KR102025736B1 - Device and method for remotely diagnosing programmable logic controller - Google Patents

Abstract

According to one embodiment of the present invention, provided is a PLC remote diagnosis device, which can perform wireless communication with a PLC device set as a diagnosis target to transmit input data for remote diagnosis to the PLC device, receive output data for the input data from the PLC device, and determine whether the PLC device has a failure based on the output data, or predict a failure time point of the PLC device based on multiple types of output data which are accumulated and stored.

Description

PLC remote diagnostic device and method {DEVICE AND METHOD FOR REMOTELY DIAGNOSING PROGRAMMABLE LOGIC CONTROLLER}

The present invention relates to a PLC remote diagnostic apparatus and method, and more particularly to a power line communication remote diagnostic apparatus and method that can easily diagnose the PLC module without a site visit by remotely diagnosing the PLC.

Programmable Logic Controllers (PLCs) are digital electronic controllers that use programmable memory to control special functions such as logic, sequencing, timing, counting, and computation through digital or analog I / O modules, and control various types of machines or processors. The device is a sequence control device having an input / output unit, a storage unit, an operation control unit, and the like, which performs a similar function with a computer.

PLC is mainly used for industrial control work, and it is used in many fields because of its features such as high reliability, simple control software, ease of maintenance, and low price, and its applications are large, medium and small. It is used for factory automation, monitoring and control of oil pipeline gas pipe, and various sequence control.

When the PLC diagnoses a fault, a person goes out to the field and directly connects a laptop and a loader to the PLC and checks for abnormalities through a test. That is, by going directly to the site and regularly testing, checking whether the PLC module is broken requires a lot of manpower, and there is a problem in that manpower waste, time waste, and cost waste are caused.

Korean Patent Registration No. 10-0823720 Korea Patent Registration No. 10-0549216

One aspect of the present invention provides a PLC remote diagnosis device and method that allows a user to conveniently diagnose whether or not a PLC failure is remote, as well as to reliably predict a failure point of a PLC according to a frequency of degradation of the PLC.

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

The PLC remote diagnosis apparatus according to an embodiment of the present invention performs wireless communication with a PLC device set as a diagnosis target, transmits input data for remote diagnosis to the PLC device, and transmits the input data for the input data from the PLC device. The apparatus may include a measuring unit configured to receive output data and a diagnosis unit configured to determine whether the PLC device is faulty based on the output data, or to predict a failure point of the PLC device based on a plurality of accumulated and stored output data.

The diagnosis unit may determine whether the PLC device is faulty or predict a failure point of the PLC device according to a result of comparing the output data with a preset reference section.

The diagnosis unit may determine that the PLC device is out of order when it is determined that the output data is included in the first reference section, and when it is determined that the output data is included in a second reference section that is lower than the first reference section. It may be determined that the PLC device is deteriorated, and the output data received at the time determined as the deteriorated state may be included in the history data set for predicting the failure point.

The diagnostic unit measures the duration of time that the output data is included in the second reference section, and includes the output data in the history data set when the output data lasts more than a preset reference time in the second reference section. You can.

The diagnosis unit may set the reference time in inverse proportion to the size of the second reference section.

The diagnosis unit may predict a failure point of the PLC device based on a time series variation of a plurality of output data included in the history data set.

The measuring unit may store a plurality of different input data in advance so as to transmit optimized input data for each type of PLC device, and transmit a test signal before transmitting the input data to the PLC device to respond to the test signal. The signal may be analyzed to dynamically set input data to be transmitted to the PLC device among a plurality of prestored input data.

In addition, a PLC remote diagnosis method performed by a PLC remote diagnosis device for remotely diagnosing the PLC device by connecting to a PLC device set as a diagnosis target by wireless communication, according to an embodiment of the present invention, is remote to the PLC device. Transmitting input data for diagnosis, receiving output data for the input data from the PLC apparatus through the communication unit, determining whether the PLC apparatus is faulty based on the output data, or accumulating a plurality of the A failure point of the PLC device can be predicted based on the output data.

Determining whether the PLC device is faulty may determine that the PLC device is faulty when it is determined that the output data is included in a first preset reference section.

