WO2021235594A1 - Emergency generator remote monitoring system - Google Patents

Abstract

The present invention relates to an emergency generator remote monitoring system. The present invention relates to an emergency generator remote monitoring system (1) comprising an embedded board (100), a server (200), and a client (300), wherein the embedded board (100), the server (200), and the client (300) are connected via a network, and the embedded board (100) performs an access to the network wirelessly via an AP, wherein the emergency generator remote monitoring system (1) comprises: a server program (200a) which, when the embedded board (100) receives data from an emergency generator (10) via RS485 communication in which the data is provided with, as data types, a battery voltage (default), a cooling water temperature (default), an oil temperature (default), oil pressure (default), a power factor (default), a heater temperature (option), power (option), and whether to operate (option), and then provides same to the server (200) via the network, not only stores the data obtained from the emergency generator (10) in a database (200b), but also processes the data in a manner of diagnosing a malfunction of the emergency generator (100a) and predicting the malfunction in advance by integrating the data stored in the database (200b) to generate big data, thereby generating a pattern of the data, and applying the generated pattern to self-learning machine learning, and is installed in the server (200); and a client program (300a) installed in the client (300) and providing, to a user, the data in the database (200b) and processed data after accessing the database (200b) via the network.

Description

Emergency Generator Remote Monitoring System

The present invention relates to a remote monitoring system for an emergency generator, and more specifically, for systematic management, attaching an ICT-based sensor to a generator, which is a core part of an emergency generator, for constant monitoring of an ICT-based emergency generator, and real-time collection of usage data; In addition to supporting analysis and monitoring, it is possible to not only diagnose and predict failures of emergency generators in advance, but also to accumulate data stored in the database and make them into big data to create patterns of data and apply machine learning that learns them by itself. It relates to an emergency generator remote monitoring system for

An emergency generator is a power generation device and facility for supplying power to important facilities or facilities that require power supply when the regular power (power supplied through the general power grid) cannot be supplied due to a disaster, accident, or breakdown in a normal state. Diesel engine type generators are most commonly used.

Emergency power sources including emergency generators are [Act on Fire Prevention, Installation/Maintenance and Safety Management of Firefighting Facilities] (Article 9, Paragraph 1), the same Act [Enforcement Decree] and the National Fire Service [Fire safety standards for indoor fire hydrant facilities (NFSC 102)] ( Article 8), it is mandatory to install (a building with 7 or more floors and a total area of 2000 m2 or more, a specific firefighting object with a total floor area of 3,000 m2 or more). In addition, the [Electricity Business Act] and [Building Act] also stipulate the mandatory provisions for installing a ‘reserve power supply’.

Meanwhile, 83,865 emergency generators (total capacity: 27,347MW, equivalent to 20 nuclear power plants) have been installed and are in operation (as of 2017). There has been a problem with the poor emergency generator management system in the absence of management regulations other than the recommendation to conduct a test run for at least 30 minutes under no load once a week for function maintenance and inspection.

The present invention is to solve the above problems, and the traditional function of an emergency generator was to supply emergency power in case of a power outage, etc., but its function is varied according to the recent government's DR: Demand Response (DR) policy promotion and expansion. This is to provide an emergency generator remote monitoring system to enable remote monitoring of each emergency generator by remotely accessing the data on the emergency generator into big data and accessing it from the client.

In addition, in the present invention, in the management of the emergency generator, the manager performs no-load operation within 5 minutes or so once or twice a month and checks the degree of basic matters, so for systematic management, regular monitoring of the ICT-based emergency generator is required This is to provide an emergency generator remote monitoring system to support the attachment of ICT-based sensors to the generator, which is a key component of the emergency generator, and real-time collection, analysis, and monitoring of usage data.

In addition, the present invention can apply machine learning to create a pattern of data by accumulating data stored in a database and turning it into big data, and machine learning to learn it by itself. To provide a remote monitoring system.

