KR102817561B1 - IoT SMART OVEN POWER CONSUMPTION MEASUREMENT AND RETAIL OVEN OPERATION CONTROL SYSTEM BASED ON POWER USAGE PATTERNS, AND METHOD THEREOF - Google Patents

Abstract

The present invention relates to a retail oven operation control system using IoT smart oven power consumption measurement and power consumption pattern, and a method thereof, and more specifically, to a retail oven operation control system using IoT smart oven power consumption measurement and power consumption pattern for improving energy efficiency through power consumption management for power consumption optimization and prevention of unnecessary preheating, and for improving retail operation efficiency through multi-oven control, and a method thereof.

Description

IoT SMART OVEN POWER CONSUMPTION MEASUREMENT AND RETAIL OVEN OPERATION CONTROL SYSTEM BASED ON POWER USAGE PATTERNS, AND METHOD THEREOF

The present invention relates to a retail oven operation control system using IoT smart oven power consumption measurement and power consumption pattern, and a method thereof, and more specifically, to a retail oven operation control system using IoT smart oven power consumption measurement and power consumption pattern for improving energy efficiency through power consumption management for power consumption optimization and prevention of unnecessary preheating, and for improving retail operation efficiency through multi-oven control, and a method thereof.

Oven control in conventional retail stores typically involved users manually setting the temperature and cooking mode of each oven to cook bread.

These mainly rely on manual operation, and there was a problem that it was difficult to provide an optimal cooking environment by not considering the energy efficiency of the oven that operates according to a given cooking time and temperature setting.

Existing technologies also did not provide the ability to measure power consumption or automatically adjust cooking modes or operating times based on power usage patterns.

In other words, most traditional retail stores' oven control methods simply automatically proceed with the cooking process according to the set temperature and time, which can result in unnecessary energy waste or affect cooking quality.

In addition, the existing system lacked the technology to efficiently control multiple ovens simultaneously in a retail environment. In order to manage multiple ovens simultaneously, it is necessary to monitor the operating status and power consumption of each oven and perform optimal control accordingly, but there has been a limitation in not being able to provide a comprehensive management platform for this.

Accordingly, the technology field requires technology development to improve energy efficiency through power usage management to optimize power consumption and prevent unnecessary preheating, and to improve retail store operation efficiency through multi-oven control.

Republic of Korea Patent Application No. 10-2018-0082957 "APPARATUS AND METHOD OF PREDICTING POWER USAGE" Republic of Korea Patent Application No. 10-2019-0034396 "APPARATUS FOR FAULT DIAGNOSIS OF HEATER USING POWER CONSUMPTION AND METHOD THEREOF"

The present invention is intended to solve the above problems, and to provide a retail store oven operation control system using IoT smart oven power usage measurement and power usage patterns for improving energy efficiency through power usage management for power consumption optimization and prevention of unnecessary preheating, and for improving retail store operation efficiency through multi-oven control, and a method therefor.

However, the purposes of the present invention are not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

In order to achieve the above purpose, the IoT smart oven power usage measurement and retail oven operation control system using power usage pattern according to an embodiment of the present invention comprises a plurality of IoT smart ovens (100), a network (200), and a management server (300). In the IoT smart oven power usage measurement and retail oven operation control system (1) using power usage pattern, the management server (300) receives oven serial numbers, image information, and sensing information (including voltage, current, power factor, and temperature) from each IoT smart oven (100) operated in a retail store through the network (200) at a predetermined cycle (e.g., in units of n to m minutes), and then stores the oven serial numbers as metadata and the image information and sensing information as unit collection information on a database (330). And it is characterized by including an analysis module (322) that recognizes the operation of the IoT smart oven (100) according to the image information and sensing information, and then measures the power usage corresponding to the cooking mode and operation time of the oven based on the sensing information that measures the type of bread cooked in the oven from the image information, and the voltage, current, power factor, and temperature of the IoT smart oven (100) while the bread is being cooked.