Predicting a failure point of the PLC device, when it is determined that the output data is included in the second reference interval lower than the first reference interval, by measuring the duration of the output data included in the second reference interval If it is determined that the duration is greater than or equal to the reference time set according to the length of the second reference interval, the output data is included in the history data set, and the time series variation of the plurality of output data accumulated in the history data set is analyzed. By predicting the failure time of the PLC device.

According to one aspect of the present invention, the user can conveniently diagnose whether or not the PLC failure even remotely, and even if the PLC is not diagnosed as a failure at the time of diagnosis, reliable failure time of the PLC according to the cumulative degradation degree of the PLC It can be predicted.

1 is a conceptual diagram illustrating a schematic configuration of a PLC remote diagnostic system according to an embodiment of the present invention.
2 and 3 are block diagrams showing the specific configuration of the PLC remote diagnostic apparatus of FIG.
4 is a view for explaining a specific process of determining whether or not the PLC device failure using the output data received from the PLC device.
5 and 6 are flow charts showing a schematic flow of a PLC remote diagnostic method according to an embodiment of the present invention.

DETAILED DESCRIPTION The following detailed description of the invention refers to the accompanying drawings that show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different but need not be mutually exclusive. For example, certain shapes, structures, and characteristics described herein may be embodied in other embodiments without departing from the spirit and scope of the invention in connection with one embodiment. In addition, it is to be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. Like reference numerals in the drawings refer to the same or similar functions throughout the several aspects.

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

1 is a conceptual diagram illustrating a schematic configuration of a PLC remote diagnostic system according to an embodiment of the present invention.

The PLC remote diagnosis system 1000 according to an embodiment of the present invention may include a PLC remote diagnosis apparatus 100 and a PLC apparatus 200.

Programmable Logic Controller (PLC) device 200 uses programmable memory to perform special functions such as logic, sequencing, timing, counting, and operation through digital or analog input / output modules and controls various types of machines or processors. As an electronic device for digital operation, a sequence control device having an input / output unit, a storage unit, an operation control unit, and the like that performs a similar function with a computer.

PLC device 200 may be provided in a variety of equipment, for example, can be installed for the automation of plant equipment, in this case, the integrated control of the peripheral devices of the production line to efficiently control the overall control of the conveyor, External loads such as proximity sensors, temperature sensors, solenoid valves and motors can be connected and moved in a certain sequence. In addition, the PLC device 200 enables large-scale factory automation or unmanned factory automation through communication such as a robot, a host computer, and the like with Ethernet.

The PLC remote diagnosis apparatus 100 is a device for remotely diagnosing whether or not the PLC apparatus 200 is malfunctioning.

The PLC remote diagnosis apparatus 100 according to the present invention may be a mobile device or a fixed device, and may be a personal computer (PC), a smartphone, a device, a notebook, an apparatus, The term "terminal" (UE), a user equipment (UE), a mobile station (MS), a wireless device, a handheld device, etc. may be called.

The PLC remote diagnosis apparatus 100 may be preinstalled with software (application) for performing each step of configuring the PLC remote diagnosis method according to the present invention described below. Accordingly, the user may execute the pre-installed software to connect the PLC remote diagnosis apparatus 100 to the PLC apparatus 200 installed at a long distance by wireless communication, and use the PLC remote diagnosis apparatus 100 to execute the PLC apparatus 200. Remote diagnostic services).

To this end, the PLC remote diagnosis apparatus 100 performs wireless communication with the PLC device 200 set as a diagnosis target, transmits input data for remote diagnosis, receives output data thereof, and uses the output data. It is possible to determine whether the PLC device 200 has failed or to predict a failure time. A detailed configuration and function of the PLC remote diagnosis apparatus 100 will be described with reference to FIG. 2.

FIG. 2 is a block diagram illustrating a detailed configuration of the PLC remote diagnosis apparatus 100 of FIG. 1.

In detail, the PLC remote diagnosis apparatus 100 may include a communication unit 110, an input unit 120, an output unit 130, and a controller 140.