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

In order to achieve the above object, the emergency generator remote monitoring system according to an embodiment of the present invention is composed of an embedded board 100 , a server 200 , and a client 300 , and an embedded board 100 , a server 200 . , the client 300 is connected through a network, and the embedded board 100 is an emergency generator remote monitoring system 1 that performs access to the network via an AP via wireless, the embedded board 100 performs RS485 communication Data is received from the emergency generator 10 through As the power (option) and operation status (option) are provided to the server 200 through the network after being provided, the data obtained from the emergency generator 10 is converted into a DB in the database 200b and stored, as well as the database ( 200b) by accumulating the data stored in the big data to make a pattern of data, and applying machine learning to learn by itself about the generated pattern, as well as diagnosing the failure of the emergency generator 100, and predicting the failure in advance. A server program (200a) installed on the server (200) to process; and a client program (300a) installed in the client (300) and providing data and processing data on the database (200b) to the user after access to the database (200b) through the network; It may be characterized in that it comprises a.

At this time, since the server program 200a is a method of accumulating data in the database 200b, any user of the client 300 registered in the server 200 can check the data in an N:N manner rather than a 1:1 method. , at the same time, it may be characterized in that the data and previous data of the N units of the emergency generator 10, data according to the installation environment of the emergency generator 10 are accumulated, compared and analyzed with each other, and patterned.

In addition, the embedded board 100 integrates vibration, power, and temperature in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud-type network. can be characterized.

In addition, the client program 300a, as the main monitoring UI screen, enables monitoring of the entire state data of the emergency generator 10, and may be characterized in that the data that can be checked according to the user's authority is differently limited. .

In addition, the server 200 applies the verification standards of field experts in securing the optimal operating characteristics and proceeds with machine learning using the "data set" using the emergency generator 10 capacity-specific and load-specific tests. Based on the data obtained during the shipment test stage of the emergency generator 10 can

The emergency generator remote monitoring system according to an embodiment of the present invention is to supply emergency power in case of an emergency such as a power outage as a traditional function of the emergency generator, but according to the recent government power demand management (DR: Demand Response) policy promotion and expansion, its function As this is being diversified, it provides the effect of enabling remote monitoring of each emergency generator by remotely accessing the data on the emergency generator into big data and accessing it from the client.

In addition, in the emergency generator remote monitoring system according to another embodiment of the present invention, in the emergency generator management, the manager performs a no-load operation within 5 minutes per 1-2 times a month and checks the degree of basic matters. For systematic management, it provides the effect of attaching ICT-based sensors to the generator, which is a core part of emergency generators, for constant monitoring of ICT-based emergency generators, and supporting real-time collection, analysis, and monitoring of usage data.

In addition, the emergency generator remote monitoring system according to another embodiment of the present invention can apply machine learning to create a pattern of data by accumulating data stored in the database and turning it into big data, and machine learning can be applied to diagnose the failure of the emergency generator. In addition, it provides the effect of being able to predict failures in advance.

1 is a view showing an emergency generator remote monitoring system 1 according to an embodiment of the present invention.

2 is a hardware configuration diagram of the embedded board 100 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

3 is a view showing a UI screen of the MySQL Workbench program used as a server program 200a among the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

4 is a diagram illustrating a UI screen when the server program 200a of the emergency generator remote monitoring system 1 is driven according to an embodiment of the present invention.

5 to 10 are diagrams illustrating a UI screen provided by the client program 300a of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

11 is a view showing an expanded form of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

12 is a diagram for explaining the systemization of the big data analysis process for the emergency generator 10 on the database 200b by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention .

13 is a diagram showing data science methodology planning and design by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

14 is a diagram illustrating a procedure for performing data analysis of the emergency generator 10 by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

15 is a view showing prediction verification visualization by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention, and the RNN and LSTM configuration diagrams and RNN data analysis structure.

16 is a view for explaining the data processing function by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed description of preferred embodiments of the present invention will be described with reference to the accompanying 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 thereof will be omitted.

In the present specification, when one component 'transmits' data or signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component This means that data or signals can be transmitted to other components.

1 is a view showing an emergency generator remote monitoring system 1 according to an embodiment of the present invention. 2 is a hardware configuration diagram of the embedded board 100 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.