At this time, the IoT smart oven (100) is formed as a high-temperature camera inside the oven for photographing the inside of the IoT smart oven (100), or is formed as a separately mounted form through a housing that can photograph the inside from the outside of the IoT smart oven (100), and includes a camera (110); which obtains image information inside the oven; It is possible to provide an IoT smart oven power usage measurement and retail store oven operation control system using power usage patterns.

In addition, the IoT smart oven (100) can provide a retail oven operation control system using an IoT smart oven power usage measurement and power usage pattern, characterized in that it includes a sensor module (120) including a voltage sensor (121), a current sensor (122), and a temperature sensor (123) to generate sensing information corresponding to information including voltage, current, power factor, and temperature in the IoT smart oven (100) while bread is being cooked during oven operation.

In addition, the management server (300) further includes a pattern learning module (323) that learns the power usage pattern of the oven over time using an AI model for the unit collection information provided from each IoT smart oven (100) by the information collection module (321); It is possible to provide an IoT smart oven power usage measurement and retail store oven operation control system using the power usage pattern.

In addition, the management server (300) may provide an IoT smart oven power usage measurement and retail store oven operation control system using power usage patterns, characterized in that it further includes an information provision module (324) that preheats the IoT smart oven (100) according to the power usage pattern using an AI model or provides recommendation information for changing the cooking order or cooking time of bread to the IoT smart oven (100) or smart terminal (400).

In order to achieve the above purpose, the method for measuring power usage of an IoT smart oven and controlling operation of a retail store oven using a power usage pattern according to an embodiment of the present invention is characterized by including a first step in which a management server (300) receives an oven serial number, image information, and sensing information (including voltage, current, power factor, and temperature) as real-time information from each IoT smart oven (100) operated in a retail store through a network (200), and then stores the oven serial number as metadata and the image information and the sensing information as unit collection information in a database (330); and a second step in which the management server (300) recognizes the operation of the IoT smart oven (100) according to the image information and the sensing information, and then measures power usage corresponding to the cooking mode and operation time of the oven based on the sensing information measuring the type of bread being cooked in the oven and the voltage, current, power factor, and temperature of the IoT smart oven (100) while the bread is being cooked from the image information.

At this time, after the second step, the management server (300) learns the power usage pattern of the oven over time using an AI model for the unit collection information provided from each IoT smart oven (100) by the first step; It is possible to provide a method for measuring IoT smart oven power usage and controlling the operation of a retail store oven using the power usage pattern, characterized in that it further includes a third step;

In addition, after the third step, the management server (300) preheats the IoT smart oven (100) according to the power usage pattern using the AI model or provides the IoT smart oven (100) or the smart terminal (400) with recommended information for changing the cooking order or cooking time of bread. The present invention can provide a method for measuring power usage of an IoT smart oven and controlling operation of a retail store oven using a power usage pattern.

A retail store oven operation control system and method using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention provide the effects of improving energy efficiency through power usage management for power consumption optimization and prevention of unnecessary preheating, and enabling retail store operation efficiency through multi-oven control.

FIG. 1 is a diagram showing a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention.
FIG. 2 is a block diagram showing components of an IoT smart oven (100) among a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention.
FIG. 3 is a block diagram showing components of a sensor module (120) of an IoT smart oven (100) among a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention.
FIG. 4 is a block diagram showing components of a management server (300) in a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating a method for controlling operation of a retail oven using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention.

Hereinafter, a detailed description of a preferred embodiment of the present invention will be described with reference to the attached drawings. In the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description will be omitted.

In this specification, when a component 'transmits' data or a signal to another component, it means that the component can transmit the data or signal directly to the other component, and can transmit the data or signal to the other component via at least one other component.

FIG. 1 is a diagram showing a retail oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention. Referring to FIG. 1, the retail oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern may include a plurality of IoT smart ovens (100), a network (200), a management server (300), and a plurality of smart terminals (400).