The communication unit 110 is a module for performing wired and wireless communication. Specifically, the communication unit 110 communicates with the PLC device 200 through the wireless Internet, and data generated in this process may be transmitted and received through the communication unit 110.

The input unit 120 is a module that detects an input signal from a user who owns the PLC remote diagnosis apparatus 100. The input unit 120 may be in the form of a keyboard, a mouse, a microphone, a touch pad, a joy pad, and the like, and the user may control the control signal for remote diagnosis of the PLC device 200 to the PLC remote diagnosis apparatus 100 through the input unit 120. Can be entered.

The output unit 130 may be in the form of a display module, a speaker, a touch pad, and the like, and may output the remote diagnosis result of the PLC device 200 as visual and audio information.

The controller 140 is a module that controls the overall operation of the PLC remote diagnostic apparatus 100. For example, the controller 140 controls the specific PLC device 200 selected by the user through the input unit 120 to be connected to the PLC remote diagnosis apparatus 100 through the communication unit 110, and the diagnosis result of the PLC device 200. May be controlled to be output through the output unit 130.

In particular, the controller 140 may determine whether the PLC device 200 is malfunctioning or predict a malfunction time of the PLC device 200 based on the output data received from the PLC device 200. In this regard, it will be described with reference to FIG.

3 is a block diagram illustrating a specific configuration of the controller 140 of FIG. 2.

In detail, the controller 140 of the PLC remote diagnosis apparatus 100 may include a measurement unit 141 and a diagnosis unit 142.

The measurement unit 141 may transmit and receive data for diagnosis by performing wireless communication with the PLC device 200 set as a diagnosis target through the communication unit 110. That is, the measurement unit 141 transmits input data for remote diagnosis to the PLC device 200 through the communication unit 110, and then outputs the output data thereof from the PLC device 200 which has performed an operation according to the input data. Can be received.

In this case, since the PLC remote diagnosis apparatus 100 according to the present invention diagnoses various types of PLC apparatus 200 remotely as described above, it is necessary to transmit input data suitable for various types of PLC apparatus 200 characteristics. have. To this end, the measurement unit 141 may store a plurality of different types of input data in advance so as to transmit input data optimized for each type of the PLC device 200.

In this case, the measurement unit 141 may transmit a test signal for setting input data to be transmitted to the PLC device 200 set as a diagnosis target among the plurality of input data. The measurement unit 141 may analyze the response signal received from the PLC device 200 performing the operation according to the test signal, and select which kind of input data is to be transmitted to the PLC device 200. For example, the measurement unit 141 analyzes the frequency characteristics of the response signal and compares the analyzed frequency characteristics with previously stored characteristic data to estimate which PLC device 200 generated the pattern of the response signal according to the test signal. can do.

The diagnosis unit 142 may determine the state of the PLC device 200 based on the output data received by the measuring unit 141. That is, the diagnostic unit 142 analyzes the received output data to determine whether the PLC device 200 is in a malfunction state (failure state) in which it cannot perform a normal operation, or whether the PLC device 200 is in a degraded state in which a part of the output performance is degraded but not in a malfunction state. It may be determined whether the device 200 is in a normal state.

To this end, the diagnosis unit 142 calculates the similarity by comparing the output data with the preset reference data, and determines whether there is a malfunction at the present time of the PLC device 200 according to the result of comparing the similarity with the preset reference section. Or predict a malfunction time of the PLC device 200. In this regard, it will be described with reference to FIG.

4 is a view for explaining a specific process of determining whether or not the PLC device failure using the output data received from the PLC device.

The diagnosis unit 142 may compare the similarity calculated in the above process with a preset reference section to determine whether the PLC device 200 is malfunctioning at the present time or to predict the malfunctioning time of the PLC device 200.

The diagnosis unit 142 may classify the PLC device 200 into any one of a normal state, a degraded state, and a malfunction state according to the magnitude of the similarity calculated as shown.

In detail, the diagnosis unit 142 may determine that the PLC device 200 is in a malfunction state when it is determined that the similarity is included in the first reference section that is set as the threshold section having a relatively high similarity. That is, when the difference between the reference data set as data generated when the normal function is performed on the input data and the output data which is the actual output result is large, the corresponding PLC device 200 is malfunctioning. It can be judged that.