First, referring to FIG. 1 , the emergency generator remote monitoring system 1 includes an embedded board 100 , a server program 200a and a database 200b installed in the server 200 , and a client program 300a installed in the client 300 . ) can be composed of Here, the embedded board 100 , the server 200 , and the client 300 are connected through a network, and in particular, the embedded board 100 can be accessed through the network via an AP via wireless.

Here, the network is a high-speed backbone network of a large-scale communication network capable of large-capacity and long-distance voice and data services, and may be a next-generation wired or wireless network for providing the Internet or high-speed multimedia services. If the network device is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of the asynchronous mobile communication network, there may be a wideband code division multiple access (WCDMA) type communication network. In this case, although not shown in the drawings, the network may include a Radio Network Controller (RNC). Meanwhile, although the WCDMA network is taken as an example, it may be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks.

Meanwhile, referring to Figure 2, the embedded board 100 performs DC 24V power input and DC 5V, DC 3.3V generation, ADC 4 channels (voltage and current type selectable), digital input 2 channels, digital relay output 2 channels, 2 channels of digital transistor output, RS485 communication can be performed.

The embedded board 100 receives data from the IoT sensor attached to the emergency generator 10 through RS485 communication, and the data types include battery voltage (basic), coolant temperature (basic), oil temperature (basic), oil pressure ( Basic), power factor (default), heater temperature (option), power (option), and operation status (option) can be provided.

The server program 200a installed in the server 200 uses MySQL Server, which is an open source database management system, and may be replaced with a local database in some cases. Referring to FIG. 3 , a UI screen of the MySQL Workbench program used as the server program 200a is shown.

On the other hand, the function of the database 200b is to receive various information (data) obtained from the emergency generator 10 by the server 200 and store it in a DB, so that it is processed into various types of data required by the user. to be able to provide it.

Data obtained through the server program 200a of the server 200 is stored in the database 200b, and data is provided to the user through the client program 300a installed in the client 300 .

In the design of the database 200b in the present invention, the database 200b is designed to store various data of user registration and emergency generator. The data of the emergency generator 10 stored in the database 200b is shown in Table 1 below. Other additional data can be adjusted in consultation with the user.

day name	Contents
Bat Vol	battery voltage
Cool Temp	coolant temperature
Oil Temp	oil temperature
Oil Pressure	oil pressure
Heater Temp	heater temperature
Power	power
Power Factor	power factor
Status	Operation
Loc	Installation location information

The server program 200a is responsible for periodically acquiring data from the embedded board 100 and storing it in the database 200b, and may be manufactured to output a UI screen as shown in FIG. 4 when driven.

When the server 200 is driven, it is automatically executed together with the database 200b to prepare to receive data from the embedded board 100 connected to the emergency generator 10 .

The server 200 is a TCP/IP (or UDP) Server, and transmits and receives data with the embedded board 100 through TCP/IP (or UDP) communication, and the communication protocol uses a dedicated protocol to enhance complementarity. The server 200 stores the motorized data in the database 200b, manages the database 200b, and automatically starts the server program 200a when driven, as well as through an input device connected to the server 200 The user may manually start and end the operation of the server 200 by clicking the start/end button as shown in FIG. 4 .

The client program 300a installed in the client 300 may monitor data of all emergency generators 10 currently registered in the server 200 wherever a network such as the Internet is connected. The client program 300a may bring necessary data while communicating directly with the database 200b and provide it to the user.

The UI and functions of the client program 300a are as follows.

That is, FIG. 5 shows the Login UI screen, and the client program 300a is executed only when the registered user logs in normally on the client 300 .

Next, Figure 6 is a main monitoring UI screen, so that the entire state data of the emergency generator 10 can be monitored. Depending on the user's authority, the data that can be checked may be restricted differently.

7 is an individual generator monitoring UI screen so that data of the individual emergency generator 10 can be monitored. Data can be checked through selection on the main monitoring UI screen for the individual emergency generator 10 that the user wants to check.

8 is an emergency generator status UI screen in which the registered emergency generator status outputs the client 300 when the installation status menu is selected on the main monitoring UI screen, and the emergency generator currently registered in the database 200b by the server 200 (10) can be displayed on the screen.

