Overturn risk judgment method for vertical transportation of steel coils through walking beam transportation chain

Abstract

The invention provides an overturning risk judgment method for vertical transportation of a steel coil through a walking beam transportation chain, and relates to the technical field of rolling, and the method comprises the following steps: 1, obtaining initial data of the steel coil and the walking beam transportation chain; 2, steel coil point cloud data and saddle point cloud data are obtained through a laser radar; 3, the average distance between the end face of the steel coil and the saddle and the radar is obtained through the point cloud data; 4, the deviation distance between the center line of the steel coil and the center line of the saddle is calculated; 5, judging the coil shape of the steel coil, and calculating the outer ring overflow amount and the overflow width of the steel coil; and 6, whether the steel coil has the overturning risk or not is judged according to the initial data and the calculation data. The end face point cloud data information of the hot rolled steel coil and the saddle is obtained through the three-dimensional laser radar, the overturning risk is calculated through the steel coil overturning risk judgment rule, online detection of the shape of the hot rolled steel coil and early warning of the overturning risk of the steel coil are achieved, and the accident rate is reduced.

Description

Technical Field

[0001] The present application relates to the fields of metallurgical machinery, automation and rolling technology, and in particular to a method for determining the overturning risk of a walking beam transport chain vertically transporting a steel coil. Background Art

[0002] In the context of overcapacity in the steel industry, market competition is becoming increasingly fierce, and higher requirements are placed on the rhythm control of production and unplanned downtime. After the strip steel is coiled by the coiler, the coil is placed on the saddle of the walking beam transport chain by the coil unloading trolley and the coil transport trolley, and then transported to the coil warehouse by the walking beam transport chain. Since the position of the steel coil on the saddle is not fixed and the width of the steel coil is greater than the width of the saddle, and considering the heavy coil and poor coil shape, the steel coil often overturns on the walking beam transport chain, which seriously affects the production rhythm, causes long unplanned downtime, and increases production costs.

[0003] At present, the research on the overturning problem during the transportation of steel coils is mainly to prevent deviation and overturning by detecting the displacement deviation during the transportation of steel coils and estimating the final cumulative deviation. For example, the patent "A method and device for detecting the deviation of the transportation position of hot-rolled steel coils" (CN113589310A) calculates the expected offset of the steel coil at the easy-to-overturn position, realizes the judgment of the inclination of the steel coil, and alarms in advance, which can avoid the problem of the steel coil running off and overturning on the walking beam. However, the technology recorded in the above patent document is based on the condition that the steel coil is not crooked during transportation. It only judges that the position of the steel coil will exceed the limit value and thus judges that there is a risk of overturning. It lacks the judgment of the coil shape and overturning risk of the steel coil during the transportation of the walking beam. Summary of the invention

[0004] The present application is made in view of the above-mentioned problems, and its purpose is to provide a method for determining the overturning risk of steel coils transported vertically by a walking beam transport chain, by capturing the position information of the hot-rolled steel coils, starting a three-dimensional laser radar to obtain a large amount of end face point cloud data information of the hot-rolled steel coils and saddles, obtaining the end face point cloud features of the hot-rolled steel coils through a feature extraction algorithm, accurately identifying the coil shape information of the hot-rolled steel coils, calculating the characteristic parameters of the steel coils, calculating the overturning risk according to the invented steel coil overturning risk determination rules, realizing online detection of the hot-rolled steel coil shape and early warning of the steel coil overturning risk, standardizing risk determination, being able to warn of accidents in advance, preventing the occurrence of steel coil overturning accidents, and reducing the accident rate.

[0005] Specifically, a first aspect of the present application provides a method for determining the overturning risk of a steel coil being vertically transported by a walking beam transport chain, comprising the following steps:

[0006] Step 1: Obtain the rolling width B of the steel coil, the radius R of the steel coil and the saddle width b of the walking beam transport chain;

[0007] Since the walking beam needs to be inserted into the transition saddle to connect the coil, the width b of the walking beam saddle is generally only about half of the rolled width B of the steel coil.

[0008] Step 2: Obtain the point cloud data P1 of the steel coil and the point cloud data P2 of the saddle used to support the steel coil through the laser radar arranged on one side of the steel coil transport chain;

[0009] LiDAR is a sensor system that uses lasers to detect and measure target position, speed and other characteristic quantities. Its working principle is to calculate the distance and three-dimensional structure information of the object by emitting a laser beam and receiving the reflected signal.

[0010] Step 3: Obtain the average distance Z1 between the end face of the steel coil and the radar and the average distance Z2 between the end face of the saddle and the radar through the steel coil point cloud data P1 and the saddle point cloud data P2;

[0011] Step 4: Calculate the deviation distance d between the center line of the steel coil and the center line of the saddle by the average distance Z1 and Z2. The formula is as follows:

[0012]

[0013] Where: d is the deviation distance between the center line of the steel coil and the center line of the saddle;

[0014] abs is the absolute value function;

[0015] Z1 is the average distance between the end face of the steel coil and the radar;

[0016] Z2 is the average distance between the saddle end face and the radar;

[0017] B is the rolling width of the steel coil;

[0018] b is the saddle width of the walking beam transport chain;

[0019] Step 5: Based on the straight-through filtering, the point cloud data set related to the outer circle in the steel coil point cloud data P1 is obtained, and the radius corresponding to the point cloud data set is subjected to feature extraction to determine the coil shape of the steel coil, and the outer circle overflow amount c and overflow width L of the steel coil are calculated through the maximum value of the radius;

[0020] Step 6: Determine whether the steel coil has a risk of overturning based on the relevant data obtained in step 1, the deviation distance d between the center line of the steel coil and the center line of the saddle, the outer ring overflow amount c and the overflow width L of the steel coil.