And, if it is determined that the calculated similarity is not included in the first reference section, but is included in the second reference section that is set to a threshold section of a smaller size than the first reference section, the PLC device 200 It may be determined whether the degradation state of the.

In other words, the diagnosis unit 142 does not necessarily determine that the PLC device 200 is in a deteriorated state because the similarity is included in the second reference section, but finally determines whether the deterioration state is performed after a predetermined determination process. You can judge.

When the diagnosis unit 142 determines that the similarity is included in the second reference section outside the normal range, the diagnosis unit 142 determines that the PLC device 200 is in a deteriorated state and includes the output data received at the determined time point in the history data set. You can.

In this case, the diagnosis unit 142 may not include the same in the history data set because the similarity is temporarily out of the normal range, and may include the same in the history data set only when the similarity is continuously out of the normal range for a predetermined time or more.

Even if the similarity is out of the normal range, if the duration lasts for a relatively short time period, there may be a case where only a slight or negligible effect is applied to the PLC device 200 itself. Therefore, the diagnosis unit 142 may set the reference time and determine that the similarity persists for more than the reference time as a signal indicated by a caution signal or a malfunction signal. The reference time may be determined corresponding to the reference section. Specifically, the diagnosis unit 142 may set the size of the reference time relatively long when the reference section is a very narrow range. For example, when the size of the second reference section is set to 4 to 6, the similarity showing a small error from the normal data (for example, 3) by setting the reference time to 10 minutes persists in the second reference section for a relatively long time. It can be judged as a deterioration signal. On the contrary, when the second reference section is set to a section of 4 to 10, the diagnosis unit 142 may set the length of the reference time to about 5 seconds and may immediately react when the similarity rapidly changes.

The diagnosis unit 142 may determine that the PLC device 200 is in a deteriorated state when the similarity calculated as described above lasts more than a reference time in the second reference section. In this case, the diagnosis unit 142 may accumulate and store the output data collected during the time interval determined as the deterioration state in the history data set. As described above, the length of the reference time may be set in inverse proportion to the length of the threshold section set as the reference section instead of the fixed time.

Thereafter, the diagnosis unit 142 may analyze a plurality of output data accumulated in the history data set to predict a malfunction time of the PLC device 200.

The diagnosis unit 142 may grasp the current situation of the PLC device 200 and further calculate a next expected attention or malfunction signal based on the data included in the history data set. In the present embodiment, the time series data may be analyzed and predicted using an auto regressive conditional heteroscedasticity (ARCH) model.

The ARCH model is characterized not only by estimating trends and mean regressions but also by estimating variance to analyze past data and to have future predictive power. The ARCH model, introduced by Engle, which models the time-series characteristics of conditional variance, has become a widely used analytical tool in that the model is simple and reflects the empirical characteristics of the financial time series.

Therefore, in the present embodiment, an ARCH model may be used to model the frequency of appearance of data included in the historical data set. Equation 3 is an equation that describes the current time series in a time series of electricity.

[Equation 3]

Where y _t is the mean of the time series, a ₀ , a ₁ ? Is an unknown parameter, and ε _t is the residual. That _{is,? Y t-1, y} t-2 that represent the normal to the history of data out over the reference time, the difference value as compared to the original y _tq shown y _t. Equation expressing the residual is as follows.

[Equation 4]

Where σ is conditional dispersion. σ _t is assumed to be known in period t-1, and the random variable z _t follows a normal distribution with a mean of zero and a constant variance. The process for calculating σ is as follows.

[Equation 5]

,

, a₀, a₁ ?는 미지의 모수, σ는 조건부 분산이다. 전술한 바에 따라 정상 범위를 기준시간 이상 벗어난 데이터가 포함된 히스토리 데이터셋을 기초로 예상 오류 시점을 예측할 수 있다.here

,

, a ₀ , a ₁ ? are unknown parameters, and σ is a conditional variance. As described above, the predicted error point may be predicted based on a history data set including data that deviates from the normal range by more than the reference time.