9 is an alarm information UI screen showing that alarm information of an installed emergency generator is displayed on the client 300 when the alarm log menu is selected on the main monitoring UI screen. 10 is an administrator-only screen that is a user registration UI screen, and only an administrator can register a user.

On the other hand, the server 200 can apply machine learning to create a pattern of data by accumulating the data stored in the database 200b and turning it into big data, so that the machine learns it by itself. However, failures can be predicted in advance.

To this end, the server 200 analyzes the collected data distributed and stored for each category such as each region and use in the divided section DB in which the database 200b is divided by the distributed file program installed in the database 200b through a machine learning algorithm and malfunctions. prediction can be made. More specifically, the machine learning algorithm used in the server program 200a of the server 200 may be one of a decision tree (DT) classification algorithm, a random forest classification algorithm, and a support vector machine (SVM) classification algorithm. .

The server program 200a analyzes the collected data distributed and stored in the divided section DB by the distributed file program, extracts a plurality of feature data as a result of the analysis, and uses at least one or more of the extracted feature data among a plurality of machine learning algorithms Thus, it is possible to determine whether there is an abnormal state as a result of learning.

That is, the server program 200a may apply an ensemble structure composed of a plurality of complementary machine learning algorithms to improve the accuracy of the state determination result.

The decision tree classification algorithm is a method of deriving results by learning in a tree structure, which makes it easy to interpret and understand the results, and the data processing speed is fast, and it may be possible to derive rules based on the search tree. RF can be applied as a method to improve the low classification accuracy of DT. The random forest classification algorithm is a method of slaughtering the results of learning multiple DTs as an ensemble. SVM can be applied as a method to improve overfitting that may occur through DT or RF learning. The SVM classification algorithm classifies data belonging to different classifications on a plane-based basis, and generally has high accuracy and may have low sensitivity to structural overfitting.

In addition, since it is a method of accumulating data in the database 200b, any user registered in the N:N method, not the 1:1 method, can check the data, and at the same time the data and previous data of the N emergency generators 10, and Data according to the installation environment of the emergency generator 10 can be accumulated, compared/analyzed with each other, and patterned.

In the data part collected by the server program 200a, not only the engine, but also the body part, the panel part, etc. are integrated to receive data. The patterned data can be visualized and compared/analyzed for optimal values.

More specifically, the embedded board 100 receives vibration, power, temperature, etc. in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud network. have.

Here, the server 200 is a cloud server, divided application of data storage for analysis of vibration data, raw data edge computer storage (Data estimating: 50 GB/day), and cloud storage of Max 20 Parameter Data (Data estimating: 25) ~50MB/day).

On the other hand, the embedded board 100 receives data as IoT-based data for the emergency generator 10, but in the case of IoT data for data integration and transmission for securing reliability of IoT-based data, it is a large amount of data generated every second. In order to reduce the data loss rate, rather than transmitting data the moment it is generated, data collected from various sensors is integrated to reduce the loss rate through Flow Control and Error Control during cloud transmission, and to improve the security of data transmitted through the network. In order to strengthen the data, it is possible to secure data stability by applying an encryption technique using a public key.

To this end, the embedded board 100 can maintain security through secondary encryption to strengthen the security of data, that is, the embedded board 100 includes category data (each division area, use, etc.) of the emergency generator 10 . ), after receiving the unique identification number of the emergency generator 10, binarizing the category data and the unique identification number to perform a series of plaintext block data conversion, and then performing secondary encryption using a preset public key, an encryption key. . That is, after performing one-stage encryption to generate an intermediate constant by applying block encryption to the plaintext block information by the encryption key, a plurality of plaintext blocks for the generated intermediate constant and each individual data (category information and unique identification number) Complementation can be strengthened by performing an exclusive OR on each to generate a second encrypted ciphertext block.

Here, when receiving the unique identification number of the emergency generator 10 in addition to the category data to decrypt the public key with the embedded board 100 for the secondary encrypted data, the server 200 decrypts the data in the reverse order of encryption. It can be used to perform access to

In addition, the server program 200a of the server 200 may "perform a multidimensional data analysis algorithm and real-time monitoring visualization" using data received through the embedded board 100 .

