KR20220108980A - Crane collision prevention system with sensor and analysis method of the sensor data - Google Patents

Abstract

An analysis method of sensor data provided in a crane by a computing device of the present invention includes steps of: (a) receiving sensor data measured by a plurality of sensors and deleting data determined to be abnormal among the sensor data; (b) converting two-dimensional sensor data after step (a) into three-dimensional sensor data based on a reference coordinate system; (c) deleting sensor data located outside a set region of interest; and (d) calculating a distance between the sensor data located inside the region of interest after deleting the sensor data located outside the region of interest and a boom of the crane. Even in an environment where multiple cranes work at the same time, collision of the crane can be prevented by predicting a risk of collision near the boom area of the crane.

Description

CRANE COLLISION PREVENTION SYSTEM WITH SENSOR AND ANALYSIS METHOD OF THE SENSOR DATA

The present invention relates to a crane collision avoidance system having a sensor and a method for analyzing the sensor data.

Cranes, such as jib cranes, which are frequently used in industrial sites such as shipbuilding, have a huge size, and it is difficult to grasp the collision risk factors near the boom area of the crane from the driver's seat.

In particular, when multiple cranes are working at the same time, each crane cannot predict the operation of the other cranes in detail. It is difficult to judge the possibilities.

Domestic Patent Registration No. 10-2127620: Crane Collision Prevention Device and Method (Registered on June 23, 2020).

The present invention is an invention aimed at solving the technical problems as described above, and even in an environment in which a plurality of cranes work simultaneously, by predicting the collision risk factors near the boom area of the crane, the collision of the crane can be prevented. An object of the present invention is to provide a crane collision avoidance system having a sensor capable of being capable of performing an analysis and an analysis method of the sensor data.

A method for analyzing sensor data provided in a crane by a computing device of the present invention includes the steps of: (a) receiving sensor data measured by a plurality of sensors, and deleting data determined to be abnormal among the sensor data; (b) converting the two-dimensional sensor data after step (a) into three-dimensional sensor data using a reference coordinate system; (c) deleting sensor data located outside the set ROI; and (d) calculating a distance between the sensor data located inside the region of interest after deleting the sensor data located outside the region of interest and the boom of the crane.

Specifically, the first sensor and the second sensor among the plurality of sensors are provided on the upper and front surfaces of the boom of the crane, respectively, and the third sensor and the fourth sensor among the plurality of sensors are on the left side of the boom of the crane It is characterized in that it is provided on the side and the right side, respectively.

In addition, the sensor data analysis method of the present invention, in the step (a), the object determined to have moved at a speed greater than or equal to a first speed among the sensor data; and an object determined to be less than or equal to the first size among the sensor data; it is preferable that the abnormal data be deleted by determining the abnormal data.

In addition, in the sensor data analysis method of the present invention, in the step (d), for each sensor data located inside the region of interest, a first straight line extended by connecting the start point and the end point of the boom of the crane When the sensor data is projected at a right angle toward Calculated by the distance between the boom of the crane, and when the first point is located on the boom of the crane, the distance between the first point and the sensor data is the sensor data and the boom of the crane Calculated as a distance between It is characterized in that it is calculated as a distance of .

In addition, in the sensor data analysis method of the present invention, after step (d), (e) when the sensor data is located in the preset first critical region to the N-th critical region, the threshold of the corresponding sensor data The method may further include outputting an alarm signal corresponding to the area information, wherein N is a natural number of 1 or more.

In addition, in the sensor data analysis method of the present invention, after step (d), (f) if there is a plurality of adjacent sensor data among the sensor data located inside the region of interest, the adjacent plurality of sensors It characterized in that it further comprises; combining the data into one representative data. Specifically, in the sensor data analysis method of the present invention, in the step (f), when there is a plurality of adjacent sensor data among the sensor data located inside the region of interest, the crane among the plurality of sensor data It is preferable to select the sensor data having the closest distance to the boom as the representative data.

According to the crane collision prevention system and the sensor data analysis method of the present invention, the collision of the crane can be prevented by predicting the collision risk factor near the boom area of the crane even in an environment in which a large number of cranes work simultaneously. .

