KR20230047833A - System for sensing fire of electrical installations - Google Patents

Abstract

A fire detection system for electrical equipment is disclosed. The present invention relates to a fire diagnosis system for electrical equipment, which may include: an IoT sensing unit which detects electromagnetic waves of a predetermined wavelength generated from the electrical equipment; a thermal imaging camera unit which detects temperature changes in the electrical equipment; and a computing device which receives detected data from the sensing unit and the camera unit and generates arc occurrence information and fire occurrence information based on the detected data.

Description

Fire detection system of electrical installations {SYSTEM FOR SENSING FIRE OF ELECTRICAL INSTALLATIONS}

The present invention relates to a fire detection system for electrical installations. More specifically, it relates to a system for detecting whether an arc and a fire occur in an electrical installation by utilizing a non-contact sensor.

In general, measurement fire detection data such as arc, carbon dioxide, humidity, temperature, and vibration detected in industrial electrical facilities such as ESS, electrical rooms, switchboards, motor control panels, distribution panels, solar junction boxes, and transformers are very important fire management points. .

In particular, in power facilities such as photovoltaic power plants, thermal power plants, substations, and factories, fire detection data for arc, carbon dioxide, humidity, and temperature measurement are elements that must be measured and are very important fire detection variables that are managed. It is a fire prevention management element that is recognized as important in the system.

The existing safety management system for electrical facilities subject to management conducts manual inspections once or twice a year that rely on manpower through safety diagnosis experts, and has limitations in determining the state of gas detectors and sprinklers with the naked eye, resulting in unexpected fires. It was difficult to properly implement fire safety management for exceptional circumstances.

In addition, since the existing fire detection system detects flames and smoke generated at the time of fire and takes measures, it is possible to detect a fire only when a fire occurs, so there is a problem in that it is impossible to take an initial response before ignition occurs.

In addition, in the related art, in order to collect the above fire detection data for a specific place, there is a high risk of malfunction of a circuit breaker-type arc detection device, and when the circuit breaker operates due to a malfunction, the entire power is cut off, resulting in increased damage.

In addition, in the prior art, a fire was detected through a thermal imaging camera directly used by a manager, but this has a problem in that it is difficult to detect an instantaneous arc.

In particular, the conventional arc detection device has a problem in that it cuts off a normal operating circuit due to sensitivity control limitations and cannot distinguish electrical signals generated from lightning, laser printers, vacuum cleaners, washing machines, and the like.

Registered Patent No. 10-2119039 (Title of Invention: Combination fire detection device based on arc detection)

An object of the present invention is to provide a system for diagnosing whether an arc and a fire occur in electrical equipment by utilizing a non-contact sensor.

In order to solve the above problems, the present invention is a fire diagnosis system for electrical equipment, an IoT sensing unit for detecting electromagnetic waves of a predetermined wavelength generated from the electrical equipment, and a thermal imaging camera for detecting a temperature change of the electrical equipment. and a computing device that receives data detected by the sensing unit and the camera unit, and generates arc generation information and fire occurrence information based on the detected data.

In addition, the IoT sensing unit may communicate with the computing device using wireless communication.

The thermal imaging camera unit may include a housing including a through hole on a side wall, a lens installed in the through hole, and a driving unit driving the lens, wherein the through hole may have a size corresponding to a diameter of the lens. there is.

The system may further include a current sensor configured to sense a change in current of the electrical equipment, and the computing device may generate the fire occurrence information based on the sensed data and the change in current.

Also, the computing device may generate the arc generation information based on a thermal image generated by the thermal imaging camera.

Also, the computing device may receive a thermal image from the thermal imaging camera and learn the thermal image using a CNN learning algorithm.

In addition, the electromagnetic wave of the predetermined wavelength may be at least one electromagnetic wave of UV-C (ultraviolet-C) or IR (infrared ray).

The present invention has the effect of providing a system for diagnosing whether an arc occurs and whether a fire occurs in an electrical facility by utilizing a non-contact sensor.