More specifically, the server 200 analyzes the gap between the issue and the improvement goal according to the problem for each analysis requirement to support the integrated management of the emergency generator 10, and based on the quality standard of the emergency generator 10 As a result, it is possible to determine the critical value for a problem by applying the verification standards of field experts and continuously analyze the difference from the target value to satisfy it.

In addition, the server program 200a of the server 200 is an issue according to the problem through comparative analysis of various factors affecting the quality standard of the emergency generator 10, time series analysis to ensure the continuity of the stable state, etc. In order to secure the optimal state of the generator through correlation and comparative analysis of the collected data between each emergency generator 10 collected on a cloud basis, and to support the integrated management of the emergency generator 10, the emergency generator (10) Although it is important to monitor the status of one unit in real time, it is possible to perform an analysis for securing unexpected insights by integrating and analyzing the status of various emergency generators 10 .

In addition, the server 200 collects and analyzes data of the emergency generator 10 in various states to secure a factor value for the optimal state of the emergency generator 10, and analyzes the IoT sensing emergency generator 10 state data in real time. Cube model / star schema that is easy for various analysis by applying multidimensional big data analysis techniques based on , unstructured / structured, sensor data, off-line cheek sheet data, machine learning preprocessing and standardization, and securing decision-making requirements and related data In order to store data in the form of data in the database 200b, and to store and process data centered on structured data, online and multi-dimensional analysis techniques may be applied first.

In addition, the server 200 applies a data science-based analysis methodology for systematization of the data analysis process, establishes a data science method analysis execution procedure {planning → design → analysis → results (feedback)}, data science method planning and Design {APAA (Acquire → Prepare → Analyze → Act)} can be performed.

Meanwhile, FIG. 12 is a diagram for explaining the systemization of a big data analysis process for the emergency generator 10 on the database 200b by the server 200 .

First, looking at "data science-based analysis methodology", the server program 200a of the server 200 introduces and utilizes the concept of an agile methodology as an analysis methodology of data science. Agile methodology is carried out focusing on the quality of the analysis model rather than project execution and output, and the analysis model is completed through continuous consultation with the customer, so it corresponds to an efficient analysis model suitable for the characteristics of the customer's business.

Next, looking at the "data science methodology analysis execution procedure", the server program 200a of the server 200 applies the data science methodology when an analysis task is selected as the analysis procedure of the data analysis methodology to plan → design → analysis → result It is possible to perform a pilot analysis in a total of four steps, complete the analysis model through continuous feedback, and provide architectural design support that is resilient to change through business architecture, application architecture, and technology architecture as architecture-oriented development support.

Here, the server program 200a of the server 200 utilizes the framework, which is a commercialized software architecture, because it is activated, and provides an application modeling technique based on the MVC architecture, as well as data science methodology planning and design. can be done like

On the other hand, looking at the step-by-step procedure and details of the data analysis methodology by the server program 200a of the server 200, Booz Allen Hamilton's data science process is applied, and the data is based on the iterative execution technique of the agile methodology. The analysis is performed in the order of collection → purification → analysis → utilization.

Table 2 below is a table showing the data science methodology step-by-step procedure and details.

Meanwhile, the following FIG. 14 is a diagram illustrating a procedure for performing data analysis of the emergency generator 10 by the server program 200a of the server 200 .

The server program 200a of the server 200 provides a state diagnosis and prediction algorithm of the emergency generator 10 based on sensor data in the analysis.

More specifically, the server program 200a of the server 200 performs a machine vibration data / fault learning technique / emergency generator 10 self-state diagnosis interlocking test, and includes experts in each field to plan the entire project (Work Breakdown Structure) ) and apply the verification criteria of field experts to determine the critical value for the problem.

Then, in the server program 200a of the server 200, in providing an algorithm through correlation analysis of collected data, the results obtained through machine learning are organized into TP, FP, FN, and TN error matrix (confusion matrix) In the case of a machine learning algorithm that categorizes categorical data for the purpose, accuracy, precision, recall, and general tracking and monitoring target measurements are made, and a model is created with a dataset and test data using the emergency generator 10 status information data. By increasing the set, the accuracy of the algorithm can be increased and it can be applied to evaluation.