1 is a block diagram of a crane collision prevention system according to a preferred embodiment of the present invention.
Fig. 2 is an explanatory view of measurement areas of a first sensor and a second sensor;
Fig. 3 is an explanatory diagram of a measurement area of a third sensor and a fourth sensor;
4 is a flowchart of a method for analyzing sensor data by a computing device of the present invention.
5 is a first explanatory diagram for a method of calculating a distance between the boom of the crane and the corresponding sensor data.
6 is a flowchart of a method for calculating the distance between the boom of the crane and the corresponding sensor data.
7 is a second explanatory diagram for a method of calculating a distance between the boom of the crane and the corresponding sensor data.
8 is a diagram illustrating an example of setting a first critical region to an N-th critical region;

Hereinafter, with reference to the accompanying drawings, a crane collision prevention system having a sensor according to an embodiment of the present invention and a method for analyzing the sensor data will be described in detail.

Of course, the following examples of the present invention are not intended to limit or limit the scope of the present invention only to embody the present invention. What can be easily inferred by an expert in the technical field to which the present invention belongs from the detailed description and examples of the present invention is construed as belonging to the scope of the present invention.

First, Figure 1 shows the configuration of a crane collision prevention system 100 according to a preferred embodiment of the present invention.

As can be seen from FIG. 1 , the crane collision prevention system 100 according to a preferred embodiment of the present invention includes a sensor unit 10 , a computing device 20 , and a server 30 .

The sensor unit 10 includes a plurality of sensors S.

Among the plurality of sensors (S), the first sensor (S1) and the second sensor (S2) are respectively provided on the upper and front surfaces of the boom (B) of the crane (C). In addition, the third sensor (S3) and the fourth sensor (S4) of the plurality of sensors (S) are preferably provided on the left side and the right side of the boom (B) of the crane (C), respectively.

For reference, the boom (B) of the crane (C) can move vertically and horizontally.

Specifically, the first sensor S1 and the second sensor S2 may use a radar sensor, and the third sensor S3 and the fourth sensor S4 may use a lidar sensor.

FIG. 2 is an explanatory diagram illustrating measurement areas of the first sensor S1 and the second sensor S2, and FIG. 3 is an explanatory diagram illustrating measurement areas of the third sensor S3 and the fourth sensor S4.

As can be seen from FIG. 2 , the first sensor S1 and the second sensor S2 are provided on the upper portion and the front surface of the distal end portion of the boom B of the crane C. As shown in FIG. As can be seen from FIG. 3 , the third sensor S3 and the fourth sensor S4 are configured to measure the area corresponding to the measurement area of the first sensor S1 and the entire area of the boom B , When the boom (B) is divided into 5 equal parts, it will be preferable to be provided on the left side and the right side of the 2/5 to 3/5 area from the end of the boom (B).

The computing device 20 is a device including a processor and a memory, and is disposed in the driver's seat of the crane C so that the operator of the crane C can monitor it.

The computing device 20 and the plurality of sensors S may be connected by a UTP cable. In addition, the computing device 20 receives and analyzes sensor data measured by a plurality of sensors S.

In addition, the server 30 is wirelessly connected to the computing device 20 so that a remote user such as an integrated control room or a field office can monitor.

Hereinafter, a method for analyzing sensor data by the computing device 20 of the present invention will be described in detail.

Specifically, the sensor data analysis method of the present invention may be implemented in the form of a computer program executed by the processor of the computing device 20 .

4 is a flowchart of a method for analyzing sensor data by the computing device 20 of the present invention.

As can be seen from Fig. 4, the sensor data analysis method of the present invention includes the steps of loading reference information (S10); generating a three-dimensional reference coordinate system based on preset origin coordinates (S20); receiving sensor data measured by a plurality of sensors (S) (S30); receiving sensor data measured by a plurality of sensors (S) and deleting data determined to be abnormal among sensor data (S40); and converting the two-dimensional sensor data into three-dimensional sensor data according to the three-dimensional reference coordinate system (S50).

The reference information of step S10 includes information of the boom (B) of the crane (C), a plurality of sensors, the region of interest, and the critical region stored in advance in the computing device (20).

As information about the boom B of the crane C, dimensional information such as the length, height, and thickness of the boom B can be exemplified. In addition, as the information on the sensor S, location and direction information on each of the plurality of sensors S may be exemplified.