In addition, the present invention utilizes a lens including a unique coating layer, so that even when a thermal imaging camera is located near a fire, it is less affected by high temperature and can generate accurate images.

In addition, the present invention has an effect of generating a more accurate learning result by learning an image using a unique filter, and through this, accurately detecting whether an arc or a fire has occurred.

The effects obtainable according to the present invention are not limited to the effects mentioned above, and other effects not mentioned are clearly understood by those skilled in the art from the outer surface of the protection unit below. It could be.

1 shows a fire detection system for electrical installations according to the present invention.
2 illustrates a computing device in accordance with the present invention.
3 shows the functional configuration of a memory according to the present invention.
4 schematically illustrates a camera module included in a thermal imaging camera according to the present invention.
5 and 6 show fire detection according to the present invention.
7 is a diagram showing an example of an image correction filter according to the present invention.
8 is a diagram showing CIE 1931 color coordinates according to the present invention.
The accompanying drawings, which are included as part of the detailed description to aid understanding of the present specification, provide examples of the present specification and describe technical features of the present specification together with the detailed description.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted.

In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted.

In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various components, but the components are not limited by the terms. These terms are only used for the purpose of distinguishing one component from another.

Singular expressions include plural expressions unless the context clearly dictates otherwise.

In this application, terms such as "comprise" or "having" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof external to the protection unit in the specification, but one or It should be understood that it does not preclude the possibility of the presence or addition of other features, numbers, steps, operations, components, parts, or combinations thereof beyond that.

Hereinafter, based on the above information, a fire detection system for electrical equipment according to a preferred embodiment of the present specification will be described in detail as follows.

1 shows a fire detection system for electrical installations according to the present invention.

According to FIG. 1 , a fire detection system for electrical equipment according to the present invention may include an IoT sensing unit 210, a thermal imaging camera unit 220, a current detection unit 230, and a computing device 100.

The IoT sensing unit 210 may include a communication function capable of transmitting and receiving data to and from a computer device such as a server. The IoT sensing unit 210 may be configured to detect electromagnetic waves of a predetermined wavelength generated from electrical equipment. In this case, the electromagnetic wave of a predetermined wavelength may be at least one of UV-C (ultraviolet-C) and IR (infrared ray).

The thermal imaging camera unit 220 may include a thermal image module therein to generate thermal image data. The thermal imaging camera unit 220 may detect temperature changes of electrical equipment through infrared light. Thermal image data generated by the thermal imaging camera unit 220 may be transmitted to the computing device 100 or the server.

The current detection unit 230 may be connected to an element to detect whether or not a fire occurs among electrical equipment and sense a change in current. The current sensor 230 may be a conventional ammeter. The current sensing unit 230 may detect a current value per time.

The computing device 100 according to the present invention may correspond to a user terminal, a laptop computer, a PDA, a tablet, a smart phone, an external server, a cloud server, and the like. The computing device 100 according to the present invention may be connected to the IoT sensing unit 210, the thermal imaging camera unit 220, and the current sensing unit 230 in a wired/wireless manner to transmit and receive data. The computing device 100 according to the present invention may learn the received data and generate arc occurrence information and fire occurrence information based on the learning result value. The arc generation information may be information on whether an arc has occurred in the electric facility before a fire occurs, and may further include information on a duration when an arc continuously occurs. The fire occurrence information may be information about whether a fire has occurred beyond the arc.

2 illustrates a computing device in accordance with the present invention.

According to FIG. 2 , a computing device 100 according to the present invention may include a processor 110 , a memory 120 and a communication module 130 .

The processor 110 may execute commands stored in the memory 120 to control other components. The processor 110 may execute instructions stored in the memory 120 .

The processor 110 is a component capable of performing calculations and controlling other devices. Mainly, it may mean a central processing unit (CPU), an application processor (AP), a graphics processing unit (GPU), and the like. Also, the CPU, AP, or GPU may include one or more cores therein, and the CPU, AP, or GPU may operate using an operating voltage and a clock signal. However, while a CPU or AP consists of a few cores optimized for serial processing, a GPU may consist of thousands of smaller and more efficient cores designed for parallel processing.