As described above, the diagnostic unit 142 measures the time for which the similarity continues when the calculated similarity is out of the normal range, and periodically calculates the similarity, and the preset time for which the similarity is continuously out of the normal range is set in advance. If the signal persists for more than a time, an alarm signal is generated and included in the history data set, and a malfunction time point of the substation is predicted based on the generation interval of the alarm signal, and specifically, a history data set is generated based on the accumulated alarm signal. In addition, a malfunction time point of the PLC device 200 may be predicted based on a time series variation of the alarm signal included in the history data set.

5 and 6 are flow charts showing a schematic flow of a PLC remote diagnostic method according to an embodiment of the present invention.

The PLC remote diagnosis method according to the present invention may be performed by the above-described PLC remote diagnosis apparatus 100. For this purpose, the PLC remote diagnosis apparatus 100 according to the present invention configures the PLC remote diagnosis method described below. Software (application) for performing the above may be preinstalled. Accordingly, when the user executes the software (application) preinstalled in the PLC remote diagnosis apparatus 100, the user can easily diagnose the PLC apparatus 200 remotely according to the PLC remote diagnosis method.

First, the PLC remote diagnosis apparatus 100 may transmit input data to the PLC apparatus 200 set as a measurement target (51). To this end, the PLC remote diagnostic apparatus 100 is connected to the PLC device 200 set as a measurement target through wireless communication, and after connecting, transmits a test signal to analyze a response signal thereto to measure a PLC of the plurality of input data. Input data suitable for the device 200 may be selected.

Next, the PLC remote diagnosis apparatus 100 may receive output data according to input data from the PLC apparatus 200 (52), and calculate the similarity by comparing the received output data with preset reference data (53). ).

When the PLC remote diagnosis apparatus 100 determines that the calculated similarity is included in the first reference section (YES of 54a), that is, when the difference between the output data and the reference data is relatively large, the PLC device 200 outputs the normal output. In operation 55a, it may be determined that there is a malfunctioning state in which data cannot be generated.

On the other hand, when the PLC remote diagnosis apparatus 100 determines that the calculated similarity is included in the second reference section having the critical section smaller than the first reference section (YES of 54b), the PLC remote diagnosis apparatus 100 determines whether the PLC device 200 is in a deteriorated state. Subsequent procedures for determining can be performed (A).

In detail, as illustrated in FIG. 6, the PLC remote diagnosis apparatus 100 may measure a duration in which the calculated similarity is continuously included in the second reference section (61). As described above, since the output data is periodically received at predetermined time intervals, the PLC remote diagnostic apparatus 100 can measure the duration of the similarity calculated at each predetermined time interval continuously included in the second reference interval. Do.

If it is determined that the duration included in the second reference section exceeds the reference time, the PLC remote diagnosis apparatus 100 may determine that the PLC device 200 is in a degraded state (62). The PLC remote diagnosis apparatus 100 may accumulate and store the output data collected during the time interval determined as the deterioration state in the history data set (63). Based on this, the PLC remote diagnosis apparatus 100 may predict a malfunction time point of the PLC apparatus 200 based on a clock change trend of the output data included in the history data set (64). Since a detailed description of each step has been described above with reference to FIG. 4, repeated descriptions thereof will be omitted.

Referring back to FIG. 5, if the PLC remote diagnosis apparatus 100 determines that the calculated similarity is not included in the second reference section (NO in 54b), the PLC remote diagnosis apparatus 100 determines that the corresponding output data is normal output data. 200 may be diagnosed as a normal state (55b).

Subsequently, the PLC remote diagnosis apparatus 100 may display the determined result (normal state, deteriorated state, or malfunction state) on a display provided to allow the user to recognize the remote diagnosis result. At the same time, when the PLC device 200 is in the normal state and the degraded state except the malfunction state, the PLC remote diagnosis apparatus 100 may display the information on the predicted time of malfunction of the PLC apparatus 200 with reference to the history dataset. It may be.

Such a technique for providing a PLC remote diagnostic method may be implemented in the form of program instructions that may be implemented by an application or executed through various computer components, and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.