The region of interest refers to a region in which the user desires to analyze data for the measurement regions of the plurality of sensors S. The computing device 20, after excluding the boom (B) and the accessory object of the crane (C) from the region of interest in the region of interest designated by the user, it is preferable to set the final region of interest.

The user can set the ROI on the user interface displayed on the monitor of the computing device 20 , so that the ROI can be reflected in real time.

Regions of interest are indicated in FIGS. 2 and 3 . The critical region is a region designated by the user as a region that requires special attention due to a high probability of collision among regions of interest.

For reference, the critical region can be classified into a number. That is, the critical region may be classified into a first critical region to an N-th critical region, and N may be a natural number equal to or greater than 1 .

The three-dimensional reference coordinate system of step S20 is a relative coordinate system having the position of the driver's seat as the origin.

For reference, the sensor data measured by the plurality of sensors S input in step S30 is preferably processed in steps S40 to S100 in a first preset time unit.

Step S40 is to exclude environmental factors such as rain, snow, dust, and data on objects such as birds.

That is, the criteria for determining the abnormal data in step S40 are as follows.

- Objects that appear and disappear quickly: When the distance between the previous and newly measured coordinates is greater than a certain value (eg, the coordinate movement distance is more than 5m based on the measurement period)

- Small object: If the size of the group is smaller than the standard size after grouping the measured coordinates based on the distance (eg, the size of the group is less than 1m)

Specifically, in step S40, the object determined to have moved at the first speed or higher among the sensor data; and an object determined to be less than or equal to the first size among the sensor data is determined as abnormal data, and the corresponding abnormal data is preferably deleted.

The determination of whether the corresponding object is less than or equal to the first size may be performed by grouping adjacent sensor data within a predetermined distance and then determining whether the size of the corresponding group is equal to or less than the first size.

In step S50, the preset origin coordinates may be set as the coordinates of the driver's seat of the corresponding crane (C).

After a plurality of sensors (S) acquire the sensor data of the two-dimensional coordinate system in the fixed posture of the boom (B), the boom (B) is moved to the next posture. In response to the movement of the boom (B), it is necessary to convert it into a reference coordinate system.

Specifically, in step S50, when the boom (B) moves, the position and/or direction of the sensor (S) also changes, so the sensor data is rotated and moved, and converted into three-dimensional sensor data by the reference coordinate system. can do.

In addition, the method for analyzing sensor data according to the present invention includes: deleting sensor data located outside of a set ROI (S60); Calculating the distance between the sensor data located inside the region of interest and the boom (B) of the crane (C) after deleting the sensor data located outside the region of interest (S70); outputting an alarm signal corresponding to the critical area information of the corresponding sensor data when the corresponding sensor data is located in the preset first critical area to the Nth critical area (S80); If there is a plurality of adjacent sensor data among the sensor data located inside the ROI, combining the adjacent plurality of sensor data into one representative data (S90); and when there is a plurality of adjacent sensor data, transmitting the data after merging the adjacent plurality of sensor data into one representative data (S100) to the server 30;

Fig. 5 is a first explanatory diagram for a method of calculating a distance between the boom B of the crane C and the corresponding sensor data.

As can be seen from Figure 5, in step S80, for each of the sensor data located inside the region of interest, a first straight line passing through the boom (B) of the crane (C); and the first straight line and orthogonal to the corresponding sensor The distance between the boom (B) of the crane (C) and the corresponding sensor data is calculated as follows according to the position of the first point where the second straight line passes through the data. That is, the first point is a point where the position of the sensor data is projected on the first straight line passing the boom B of the crean C. In other words, when the sensor data is projected at a right angle toward the first straight line extending by connecting the starting point and the end point of the boom (B) of the crane (C), the point where the first straight line meets is the first point.

(1) If the first point is located in front of the start point of the boom (B) of the crane (C), the distance between the start point of the boom (B) of the crane (C) and the corresponding sensor data is determined by the corresponding sensor data and the crane (C) ) and calculated as the distance between the boom (B).

(2) When the first point is located on the boom B of the crane C, the distance between the first point and the sensor data is determined between the sensor data and the boom B of the crane C calculated as a distance of .

(3) If the first point is located behind the end point of the boom (B) of the crane (C), the distance between the end point of the boom (B) of the crane (C) and the corresponding sensor data is determined by the corresponding sensor data and the crane (C) ) and calculated as the distance between the boom (B).