The processor 110 may provide or process appropriate information or functions to a user by processing signals, data, information, etc. input or output through the components described above or by running an application program stored in the memory 120.

The memory 120 stores data supporting various functions of the computing device 100 . The memory 120 may store a plurality of application programs (application programs or applications) running in the computing device 100 , data for operation of the computing device 100 , and instructions. At least some of these application programs may be downloaded from an external server through wireless communication. Also, the application program may be stored in the memory 120, installed in the computing device 100, and driven by the processor 110 to perform an operation (or function) of the computing device 100.

The memory 120 may be a flash memory type, a hard disk type, a solid state disk type, a silicon disk drive type, or a multimedia card micro type. ), card-type memory (eg SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read -only memory), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. Also, the memory 120 may include a web storage performing a storage function on the Internet.

The communication module 130 transmits and receives information with a base station or a camera having a communication function through an antenna. The communication module 130 may include a modulation unit, a demodulation unit, a signal processing unit, and the like. Also, the communication module 130 may perform a wired communication function.

Wireless communication may refer to communication using a wireless communication network using a communication facility previously installed by telecommunication companies and a frequency of the communication facility. At this time, the communication module 130 is CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple access), and the like, as well as the communication module 130 can also be used for 3rd generation partnership project (3GPP) long term evolution (LTE). In addition, not only 5G communication, which is currently being commercialized, but also 6G, which is scheduled to be commercialized later, can be used. However, the present specification may utilize a pre-installed communication network without being bound by such a wireless communication method.

In addition, as a short range communication technology, Bluetooth, BLE (Bluetooth Low Energy), Beacon, RFID (Radio Frequency Identification), NFC (Near Field Communication), infrared communication (Infrared Data Association; IrDA) ), UWB (Ultra Wideband), ZigBee, etc. may be used.

3 shows the functional configuration of a memory according to the present invention.

According to FIG. 3 , the functional configuration of the memory 120 according to the present invention may include an arc detection module 121 , a fire detection module 122 and a warning message generating module 122 .

The arc generation detection module 121 may detect an arc based on whether electromagnetic waves in the UV-C or infrared region are detected as greater than a predetermined first intensity. This is because the arc emits a large amount of electromagnetic waves in the UV-C or infrared region when it is generated. The arc generation detection module 121 may receive an intensity value of an electromagnetic wave in a UV-C or infrared region generated at a desired point from the IoT sensing unit 210 . The arc generation detection module 121 may define that an arc has occurred in the corresponding electrical equipment and generate arc generation information when electromagnetic waves in the UV-C or infrared region are generated with a greater than a predetermined first intensity.

In addition, the arc generation detection module 121 detects the duration of the arc generation through the intensity of the electromagnetic wave in the UV-C or infrared region at the first time point and the intensity of the electromagnetic wave in the UV-C or infrared region at the second time point. can do. The arc occurrence detection module 121 may calculate the difference between the first time point and the second time point to detect the duration of the arc.

Also, the arc generation detection module 121 may derive a current change value from the current detection unit 230 and reconfirm whether an arc has occurred through the current change value. That is, the arc generation detection module 121 according to the present invention may define that an arc is generated only when a current change of a predetermined magnitude or more is detected after detecting an electromagnetic wave in an infrared region of a predetermined first intensity or higher. When an arc is generated, the amount of change in current may instantaneously be larger than a predetermined size.

The fire detection module 122 may operate when arc generation information is received from the arc generation detection module 121 . The fire occurrence detection module 122 may detect temperature information of a desired point among electrical equipment through the thermal imaging camera unit 220 . The fire detection device may receive thermal image data from the thermal imaging camera unit 220 and apply a vision recognition algorithm to the thermal image data.