The network (200) is a high-speed backbone network of a large-scale communication network capable of providing large-capacity, long-distance voice and data services, and may be a next-generation wired and wireless network for providing the Internet or high-speed multimedia services. If the network (200) is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of an asynchronous mobile communication network, a communication network using the WCDMA (Wideband Code Division Multiple Access) method may be mentioned. In this case, although not shown in the drawing, the network (200) may include an RNC (Radio Network Controller). Meanwhile, although the WCDMA network is mentioned as an example, it may be a 3G LTE network, a 4G network, a next-generation communication network such as another 5G network, or another IP network based on IP. The network (200) serves to mutually transmit signals and data between a plurality of IoT smart ovens (100), a management server (300), a plurality of smart terminals (400), and other systems.

FIG. 2 is a block diagram showing components of an IoT smart oven (100) among a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention. FIG. 3 is a block diagram showing components of a sensor module (120) of an IoT smart oven (100) among a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention. First, referring to FIG. 2, the IoT smart oven (100) may include a camera (110), a sensor module (120), an MPU (130), and a communication module (140).

Through this configuration, the IoT smart oven (100) can control the operation of the oven by utilizing the power usage pattern based on the power usage measurement of the oven based on IoT within the bakery and pastry product manufacturing business such as general bakery, high-end bakery, confectionery, bakery, bakery, commercial operation business, etc.

The camera (110) is formed as a high-temperature camera inside the oven for photographing the inside of the IoT smart oven (100), or is formed as a separately mounted form through a housing that can photograph the inside from the outside of the IoT smart oven (100), thereby enabling acquisition of image information inside the oven.

The sensor module (120) can generate sensing information corresponding to information on voltage, current, power factor, and temperature in the IoT smart oven (100) while the oven is operating and bread is being cooked, and for this purpose, it can include a voltage sensor (121), a current sensor (122), and a temperature sensor (123) as shown in Fig. 3. The voltage sensor (121) and the current sensor (122) can utilize a smart power meter module as power data collection equipment.

Voltage information provided from the voltage sensor (121) can be analyzed to determine whether electricity is normally supplied within the oven, and can be provided in a mostly fixed state in the IoT smart oven (100).

The current information provided from the current sensor (122) measures the amount of current consumed by the oven to check the load status of the oven. However, the current consumed in each mode may be different depending on the cooking mode.

The power factor information calculated based on voltage information and current information indicates the efficiency of power consumption, and is the ratio between the actual active power used and the power supplied. Since the power efficiency varies depending on the operating status and cooking method of the oven, that is, depending on the cooking mode, the cooking mode can be estimated through the power factor.

Temperature information provided from the temperature sensor (123) is directly related to the cooking mode, and the cooking mode can be estimated based on whether the temperature information is maintained constant or the speed of temperature change changes to each preset threshold.

The MPU (130) can be replaced with an NPU (Neural Processing Unit) when the IoT smart oven (100) operates in a stand-alone manner without a network (200) and a management server (300) in Fig. 1.

The communication module (140) is formed to perform signal and data transmission and reception with other IoT smart ovens (100) and other management servers (300) through a network (200), and when the MPU (130) is replaced with an NPU, it can be transformed into a communication module capable of short-range wireless communication between each IoT smart oven (100) managed by the same retail store.

FIG. 4 is a block diagram showing components of a management server (300) in a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern according to an embodiment of the present invention. Referring to FIG. 4, the management server (300) includes a transceiver (310), a control unit (320), and a database (330), and the control unit (320) may include an information collection module (321), an analysis module (322), a pattern learning module (323), and an information provision module (324).

The information collection module (321) receives the oven serial number, image information, and sensing information (voltage, current, power factor, temperature, etc.) from each IoT smart oven (100) through the network (200) at a predetermined cycle, and then stores the oven serial number as metadata and the image information and sensing information as unit collection information in a database (330).

The analysis module (322) recognizes the operation of the IoT smart oven (100) based on image information and sensing information, and can then primarily analyze the type of bread (meal bread, dessert bread, fermented bread, etc.) being cooked in the oven based on the image information.