For reference, the distance between the boom B of the crane C and the corresponding sensor data calculated in the present invention means the shortest distance between the boom B of the crane C and the corresponding sensor data.

In addition, Figure 6 is a flowchart of a distance calculation method between the boom (B) of the crane (C) and the corresponding sensor data, and Figure 7 is a method for calculating the distance between the boom (B) of the crane (C) and the corresponding sensor data 2 An explanatory drawing is shown.

As can be seen from Figures 6 and 7, the method of calculating the distance between the boom (B) of the crane (C) and the corresponding sensor data includes the steps of generating a virtual straight line for calculating the first point (S810); calculating a projection vector from the origin of the corresponding coordinate system toward the first point (S820); identifying a second point as a reference for calculating the distance (S830); and calculating the shortest distance between the boom (B) of the crane (C) and the corresponding sensor data (S840).

In the following description, let the start point (L1) of the boom (B), the end point (L2) of the boom (B), the sensor data position (PS), the origin (O), the first point (P1), the second point (P2) .

The virtual straight line in step S810 is a straight line connecting the starting point (L1) and the ending point (L2) of the boom (B) of the crane (C), and extending this virtual straight line to the outside of the starting point (L1) and the ending point (L2) becomes the first straight line.

)의 크기(

)를 계산하고, 라인 벡터(

)를 정규화(

)한다. 또한, 시점(L1)에서 해당 센서 데이터의 좌표(PS)를 향하는 포인트 벡터(

)를 이용하여, 내적 스칼라(t)를 산출 후, 최종적으로 투영 벡터(

)를 산출하게 된다.In step S820, as shown in [Equation 1] to [Equation 6], a line vector (

) of the size (

) and calculate the line vector(

) to normalize (

)do. In addition, the point vector (

) to calculate the dot product scalar (t), and finally the projection vector (

) will be calculated.

)로부터 원점(O)에서 시점(L1)으로 향하는 벡터를 제 2 벡터(

)라고 할 때, 라인 벡터(

)와 제 2 벡터(

)에 대한 내적(dot)을 연산한다. 이 내적(dot)에 의해 투영 벡터(

)의 방향을 알 수 있다. 아울러, 해당 센서 데이터와 크레인(C)의 붐(B)과의 사이의 거리 산출의 기준이 되는 제 2 지점(P2)을 확인할 수 있다.In step S830, as in [Equation 7] and [Equation 8], the normalized projection vector (

) to the second vector (

), the line vector (

) and the second vector (

) to calculate the dot product. By this dot product, the projection vector (

) direction is known. In addition, it is possible to confirm the second point P2 as a reference for calculating the distance between the sensor data and the boom (B) of the crane (C).

)의 길이(

) 이상인 경우에는, 제 1 지점(P1)이 거리 산출의 기준이 되는 제 2 지점(P2)이 된다.For the case where the corresponding dot value is '0' or more, the length of the boom (B), which is the distance between the starting point (L1) and the ending point (L2), is the second vector (

) of the length (

) or more, the first point P1 becomes the second point P2 as a reference for calculating the distance.

)의 길이(

) 이 미만인 경우에는, 붐(B)의 종점(L2)이 거리 산출의 기준이 되는 제 2 지점(P2)이 된다.In addition, the dot value is '0' or more, and the length of the boom (B) of the crane (C) is the second vector (

) of the length (

) is less than this, the end point L2 of the boom B becomes the second point P2 as a reference for distance calculation.

In addition, when the dot value is less than '0', the starting point L1 of the boom B becomes the second point P2 as a reference for calculating the distance.

) 및 제 2 벡터(

)의 길이(

)를 [수학식 9] 및 [수학식 10]과 같이 산출한다.Specifically, in step S830, when the corresponding dot value is '0' or more, the distance between the starting point L1 and the ending point L2 (

) and the second vector (

) of the length (

) is calculated as in [Equation 9] and [Equation 10].

In step S840, using the second point P2 confirmed in step S830, as in [Equation 11], a distance d between the second point P2 and the coordinates PS of the sensor data is calculated. By doing so, the shortest distance between the boom B of the crane C and the said sensor data is computed.

8 is a diagram illustrating an example of setting a first critical region to an Nth critical region. In FIG. 8, N is set to 3.