Accordingly, the server program 200a of the server 200 performs verification and feedback of the machine learning algorithm through the emergency generator 10 state information and sensor data-based diagnosis/prediction algorithm through machine learning, and as a result, the following table It performs the same function as 3.

On the other hand, the server program 200a of the server 200 uses the "data set" using the emergency generator 10 capacity-specific and load-specific tests to proceed with machine learning and secure optimal driving characteristics, verification standards of field experts Based on the data obtained in the shipment test stage of the host company by applying can do.

In addition, the server program 200a of the server 200 provides an algorithm for optimal state and predictive maintenance by using GCP data and sensor data for the emergency generator 10, and the emergency generator 10 GCP data and It provides an algorithm for optimal state and predictive maintenance using sensor data, and develops a machine learning module through RNN and LSTM analysis, and uses this to develop a diagnosis/prediction algorithm based on emergency generator (10) status information and sensor data. can provide

That is, FIG. 15A shows a prediction validation visualization (an example), and FIG. 15B is a diagram showing an RNN and LSTM configuration diagram and an RNN data analysis structure.

On the other hand, the server program 200a of the server 200 has a built-in statistical processing function to establish an automatic baseline. More specifically, the server program 200a of the server 200 may calculate the average value and standard deviation of the input signal to generate a baseline, and evaluate the data by applying several signal processing values for each parameter.

To this end, the server program 200a of the server 200 may provide a hardware pre-process for big data analysis as well as provide a data processing function once per second as shown in FIG. 16 .

As a result, the server program (200a) of the server 200 according to the present invention is an integrated sensor in the emergency generator 10, Off-Line (LCD displayed on the energy controller) information, a monitoring module that collects history management information, the collected A state analysis module that analyzes the state of the emergency generator in real time using state information and a communication module that transmits the analyzed state of the target device to an external terminal, wherein the state analysis module is an IoT installed in the emergency generator 10 A state prediction module for predicting the state of the emergency generator by using the vibration data and thermal data collected from the equipment and predicting the failure time of the emergency generator 10 using at least one data among various data collected from the equipment It may include a failure prediction module that

In addition, the server program 200a of the server 200 compares the collected data with each reference pattern data, and when there is data that deviates by more than a preset value from the reference pattern, the emergency generator 10 state is abnormal. It can be predicted that

In addition, the server program 200a of the server 200 receives the information of the device and the energy controller collecting the state data of the emergency generator 10 every preset period, and the engine GCP Data and vibration/temperature data of the body. Each of the reference pattern data is corrected by matching, and thereafter, the quality of the product can be predicted using the corrected reference pattern data, and the failure time of the equipment can be predicted.

Here, the monitoring module and the status analysis module may further include a backup server provided in the cloud-based server and backing up the status information collected by the monitoring unit and the status of the equipment analyzed by the status analysis module at a preset period.

The server 200 collects and analyzes facility information using an integrated sensor provided in the facility, so that the state of a plurality of facilities located in remote locations can be integrated and monitored, and data such as vibration and temperature collected from the facility are used to collect and analyze facility information. It is possible to predict the state and failure time of the emergency generator 10 so that the maintenance of the device can be made.

The present invention can also be implemented as computer-readable codes on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data readable by a computer system is stored.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. also includes

In addition, the computer-readable recording medium is distributed in a computer system connected through a network, so that the computer-readable code can be stored and executed in a distributed manner. In addition, 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 pertains.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms are used, these are only used in a general sense to easily explain the technical contents of the present invention and to help the understanding of the present invention. , it is not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

The emergency generator remote monitoring system according to an embodiment of the present invention is composed of an embedded board 100 , a server 200 , and a client 300 , and the embedded board 100 , the server 200 , and the client 300 are network In the emergency generator remote monitoring system 1 that performs access to the network via the AP via wireless, the embedded board 100 is connected to the embedded board 100 from the emergency generator 10 through RS485 communication. Receives data, and data types include battery voltage (default), coolant temperature (default), oil temperature (default), oil pressure (default), power factor (default), heater temperature (option), power (option), operation status As the (optional) is provided to the server 200 through the network after being provided, the data obtained from the emergency generator 10 is converted into a DB in the database 200b and stored, and the data stored in the database 200b is integrated. The data is processed in a way that not only diagnoses the failure of the emergency generator 100, but also predicts the failure in advance by applying machine learning to create a pattern of data by turning it into big data, and applying machine learning to learn by itself about the generated pattern, and the server 200 The program for the server installed in (200a); and a client program (300a) installed in the client (300) and providing data and processing data on the database (200b) to the user after access to the database (200b) through the network; It may be characterized in that it comprises a.

