gauge sensor data is a measurement value of cylindricity, roundness, and concentricity for a plurality of cam bores mounted on a camshaft.
A system for deriving a quality-environmental correlation based on IoT sensor data, the system comprising:
an IoT sensor interface board configured to receive environmental sensor data for collecting environmental information from a plurality of environmental sensors, and receive PLC measurement data measured by a PLC device; and
A data correlation analysis processing unit configured to generate a quality-environmental correlation based on the environmental information
including,
The correlation analysis processing unit is to generate environmental information based on the environmental sensor data and the PLC measurement data,
The IoT sensor interface board receives the gauge sensor data measured by the gauge sensor, the IoT sensor interface board uses LoRA communication and WiFi communication, and the correlation analysis processing unit is configured to generate quality information from the gauge sensor data become,
The IoT sensor interface board includes a timestamp processing unit for processing timestamp information and a location area processing unit for processing location area information for each of the environmental sensor data, the PLC measurement data, and the gauge sensor data,
Wherein the data correlation analysis processing unit is configured to derive the reliability of the quality information based on the environmental information at the time of generating the quality information from the gauge sensor data, a quality-environmental correlation derivation system.
delete delete delete The system according to claim 9, wherein the data correlation analysis processing unit is configured to derive corresponding seasonal information based on the timestamp information.
The system according to claim 9, wherein the data correlation analysis processing unit is configured to derive corresponding process information based on the location area information.
delete The system according to claim 9, wherein the gauge sensor data is a measurement value of cylindricity, roundness, and concentricity for a plurality of cam bores mounted on a camshaft.

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