For example, if only an arc is instantaneously generated and does not lead to a fire, the arc detection module 121 may operate and transmit data to the warning message generation module 122, but since the fire does not occur, the fire detection module 122 It is not necessary to transmit data to the warning message generating module 122 through this operation. Accordingly, the fire occurrence detection module 122 according to the present invention may distinguish between an arc and whether a fire has occurred through a vision recognition algorithm. The vision recognition algorithm according to the present invention may include a CNN learning algorithm, and the CNN learning algorithm may use an image filter to be described later.

The CNN learning algorithm according to the present invention can learn thermal image data in which an arc occurs and thermal image data in which a fire occurs from an arc as input values. Thermal image data in which an arc occurs and thermal image data in which a fire occurs due to an arc may be previously labeled and input.

In addition, in the fire detection module 122, when a fire occurs, the amount of current change instantaneously increases, and then the electric wire or the like is disconnected due to the fire, so that the current value becomes 0A. Accordingly, the fire detection module 122 may recognize whether a fire has occurred through vision recognition and then check whether a fire has occurred through sensing a change in current. At this time, the fire occurrence detection module 122 may determine whether a fire occurs by detecting whether the current change amount is greater than a predetermined size and then changes to 0A. If it is confirmed whether a fire has occurred, the fire detection module 122 may generate fire occurrence information.

When arc generation information is received from the arc generation detection module 121, the warning message generation module 122 may generate a first message for warning that an arc has occurred. Also, when receiving fire information from the fire detection module 122, the warning message generation module 122 may generate a second message for warning that a fire has occurred. The first message and/or the second message may be transmitted to a user terminal or the like and deliver a warning to an administrator or the like. In addition, the second message may be transmitted to a fire suppression device (not shown) to automatically suppress the fire.

4 schematically illustrates a camera module included in a thermal imaging camera according to the present invention.

According to FIG. 4 , the camera module 150 may generate thermal image data. The camera module 150 may include a housing 1324 including a through hole on a side wall, a lens 1321 installed in the through hole, and a driving unit 1323 that drives the lens 1321 . The through hole may have a size corresponding to the diameter of the lens 1321 . The lens 1321 may be inserted into the through hole.

The lens driving unit 1323 may be configured to control the lens 1321 to move forward or backward. The lens 1321 and the lens driving unit 1323 are connected in a conventionally known manner, and the lens 1321 may be controlled by the lens driving unit 1323 in a conventionally known manner.

In order to obtain various images, the lens 1321 needs to be exposed to the outside of the camera module 150 or the housing 1324 .

In particular, the camera 132 according to the present invention must be located outside the housing 1324 because it must directly photograph the subject. Therefore, a lens with strong fouling resistance is required. The present invention proposes a coating layer for coating a lens to solve this problem.

Preferably, the lens 1321 may include a fluorine-based compound represented by Formula 1 below on its surface; It may be coated with a coating composition containing an organic solvent, inorganic particles and a dispersant.

[Formula 1]

Here, n is an integer from 1 to 100.

When the lens 1321 is coated with the coating composition, it can exhibit excellent water repellency and stain resistance, so even if the lens 1321 installed outside the vehicle is exposed to a polluted environment for a long time, images or videos that can be used as road information are collected. can do. The compound is characterized in that it has excellent heat resistance and does not undergo deformation due to heat.

The inorganic particles may be selected from the group consisting of silica, alumina, and mixtures thereof. The average diameter of the inorganic particles is 70 to 100 μm, but is not limited to the above examples. After forming the coating layer 1322 on the surface of the lens 1321, the inorganic particles may improve physical strength and maintain viscosity within a certain range to increase moldability.

The organic solvent is selected from the group consisting of methyl ethyl ketone (MEK), toluene, and mixtures thereof, and preferably methyl ethyl ketone may be used, but is not limited to the above examples.

As the dispersing agent, a polyester-based dispersing agent may be used, and specifically, TEGO-Disperse 670 (manufacturer : EVONIK) can be used, but it is not limited to the above examples, and all dispersants obvious to those skilled in the art can be used without limitation.

The coating composition may further include a stabilizer as other additives, and the stabilizer may include a UV absorber, an antioxidant, and the like, but is not limited to the above examples and may be used without limitation.