In step S80, when the sensor data is located in the first critical region to the N-th critical region, it is preferable to output an alarm signal corresponding to the critical region information of the corresponding sensor data. That is, since the first critical area is determined as a dangerous area, an alarm of a first volume is sounded and a message of danger is broadcast, and since the second critical area is determined as a caution area, an alarm of a second volume is sounded and a message of caution is broadcast; Since the third critical area is determined as a safe area, it is possible to omit broadcasting of a separate alarm or message.

In the case of the lidar sensor, a plurality of sensor data may be generated according to the contour of the measurement target. Accordingly, data merging is performed in step S90 to improve data transmission speed and secure storage capacity. At this time, a plurality of sensor data may be bundled based on the sensor data having the closest distance to the boom (B). For example, a plurality of sensor data within a radius of 1 m may be grouped by one sensor data.

Specifically, in step S90, when a plurality of adjacent sensor data exists, the sensor data having the closest distance between the crane C and the boom B among the plurality of sensor data may be selected as representative data. The proximity may be determined using the distance between the sensor data.

In summary, the computing device 20 operates as follows for analysis of sensor data.

The computing device 20 deletes data determined to be abnormal among sensor data. That is, the computing device 20 may include: an object determined to have moved at a speed greater than or equal to a first speed among sensor data; and an object determined to be less than or equal to the first size among the sensor data is determined as abnormal data, and the corresponding abnormal data is deleted.

In addition, it is preferable that the computing device 20 converts the two-dimensional sensor data after the deletion of the abnormal data into three-dimensional sensor data based on the reference coordinate system.

In addition, the computing device 20 is characterized in that it deletes the sensor data located outside the set ROI, among the sensor data converted into the reference coordinate system. The computing device 20 calculates a distance between the sensor data located inside the region of interest and the boom B of the crane C after deleting the sensor data located outside the region of interest.

Preferably, the computing device 20 outputs an alarm signal corresponding to the critical region information of the corresponding sensor data when the corresponding sensor data is located within the preset first critical region to the Nth critical region, but N is a natural number of 1 or more.

In addition, when there is a plurality of adjacent sensor data among the sensor data located inside the ROI, the computing device 20 combines the adjacent plurality of sensor data into one representative data.

In addition, the computing device 20, among the sensor data located inside the region of interest, when there is a plurality of adjacent sensor data, the distance between the boom B of the crane C among the plurality of sensor data is The closest sensor data is selected as representative data.

As described above, according to the crane collision prevention system 100 of the present invention and the analysis method of its sensor data, even in an environment in which a plurality of cranes work simultaneously, a collision risk factor near the boom (B) area of the crane is predicted By doing so, the collision of a crane can be prevented.

100: Crane Collision Prevention System
10: sensor unit
20: computing device
30 : Server
S: sensor
C: Crane
B: Boom

Claims (20)