That is, the analysis module (322) can analyze the type of bread from the image information provided from the camera (210). To this end, various bread type patterns corresponding to the types of bread are stored in the database (330), and the analysis module (322) can analyze the type of bread in the bread image information by comparing the bread image information patterns included in the image information with the information of the various bread type patterns themselves or the bread type patterns tilted at a preset angle, reversed, etc.

Thereafter, the analysis module (322) can extract a list of oven cooking modes corresponding to the analyzed bread type from the database (330).

The analysis module (322) can measure power usage corresponding to the cooking mode and operating time of the oven based on sensing information measuring voltage, current, power factor, temperature, etc. of the IoT smart oven (100) while the bread is being cooked.

In addition, the analysis module (322) can extract each oven cooking mode including top and bottom heat, convection bake, steam bake, etc. according to voltage information, current information, power factor information, and temperature information, and generate power usage according to the operating time according to each extracted oven cooking mode.

More specifically, the analysis module (322) extracts a possible cooking mode according to each analyzed type of bread into a cooking mode list, and then matches the first sensing parameter change information corresponding to the change in each sensing parameter corresponding to each voltage information, current information, power factor information, temperature information, etc. included in the sensing information with the second sensing parameter change information, which is sensing parameter change information for each cooking mode stored in the database (330), to extract the “target cooking mode” currently being cooked.

The analysis module (322) calculates the effective power by measuring the voltage (V) and current (I) according to the following mathematical expression 1 for the extracted target cooking mode after extracting the target cooking mode, and calculates the actual power amount by applying the power factor.

Thereafter, the analysis module (322) uses the temperature data as correction data to reflect the power consumption with a weight according to the operating status of the oven, calculates the accumulated power amount over time to calculate the power consumption, and analyzes whether there is an error or failure in the smart power meter module using the power data collection equipment consisting of a voltage sensor (121) and a current sensor (122) for measuring the actual power amount.

That is, when temperature data is used as correction data and weighted according to the operating status of the oven is reflected in the power consumption, the analysis module (322) can assign weights by performing a multiplication operation on the calculated power amount with the first weight, which is a quantitative value preset as a fixed weight, which is a power amount weight in proportion to the change in temperature, when the temperature rises, i.e., the operation of the oven heater increases and the power consumption increases.

In addition, when the analysis module (322) reaches a state where the temperature stabilizes, that is, when the temperature information reaches a state where it has been stopped for a preset time at a preset temperature, the operation cycle of the oven heater for maintaining the temperature is adjusted, and the power consumption can be gradually reduced. At this time, the smaller the change in temperature, the more the weight can be given by performing a multiplication operation on the calculated power amount with a quantitative second weight that is preset as an inverse weight corresponding to the weight of the power consumption.

Meanwhile, as a first embodiment of error and failure analysis, the analysis module (322) can extract power consumption information according to time change for the target cooking mode of the IoT smart oven (100) matching the serial number of the IoT smart oven (100) from the database (330) and extract internal volume information of the IoT smart oven (100) matching the serial number of the IoT smart oven (100).

Thereafter, the analysis module (322) extracts another IoT smart oven (100) that has the same model name matching the serial number of the IoT smart oven (100) and has the same internal volume information, and then, when the temperature difference for cooking the same type of bread in the same target cooking mode in the extracted IoT smart oven (100) is detected to be abnormal, i.e., when the temperature difference between the two ovens is outside a predetermined range, i.e., when the temperature difference exceeds the temperature difference allowed in the normal cooking range, it can be analyzed as a failure.

In addition, as a second embodiment of the error and failure analysis different from the first embodiment, if the power measurement value for each preset time unit according to the target cooking mode differs by a preset threshold or more, the analysis module (322) extracts third sensing parameter change information corresponding to the change in each sensing parameter corresponding to each voltage information, current information, power factor information, temperature information, etc. included in the sensing information for power measurement in the same time unit according to the same target cooking mode provided from another IoT smart oven (100) in the same manner, and compares it again with the first sensing parameter change information, thereby extracting IoT smart ovens (100) that differ by a preset threshold or more n times (n is a natural number greater than or equal to 2), and then performing management of each IoT smart oven (100) by performing a failure or error check through the network (200) with a smart terminal (400) operated by a manager that matches the serial number of the extracted IoT smart oven (100).