For forming the coating layer 1322, the coating composition more specifically includes a fluorine-based compound represented by Chemical Formula 1; organic solvents, inorganic particles and dispersants.

The coating composition may include 40 to 60 parts by weight of the fluorine-based compound represented by Chemical Formula 1, 20 to 40 parts by weight of inorganic particles, and 5 to 15 parts by weight of a dispersant, based on 100 parts by weight of the organic solvent. In the case of the above range, a synergistic effect is expressed to the extent that the water repellency effect due to the interaction of each component is of critical significance, and when it is out of the above range, the synergistic effect is rapidly reduced or almost nonexistent.

More preferably, the viscosity of the coating composition is 1500 to 1800 cP, and when the viscosity is less than 1500 cP, when applied to the surface of the lens 1321, there is a problem that it is not easy to form the coating layer 1322 because it flows down, and the coating composition exceeds 1800 cP. In this case, there is a problem in that it is not easy to form a uniform coating layer 1322.

[Preparation Example 1: Preparation of coating layer]

1. Preparation of coating composition

A coating composition was prepared by mixing methyl ethyl ketone with a fluorine-based compound represented by Formula 1, inorganic particles, and a dispersant:

[Formula 1]

Here, n is an integer from 1 to 100.

A more specific composition of the antistatic composition is shown in Table 1 below.

TX1 TX2 TX3 TX4 TX5 organic solvent 100 100 100 100 100 acrylic compound 30 40 50 60 70 inorganic particles 10 20 30 40 50 dispersant One 5 10 15 20

(unit weight parts)

2. Preparation of coating layer

A coating layer 1322 was formed by applying the coating composition of DX1 to DX5 on one surface of the lens 1321 and then curing the coating composition.

[Experimental Example 1]

1. Evaluation of surface appearance

Due to the difference in viscosity of the coating composition, after the coating layer 1322 was prepared, a sensory evaluation was performed on whether a uniform surface was formed. Evaluation was conducted on whether or not a uniform coating layer 1322 was formed, and the evaluation was conducted according to the following criteria.

○: uniform coating layer formation

×: Formation of non-uniform coating layer

TX1 TX2 TX3 TX4 TX5 sensory evaluation Х ○ ○ ○ Х

When forming the coating layer 1322, if the viscosity is less than a certain amount, flow occurs on the surface of the lens 1321, and it is difficult to form a uniform coating layer 1322 after the curing process in many cases. Accordingly, a problem of lowering the production yield may occur. In addition, even when the viscosity is too high, it is difficult to uniformly apply the composition, and it is impossible to form a uniform coating layer 1322.

2. Measurement of water repellency angle

After forming the coating layer 1322 on the surface of the lens 1321, the results of measuring the water repellency angle are shown in Table 3 below.

)advancing contact angle (

) static contact angle (

) receding contact angle (

) TX1 117.1±2.9 112.1±4.1 < 10 TX2 132.4±1.5 131.5±2.7 141.7±3.4 TX3 138.9±3.0 138.9±2.7 139.8±3.7 TX4 136.9±2.0 135.6±2.6 140.4±3.4 TX5 116.9±0.7 115.4±3.0 < 10

As shown in Table 3, after the coating layer 1322 was formed using the coating compositions of TX1 to TX5, the result of measuring the contact angle was confirmed. TX1 and TX5 measured receding contact angles less than 10 degrees. That is, it was confirmed that a phenomenon in which water droplets are pinned occurs when the coating composition is out of the optimal range for preparing the coating composition. On the other hand, it was confirmed that the pinning phenomenon did not occur in TX2 to 4, indicating that excellent waterproofing effect could be exhibited.

3. Fouling resistance evaluation

A lens 1321 having a coating layer 1322 according to the above embodiment formed outside the facility was attached to a model camera and exposed to the external environment for 40 days. As the comparative example (Con), the same lens 1321 without the coating layer 1322 was used, and the model camera in each example was installed in the same real-life external environment conditions.