In the crane collision prevention system,
multiple sensors; and
Crane collision avoidance system comprising a; a computing device for receiving and analyzing the sensor data measured by the plurality of sensors. According to claim 1,
The first sensor and the second sensor among the plurality of sensors,
It is provided on the upper part and the front side of the boom of the crane, respectively,
A third sensor and a fourth sensor among the plurality of sensors,
Crane collision prevention system, characterized in that provided on the left side and the right side of the boom of the crane, respectively. According to claim 1,
The computing device is
Crane collision prevention system, characterized in that the data determined to be abnormal among the sensor data is deleted. 4. The method of claim 3,
The computing device is
an object determined to have moved at a speed greater than or equal to a first speed among the sensor data; and
Crane collision prevention system, characterized in that the object determined to be less than or equal to the first size of the sensor data is determined as abnormal data, and the corresponding abnormal data is deleted. 4. The method of claim 3,
The computing device is
A crane collision prevention system characterized by converting two-dimensional sensor data after deletion of abnormal data into three-dimensional sensor data using a reference coordinate system. 6. The method of claim 5,
The computing device is
Among the sensor data converted into the three-dimensional coordinate system, the system for preventing collision with a crane, characterized in that the sensor data located outside the set ROI is deleted. 7. The method of claim 6,
The computing device is
Crane collision avoidance system, characterized in that the distance between the sensor data located inside the region of interest and the boom of the crane after deleting the sensor data located outside the region of interest is calculated. 8. The method of claim 7,
The computing device, for each sensor data located inside the region of interest,
When the sensor data is projected at a right angle toward a first straight line extending by connecting the start and end points of the boom of the crane, the first point that meets the first straight line is located in front of the start point of the boom of the crane. Calculate the distance between the starting point of the boom of the crane and the corresponding sensor data as the distance between the corresponding sensor data and the boom of the crane,
When the first point is located on the boom of the crane, the distance between the first point and the sensor data is calculated as the distance between the sensor data and the boom of the crane,
When the first point is located behind the end point of the boom of the crane, the distance between the end point of the boom of the crane and the sensor data is calculated as the distance between the sensor data and the boom of the crane. crane collision avoidance system. 8. The method of claim 7,
The computing device is
When the corresponding sensor data is located within the preset first critical region to the Nth critical region, an alarm signal corresponding to the corresponding critical region information of the corresponding sensor data is output,
The N is a crane collision avoidance system, characterized in that 1 or more natural number. 8. The method of claim 7,
The computing device is
Among the sensor data located inside the region of interest, when there is a plurality of adjacent sensor data, the crane collision avoidance system, characterized in that the adjacent plurality of sensor data is combined into one representative data. 11. The method of claim 10,
The computing device is
Among the sensor data located inside the region of interest, if there is a plurality of adjacent sensor data, the sensor data having the closest distance to the boom of the crane from among the plurality of sensor data is selected as the representative data Crane collision avoidance system with In the method of analyzing sensor data provided in a crane by a computing device,
(a) receiving sensor data measured by a plurality of sensors, and deleting data determined to be abnormal from among the sensor data; and
(b) converting the two-dimensional sensor data after the step (a) into three-dimensional sensor data using a reference coordinate system; sensor data analysis method comprising: a. 13. The method of claim 12,
The first sensor and the second sensor among the plurality of sensors,
It is provided on the upper part and the front side of the boom of the crane, respectively,
A third sensor and a fourth sensor among the plurality of sensors,
Sensor data analysis method, characterized in that each provided on the left side and the right side of the boom of the crane. 13. The method of claim 12,
The analysis method of the sensor data, in the step (a),
an object determined to have moved at a speed greater than or equal to a first speed among the sensor data; and
A method for analyzing sensor data, characterized in that the object determined to be less than or equal to a first size among the sensor data is determined as abnormal data, and the corresponding abnormal data is deleted. 13. The method of claim 12,
The analysis method of the sensor data, after the step (b),
(c) deleting sensor data located outside the set ROI; 16. The method of claim 15,
The analysis method of the sensor data, after the step (c),
(d) calculating a distance between the sensor data located inside the region of interest and the boom of the crane after deleting the sensor data located outside the region of interest; sensor data comprising further comprising method of analysis. 17. The method of claim 16,
In the method of analyzing the sensor data, in step (d), for each sensor data located inside the region of interest,
When the sensor data is projected at a right angle toward a first straight line extending by connecting the start and end points of the boom of the crane, the first point that meets the first straight line is located in front of the start point of the boom of the crane. Calculate the distance between the starting point of the boom of the crane and the corresponding sensor data as the distance between the corresponding sensor data and the boom of the crane,
When the first point is located on the boom of the crane, the distance between the first point and the sensor data is calculated as the distance between the sensor data and the boom of the crane,
When the first point is located behind the end point of the boom of the crane, the distance between the end point of the boom of the crane and the sensor data is calculated as the distance between the sensor data and the boom of the crane. How to analyze sensor data. 17. The method of claim 16,
The analysis method of the sensor data, after the step (d),
(e) outputting an alarm signal corresponding to the critical area information of the corresponding sensor data when the corresponding sensor data is located inside the preset first critical area to the Nth critical area;
Wherein N is a natural number equal to or greater than 1, an analysis method of sensor data. 17. The method of claim 16,
The analysis method of the sensor data, after the step (d),
(f) if there is a plurality of adjacent sensor data among the sensor data located inside the region of interest, merging the adjacent plurality of sensor data into one representative data; How to analyze your data. 20. The method of claim 19,
The analysis method of the sensor data, in the step (f),
Among the sensor data located inside the region of interest, when there is a plurality of adjacent sensor data, the sensor data having the closest distance to the boom of the crane from among the plurality of sensor data is selected as the representative data A method of analyzing sensor data.

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