The program instructions recorded on the computer-readable recording medium are those specially designed and configured for the present invention, and may be known and available to those skilled in the computer software arts.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical recording media such as CD-ROMs, DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.

Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the process according to the invention, and vice versa.

Although the above has been described with reference to the embodiments, those skilled in the art will understand that various modifications and changes can be made without departing from the spirit and scope of the invention as set forth in the claims below. Could be.

100: PLC remote diagnostic device
200: PLC device

Claims (10)

A measurement unit performing wireless communication with a PLC device set as a diagnosis target, transmitting input data for remote diagnosis to the PLC device, and receiving output data for the input data from the PLC device; And
A diagnosis unit for determining whether the PLC device is faulty based on the output data, or predicting a failure point of the PLC device based on a plurality of accumulated and stored output data;
The measuring unit transmits a test signal for setting input data to be transmitted to a PLC device set as a diagnosis target among a plurality of input data, and analyzes frequency characteristics of a response signal received from the PLC device performing an operation according to the test signal. By comparing the analyzed frequency characteristic with the pre-stored characteristic data, it is confirmed by which PLC device the pattern of the response signal according to the test signal is generated, and what kind of input data is transmitted to the identified PLC device,
The diagnosis unit determines any one of a malfunction state, a deterioration state, or a normal state of the PLC device based on output data,
The diagnostic unit calculates the similarity by comparing the output data with the preset reference data, and predicts the failure or malfunction time according to the result of comparing the similarity with the preset reference interval,
If it is determined that the similarity is included in the first reference section in which the similarity is set as the critical section, the diagnosis unit determines that the PLC device is in a malfunction state, and the similarity is not included in the first reference section but smaller than the first reference section. When it is confirmed that the second reference section is set to the threshold section of the whether or not the degradation state of the PLC device, the PLC device is deteriorated only when the similarity lasts more than a predetermined reference time in the second reference section. Determining that the status is in a state, and including the received output data in a history data set to predict a time when the PLC device passes from a deterioration state to a malfunctioning time point.
delete delete delete The method of claim 1, wherein the diagnostic unit,
And setting the reference time in inverse proportion to the size of the second reference section.
The method of claim 1, wherein the diagnostic unit,
A PLC remote diagnosis apparatus for predicting a failure time of the PLC device based on a time series variation of a plurality of output data included in the history data set.
delete Claim 8 has been abandoned upon payment of a set-up fee. In the PLC remote diagnostic method performed by a PLC remote diagnostic device for remotely diagnosing the PLC device by connecting to a PLC device set as a diagnosis target by wireless communication,
The PLC remote diagnosis apparatus transmits a test signal for setting input data to be transmitted to a PLC device set as a diagnosis target among a plurality of input data, and frequency characteristics of a response signal received from a PLC device which has performed an operation according to the test signal. Analyze and compare the analyzed frequency characteristics with pre-stored characteristic data to determine which PLC device generated the response signal pattern according to the test signal, and select what kind of input data to send to the identified PLC device. ,
Transmits input data for remote diagnosis to the PLC device,
Receiving output data for the input data from the PLC device,
Determining one of a malfunction state, a degraded state, or a normal state of the PLC device based on the output data;
Similarity is calculated by comparing the output data with preset reference data, and predicting a failure or malfunction time according to a result of comparing the similarity with a preset reference interval,
If it is determined that the PLC remote diagnosis apparatus is included in the first reference section in which the similarity is set as the critical section, it is determined that the PLC device is in a malfunction state, and the similarity is not included in the first reference section but is included in the first reference section. If it is determined that the PLC is included in a second reference section that is set to a smaller critical section, it is determined whether the PLC device is in a deteriorated state, and the PLC is only when the similarity persists for more than a preset reference time in the second reference section. Determine that the device is in a deteriorated state, and include the received output data in a history data set to predict when the PLC device passes from the deteriorated state to a malfunctioning point,
The PLC remote diagnosis method for determining whether or not the failure of the PLC device based on the output data, or predicting the failure time of the PLC device based on the plurality of accumulated stored output data.

delete delete

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