After that, the degree of contamination of the lens 1321 before and after the experiment was evaluated, and for objective comparison, the result was compared with the comparative example in which the coating layer 1322 was not formed, and the result was evaluated by an index of 1 to 10, and Table 4 below shown in In the index below, the lower the number, the better the stain resistance.

Con TX1 TX2 TX3 TX4 TX5 stain resistance 10 7 3 3 3 8

(Unit: Index)

Referring to Table 4, when the coating layer 1322 is formed on the lens 1321, even if the lens 1321 is exposed to the outside while installing the camera in an external environment, it is easy to analyze for a long period of time based on high contamination resistance It can be seen that image data can be collected in the form of In particular, in the case of TX2 to TX4, it can be confirmed that the contamination resistance by the coating layer 1322 is very excellent.

5 and 6 show fire detection according to the present invention.

According to FIG. 5 , a process in which a fire occurs in an electrical facility is confirmed. In electrical equipment, it can be divided into a total of four stages: an arc generation stage, an arc continuous generation stage, a spark generation stage by an initial arc, and a fire generation stage.

According to FIG. 6 , the present invention may further include a temperature/humidity detection sensor for detecting temperature/humidity and a smoke detection sensor for detecting smoke. According to the present invention, the computing device 100 may operate the smoke sensor when temperature/humidity equal to or greater than a predetermined level is detected by the temperature/humidity sensor. The computing device 100 may define that a fire has occurred when temperature/humidity equal to or greater than a predetermined level is detected and smoke generation is detected.

However, the computing device 100 may compare the first result detected by the fire detection module 122 of FIG. 3 with the second result detected by the temperature/humidity sensor and the smoke sensor. The computing device 100 may generate fire occurrence information when at least one of the first result and the second result indicates that a fire has occurred. The computing device 100 may not generate fire occurrence information when both the first result and the second result indicate that no fire occurs.

7 is a diagram showing an example of an image correction filter according to the present invention, and FIG. 8 is a diagram showing CIE 1931 color coordinates according to the present invention.

According to Figure 7, the type and function of the filter is shown. That is, the CNN algorithm may be a learning algorithm using a plurality of layers. In addition, the CNN algorithm can automatically learn a filter that maximizes image classification accuracy, and by adding a new layer called a convolutional layer and a polling layer before the fully connected layer, after applying the filtering technique to the original image, the filtered image A classification operation can be performed on . The CNN algorithm is configured to apply a filtering technique to the original image by adding a new layer, called a convolutional layer and a pooling layer, before the fully-connected layer, and then perform a classification operation on the filtered image. can

At this time, the operation expression for the image filter using the CNN algorithm is as shown in Equation 1 below.

[Equation 1]

(step,

G _IJ : pixel of the i-th row, j-th column of the filtered image expressed as a matrix,

F: filter,

X: image,

F _H : height of filter (number of rows),

F _W : The width of the filter (number of columns). )

Preferably, the operation expression for the image filter using the CNN algorithm is as shown in Equation 2 below.

[Equation 2]

(step,

G _IJ : pixel of the i-th row, j-th column of the filtered image expressed as a matrix,

F': application filter

X: image,

_F'H : height of application filter (number of rows),

F' _W : The width (number of columns) of the application filter.)

Preferably, F' may be an application filter applied to the image data in order to learn and recognize the thermal image data. In particular, since the occurrence of arcs and fires can be classified according to type based on the difference in shape, size, and location displayed on the thermal image due to light or temperature changes, application filters for effectively recognizing the shape, size, and location. may be needed To meet this need, the application filter F' can be calculated by Equation 3 below.

[Equation 3]

: 계수, F' : 응용 필터)(However, F: filter,

: Coefficient, F' : Application filter)

In this case, the filter according to each F may be a matrix of any one of an edge detection filter, a sharpen filter, and a box blur filter according to FIG. 7 .