The pattern learning module (323) can learn the power usage pattern of the oven over time by using an AI model for the unit collection information provided from each IoT smart oven (100) by the information collection module (321).

That is, the pattern learning module (323) can perform AI learning by using the type of bread collected through image information, which is data collected as unit collection information when learning the power usage pattern of the IoT smart oven (100), each parameter of sensing information, which is data related to the status of the oven, analyzed target cooking mode, operation time, etc.

The pattern learning module (323) distinguishes the type of bread or food to be cooked by type of bread as a preprocessing when performing AI learning. This is because the cooking time is different for each type of bread and there may be differences in the power consumption pattern. In addition, the pattern learning module (323) learns the influence of the status of the IoT smart oven (100), such as the temperature of the oven, the fan speed, and the heater status, on the power consumption for each model of each IoT smart oven (100) corresponding to the serial number, and learns the power consumption according to the length of the cooking time, that is, the time the oven has been in operation for each cooking mode, which is an important variable affecting the power consumption, and can also learn the power consumption for the internal temperature and target temperature of the IoT smart oven (100) corresponding to the temperature information.

According to this preprocessing process, the pattern learning module (323) can learn a model for predicting power consumption through power consumption data, which is the amount of power actually consumed at each point in time according to the type of bread, oven status-related data, cooking mode, and operating time. That is, the pattern learning module (323) collects data, removes outliers in the preprocessing stage, and creates a model for each variable.

The pattern learning module (323) can perform linear regression for model selection and learning, and learn the relationship between power consumption and various variables. The model can be learned by setting variables such as bread type, cooking mode, operation time, and temperature as independent variables and setting power consumption as a dependent variable.

In addition, the pattern learning module (323) can perform model selection and learning through a neural network method, and the neural network, which is a deep learning model, learns complex power consumption patterns, and can track power consumption patterns over time by utilizing a multi-layer perceptron (MLP) or a recurrent neural network (RNN).

The information provision module (324) preheats the oven in advance based on the learned power usage pattern using an AI model according to the type of bread, each parameter of sensing information, target cooking mode, operation time, etc. by the pattern learning module (323), or generates information on changing the cooking order and cooking time of the bread, and provides it as recommended information as the terminal identification number of the smart terminal (400) registered in the database (330) with the serial number of each IoT smart oven (100) through the network (200), or directly performs remote control of each IoT smart oven (100) through the network (20).

More specifically, the information provision module (324) proposes an optimized cooking order and cooking time based on a power consumption pattern predicted based on an AI model, and since the IoT smart oven (100) consumes optimal power only when it reaches a preset temperature, it can recommend a time to preheat in advance according to the type of bread and cooking mode, and when cooking multiple types of bread simultaneously, it can recommend an appropriate cooking order for each type of bread to minimize power consumption and efficiently distribute the cooking time.

In addition, the information provision module (324) analyzes the collected data distributed and stored by DCS DB on the database (330) for each IoT smart oven (100) model by the distributed file program through a machine learning algorithm, and when cooking multiple types of bread at the same time, it can issue a status management command suitable for each type of bread. More specifically, the machine learning algorithm used in the information provision module (324) may be one of a decision tree (DT) classification algorithm, a random forest classification algorithm, and a support vector machine (SVM) classification algorithm.

The information provision module (324) analyzes the collected data distributed and stored in the DCS DB by the distributed file program, and extracts characteristic information (method of preheating the oven according to learned power usage pattern, bread cooking order, cooking time change information, etc.) for each bread being cooked at the same time as the analyzed result, and learns the extracted characteristic information using at least one or more machine learning algorithms, and extracts cooking order recommendation for minimizing power consumption for each type of bread and cooking time distribution information for each cooking mode as a result of the learning.

That is, the information provision module (324) can apply an ensemble structure composed of a number of complementary machine learning algorithms to improve the accuracy of the information extraction results.