At this time, since the server program 200a is a method of accumulating data in the database 200b, any user of the client 300 registered in the server 200 can check the data in an N:N manner rather than a 1:1 method. , at the same time, it may be characterized in that the data and previous data of the N units of the emergency generator 10, data according to the installation environment of the emergency generator 10 are accumulated, compared and analyzed with each other, and patterned.

In addition, the embedded board 100 integrates vibration, power, and temperature in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud-type network. can be characterized.

In addition, the client program 300a, as the main monitoring UI screen, enables monitoring of the entire state data of the emergency generator 10, and may be characterized in that the data that can be checked according to the user's authority is differently limited. .

In addition, the server 200 applies the verification standards of field experts in securing the optimal operating characteristics and proceeds with machine learning using the "data set" using the emergency generator 10 capacity-specific and load-specific tests. Based on the data obtained during the shipment test stage of the emergency generator 10 can

The emergency generator remote monitoring system according to an embodiment of the present invention makes it possible to remotely monitor each emergency generator by remotely accessing it from a client by converting data on the emergency generator into big data. For monitoring, it is expected to be widely used in industry by attaching ICT-based sensors to the generator, a key component of emergency generators, and supporting real-time collection, analysis, and monitoring of usage data.

Claims (5)

1. It consists of an embedded board 100, a server 200, and a client 300, and the embedded board 100, the server 200, and the client 300 are connected through a network, and the embedded board 100 is wirelessly In the emergency generator remote monitoring system (1) for performing access to the network via the AP,

   The embedded board 100 receives data from the emergency generator 10 through RS485 communication, and the data types include battery voltage (basic), coolant temperature (basic), oil temperature (basic), oil pressure (basic), power factor ( Basic), heater temperature (option), power (option), and operation status (option) are provided and then provided to the server 200 through the network, so that the data obtained from the emergency generator 10 is stored in the database 200b. Not only is it stored as a DB, but also the data stored in the database 200b is integrated into big data to create a pattern of data, and machine learning to learn the generated pattern by the machine itself is applied to diagnose the failure as well as failure of the emergency generator 100 A server program (200a) installed in the server (200) and processing data in a way to predict in advance; and

   a client program (300a) installed in the client (300) and providing data and processing data on the database (200b) to the user after access to the database (200b) through the network; Emergency generator remote monitoring system comprising a.
2. The method according to claim 1, The server program (200a),

   Because it is a method of accumulating data in the database 200b, any user of the client 300 registered in the server 200 can check the data in an N:N manner, and at the same time the data and previous data of the N emergency generators 10 , Emergency generator remote monitoring system, characterized in that by accumulating data according to the installation environment of the emergency generator (10), comparing and analyzing with each other and patterning.
3. The method according to claim 1, Embedded board 100,

   Emergency generator remote monitoring system, characterized in that after integrating vibration, power, and temperature in addition to emergency generator data (GCP Data) as collected data from the emergency generator 10, and transmitting it to the server 200 through a cloud-type network.
4. The method according to claim 1, The client program (300a),

   As the main monitoring UI screen, it is possible to monitor the entire state data of the emergency generator 10, and the emergency generator remote monitoring system, characterized in that the data that can be checked according to the user's authority is differently limited.
5. The method according to claim 1, The server 200,

   In the process of machine learning and securing operation characteristics using "data set" using tests for each capacity and load of the emergency generator 10, the data obtained in the shipment test stage of the host company by applying the verification standards of field experts Emergency generator remote monitoring system, characterized in that it determines the threshold for problems by capacity and load based on the emergency generator 10, and continuously secures a "data set" for machine learning and ensures operation characteristics.

Images