를 구하는 연산식은 아래의 수학식 4와 같다. 이때,

는 필터의 효율을 높이기 위하여 사용되는 하나의 변수로서 해석될 수 있으며, 그 단위는 무시될 수 있다. Preferably,

The operation expression for obtaining is as shown in Equation 4 below. At this time,

can be interpreted as a variable used to increase the efficiency of the filter, and its unit can be ignored.

[Equation 4]

를 활용한 응용 필터 F'를 통하여, 보다 정확한 인식이 가능하다. However, the diameter (diameter) of the lens of the camera used for image capture is in mm, and the ultrasonic generation period is in ms. Also, the f value of the lens may mean an F number. In addition, the RGB coordinate distance may mean a distance between the substituted coordinate and the origin (0, 0) by substituting color information of a unit dot appearing in the thermal image data into the RGB coordinate. In this case, the RGB coordinates may be CIE 1931 color coordinates according to FIG. 8 . Therefore, the maximum RGB coordinate distance in Equation 4 may mean the maximum distance among the distances between the unit dot and the origin shown in the thermal image data, and the minimum RGB coordinate distance among the distances between the unit dot and the origin shown in the thermal image data. may mean a minimum distance. That is, Equation 4 according to the present invention emphasizes the deviation between colors.

More accurate recognition is possible through the application filter F' using .

[Experimental Example 2]

It was examined whether the difference between the arc and the fire displayed by the thermal image data generated when the application filter F' of the present invention is applied to the image data according to the present invention is accurately determined. The table below shows the accuracy of the recognition result as a numerical value, depending on whether a filter is applied or not, requested by a person in the relevant technical field.

no filter applied Apply filter F Filter F' applied accuracy (score) 75 90 99

Table 5 shows the scores for accuracy evaluated by experts for each case. This experimental example was tested based on thermal image data for 120 different arcs and fires, and the accuracy of information on actual arcs and fires was measured out of 100 points.

As can be seen in Table 5, when the filter is not applied, there is a probability of error in recognizing arc and fire, and it is evaluated with relatively low accuracy. In contrast, the accuracy was slightly higher when the general CNN filter F was applied, but it was confirmed that the accuracy was remarkably improved when the applied filter F' was applied.

The above-described present invention can be modeled as a computer readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. , and also includes those modeled in the form of carrier waves (eg, transmission over the Internet). Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of this specification should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of this specification are included in the scope of this specification.

Any or other embodiments of the present invention described above are not mutually exclusive or distinct. Certain or other embodiments of the present invention described above may be used in combination or combination of respective configurations or functions.

The above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

100: computing device
210: IoT sensing unit
220: thermal imaging camera unit
230: current sensing unit

Claims (7)

In the fire diagnosis system of electrical equipment,
IoT sensing unit for detecting electromagnetic waves of a predetermined wavelength generated from the electrical equipment;
a thermal imaging camera unit detecting a temperature change of the electrical equipment; and
A computing device that receives data detected from the sensing unit and the camera unit and generates arc generation information and fire occurrence information based on the detected data;
Fire detection system in electrical installations.
According to claim 1,
The IoT sensing unit,
communicating with the computing device using wireless communication;
Fire detection system in electrical installations.
According to claim 1,
The thermal imaging camera unit,
A housing including a through hole in a side wall;
a lens installed in the through hole; and
Including; driving unit for driving the lens,
The through hole is formed in a size corresponding to the diameter of the lens,
Fire detection system in electrical installations.
According to claim 1,
The system,
It further includes; a current sensor for sensing a change in current of the electrical equipment;
The computing device,
Generating the fire occurrence information based on the detected data and the current change,
Fire detection system in electrical installations.
According to claim 1,
The computing device,
Generating the arc generation information based on the thermal image generated by the thermal imaging camera,
Fire detection system in electrical installations.
According to claim 1,
The computing device,
receiving a thermal image from the thermal imaging camera and learning the thermal image using a CNN learning algorithm;
Fire detection system in electrical installations.
According to claim 1,
The electromagnetic wave of the predetermined wavelength,
At least one electromagnetic wave of UV-C (ultraviolet-C) or IR (infrared ray),
Fire detection system in electrical installations.

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