The decision tree classification algorithm is a method that learns in a tree structure and derives results, so the results are easy to interpret and understand, the data processing speed is fast, and rule derivation can be possible based on the search tree. RF can be applied as a way to improve the low classification accuracy of DT. The random forest classification algorithm is a method that slaughters the results of learning multiple DTs as an ensemble, so the results are more difficult to understand than DT, but the result accuracy can be higher than DT. SVM can be applied as a way to improve overfitting that may occur through DT or RF learning. The SVM classification algorithm is a method that classifies data belonging to different classes on a plane basis, so it generally has high accuracy and can have low sensitivity to structural overfitting.

FIG. 5 is a flowchart illustrating a method for measuring IoT smart oven power usage and controlling operation of a retail oven using a power usage pattern according to an embodiment of the present invention. Referring to FIG. 5, the method for measuring IoT smart oven power usage and controlling operation of a retail oven using a power usage pattern includes an information collection process (S11), an analysis process (S12), a pattern learning process (S13), and an information provision process (S14). Since each process is the same as the process performed by the information collection module (321), the analysis module (322), the pattern learning module (323), and the information provision module (324) described above, a redundant description thereof will be omitted.

The present invention can also be implemented as a computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording devices that store data that can be read by a computer system.

Examples of computer-readable storage media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and also those implemented in the form of carrier waves (e.g., transmission over the Internet).

In addition, the computer-readable recording medium can be distributed across network-connected computer systems, so that the computer-readable code can be stored and executed in a distributed manner. And functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention belongs.

As described above, the present specification and drawings have disclosed preferred embodiments of the present invention, and although specific terms have been used, they have been used only in a general sense to easily explain the technical contents of the present invention and to help understand the invention, and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that other modified examples based on the technical idea of the present invention can be implemented in addition to the embodiments disclosed herein.

1: IoT Smart Oven Power Usage Measurement and Retail Store Oven Operation Control System Using Power Usage Patterns
100 : IoT Smart Oven
110 : Camera
120 : Sensor module
121 : Voltage sensor
122 : Current sensor
123 : Temperature sensor
130 : MPU
140 : Communication module
200 : Network
300 : Management Server
310: Transmitter and receiver
320 : Control Unit
321: Information gathering module
322: Analysis module
323: Pattern learning module
324: Information provision module
330 : Database
400 : Smart terminal

Claims (7)

In a retail store oven operation control system (1) using IoT smart oven power usage measurement and power usage pattern including multiple IoT smart ovens (100), a network (200), and a management server (300), the management server (300)
An information collection module (321) that receives oven serial numbers, image information, and sensing information (including voltage, current, power factor, and temperature) from each IoT smart oven (100) operated in a retail store through a network (200) at a predetermined cycle, and then stores the oven serial numbers as metadata and the image information and sensing information as unit collection information in a database (330);
An analysis module (322) that recognizes the operation of an IoT smart oven (100) based on image information and sensing information, and then measures the power usage corresponding to the cooking mode and operating time of the oven based on sensing information that measures the type of bread being cooked in the oven from the image information and the voltage, current, power factor, and temperature of the IoT smart oven (100) while the bread is being cooked; and
It is characterized by including a pattern learning module (323) that learns the power usage pattern of each IoT smart oven (100) over time using an AI model for the unit collection information provided from each IoT smart oven (100) by the information collection module (321).
The above analysis module (322)
After extracting the possible cooking mode according to each analyzed type of bread into a cooking mode list, the target cooking mode currently being cooked is extracted through matching the first sensing parameter change information corresponding to the change in each sensing parameter corresponding to each voltage information, current information, power factor information, and temperature information included in the sensing information, and the second sensing parameter change information, which is the sensing parameter change information for each cooking mode stored in the database (330).
The temperature information is reflected as correction data in the power consumption with a weight according to the operating status of the IoT smart oven (100), and not only is the power consumption calculated by calculating the accumulated power amount over time, but also the smart power meter module is analyzed for errors and failures using power data collection equipment consisting of a voltage sensor (121), a current sensor (122), and a power factor sensor (123) for actual power measurement.
When the temperature information is reflected as correction data to the power consumption as a weight according to the operating state of the IoT smart oven (100), the analysis module (322) assigns weights by performing a multiplication operation on the calculated power amount with a first weight, which is a quantitative numerical value preset as a positive weight, which is a power amount weight in proportion to the amount of change in temperature when the temperature of the IoT smart oven (100) rises, and when the temperature information reaches a state of being stopped at a preset temperature for a preset time, the second weight, which is a quantitative value preset as an inverse weight corresponding to the weight of the power consumption so that the temperature information is maintained, is assigned weights by performing a multiplication operation on the calculated power amount. An IoT smart oven power usage measurement and power usage pattern-based retail oven operation control system.
In the first paragraph, the IoT smart oven (100)
A system for controlling the operation of a retail store oven using an IoT smart oven power usage measurement and power usage pattern, characterized by including a camera (110) that obtains image information inside an oven, formed as a high-temperature camera inside an oven for photographing the inside of an IoT smart oven (100) or formed separately through a housing that can photograph the inside from the outside of the IoT smart oven (100);
In the first paragraph, the IoT smart oven (100)
A retail oven operation control system using an IoT smart oven power usage measurement and power usage pattern, characterized by including a sensor module (120) including a voltage sensor (121), a current sensor (122) and a temperature sensor (123) to generate sensing information corresponding to information including voltage, current, power factor and temperature in an IoT smart oven (100) while bread is being cooked during oven operation.
delete In the first paragraph, the management server (300)
An IoT smart oven power usage measurement and retail store oven operation control system using power usage patterns, characterized by further including an information provision module (324) that preheats an IoT smart oven (100) or provides recommended information for changing the cooking order or cooking time of bread to an IoT smart oven (100) or a smart terminal (400) according to a power usage pattern using an AI model.
The first step is that the management server (300) receives the oven serial number, image information, and sensing information (including voltage, current, power factor, and temperature) from each IoT smart oven (100) operated in a retail store through the network (200) at a predetermined cycle, and then stores the oven serial number as metadata and the image information and sensing information as unit collection information in a database (330); and
A second step in which the management server (300) recognizes the operation of the IoT smart oven (100) based on image information and sensing information, and then measures the power usage corresponding to the cooking mode and operating time of the oven based on sensing information measuring the type of bread being cooked in the oven from the image information and the voltage, current, power factor, and temperature of the IoT smart oven (100) while the bread is being cooked;
It is characterized by including a third step in which the management server (300) learns the power usage pattern of each IoT smart oven (100) over time using an AI model for the unit collection information provided from each IoT smart oven (100).
The second step above is,
A step of extracting a possible cooking mode according to each analyzed type of bread into a cooking mode list, and then extracting a target cooking mode currently being cooked through matching first sensing parameter change information corresponding to a change in each sensing parameter corresponding to each voltage information, current information, power factor information, and temperature information included in the sensing information, and second sensing parameter change information, which is sensing parameter change information for each cooking mode stored in the database (330);
A step of reflecting the temperature information as correction data to the power consumption with a weight according to the operating status of the IoT smart oven (100), calculating the accumulated power amount over time to calculate the power consumption, and analyzing whether there is an error or failure of the smart power meter module using a power data collection device consisting of a voltage sensor (121), a current sensor (122), and a power factor sensor (123) for measuring the actual power amount; and
A method for measuring power usage of an IoT smart oven and controlling operation of a retail store oven using a power usage pattern, comprising: a step of applying weights by performing a multiplication operation on the calculated power amount with a first weight, which is a quantitative numerical value preset as a positive weight, which is a power amount weight in proportion to the amount of change in temperature when the temperature of the IoT smart oven (100) rises, and when the temperature information reaches a state of being stopped at a preset temperature for a preset time, a second weight, which is a quantitative value preset as an inverse weight corresponding to the weight of power consumption so that the temperature information is maintained; delete