[0021] Furthermore, the step 2 specifically includes:

[0022] Step 2.1: Place the 3D laser radar at a height level with the center of the end surface of the steel coil, and align the x-axis of the laser radar coordinate system with the center of the end surface of the steel coil;

[0023] Step 2.2: Fix the three-dimensional laser radar in the laser radar installation box and install it together on the transmission side of the walking beam transport chain, and the three-dimensional laser radar is connected to the laser radar server;

[0024] Step 2.3: Scan the walking beam vertical transport chain saddle and the upper steel coil transmission side through a three-dimensional laser radar to obtain three-dimensional point cloud data of the walking beam transport chain saddle and the steel coil;

[0025] Step 2.4: When the server receives the shooting signal and basic information such as the hot-rolled steel coil number sent from the production system, the server communicates with the three-dimensional laser radar and sends a start instruction to the three-dimensional laser radar;

[0026] Step 2.5: After receiving the start command, the 3D laser radar starts scanning the end face of the hot-rolled steel coil, and transmits the point cloud data to the server after scanning for a certain period of time;

[0027] Step 2.6: The laser radar server processes and stores the obtained three-dimensional point cloud data to obtain the steel coil point cloud data P1 and the saddle point cloud data P2.

[0028] Furthermore, the step three specifically includes:

[0029] The target point cloud data P1 and P2 are segmented by adopting a straight-through filtering method to obtain an average distance Z1 between the end surface point cloud of the steel coil and the radar and an average distance Z2 between the end surface point cloud of the saddle and the radar.

[0030] Furthermore, the target point cloud data P1 and P2 are segmented by using a straight-through filtering method, which specifically includes:

[0031] By setting the filtering threshold range on the x-axis of the three-dimensional laser radar coordinate system, the steel coil end face point cloud data and the saddle end face point cloud data are obtained respectively. The x-coordinate average value Z1 of all points in the steel coil end face point cloud data is used to represent the average distance between the steel coil end face and the radar. The x-coordinate average value Z2 of all points in the saddle end face point cloud data is used to represent the average distance between the saddle end face and the radar.

[0032] Furthermore, the feature extraction of the radius corresponding to the point cloud data set is performed to determine the coil shape of the steel coil, and the body includes:

[0033] Step 5.1: Obtain i points within the range of 1 cm above and below z0 on the end surface point cloud P1 of the steel coil through straight-through filtering, and change all the z-axis values ​​of the obtained point cloud to 0, while keeping the x and y coordinate values ​​unchanged, to obtain the diameter point cloud data set of the steel coil;

[0034] Step 5.2: Obtain the point cloud radius data set with y coordinate values ​​greater than 0 through straight-through filtering;

[0035] Step 5.3: Rotate the steel coil end surface point cloud dataset by a certain angle to obtain a new steel coil end surface point cloud dataset, and re-execute the above steps to obtain a new point cloud radius dataset;

[0036] Step 5.4: Perform feature extraction on the radius corresponding to each point cloud radius data set, extract the point cloud of the outer 1 / 3 of the current point cloud radius data set, calculate the distance between the point cloud with the minimum x-direction value and the point cloud with the maximum x-direction value in the extracted point cloud, and if the calculated value is greater than the overflow standard, the current volume is judged to be an outer circle overflow volume.

[0037] Furthermore, the outer ring overflow amount c and the overflow width L of the steel coil are calculated by the maximum value of the radius, specifically including:

[0038] Extract the point cloud of the outer 1 / 3 of the current point cloud radius dataset, calculate the distance x1 between the point cloud with the minimum value and the point cloud with the maximum value in the x direction and the distance y1 between the point cloud with the minimum value and the point cloud with the maximum value in the y direction, x1 is the outer circle overflow amount c, y1 is the overflow width L.

[0039] Furthermore, the step six specifically includes:

[0040] The criteria for judging the rollover risk of a steel coil are as follows. If the corresponding data of a steel coil meets at least one of the criteria, it means that the steel coil has a rollover risk:

[0041] Judgment criteria 1:

[0042] d>α1*b;

[0043] Criteria 2:

[0044] c*L>β1*b*R;

[0045] Judgment Criteria 3:

[0046] (d>α2*b)∩(c*L>β2*b*R);

[0047] Where: d is the deviation distance between the center line of the steel coil and the center line of the saddle;

[0048] b is the saddle width of the walking beam transport chain;

[0049] α1 is the determination proportional coefficient of determination criterion 1;

[0050] c is the overflow of the outer ring of the steel coil;

[0051] L is the overflow width of the outer ring of the steel coil;

[0052] β1 is the determination proportionality coefficient of determination criterion 2;

[0053] R is the radius of the steel coil;

[0054] α2 and β2 are both determination proportional coefficients of determination criterion 3.

[0055] The value range of α1 is 0.25~0.45; the value range of β1 is 0.06~0.1; the value range of α2 is 0.1~0.25; the value range of β2 is 0.05~0.07.

[0056] Furthermore, the installation arrangement of the laser radar mainly includes a three-dimensional laser radar, a mounting bracket, an air cooler and a server;

[0057] The three-dimensional laser radar and the air cooler are both installed in the mounting bracket;

[0058] The mounting bracket is placed on the operating side at a preset distance from the static position of the steel coil when it is off the line;

[0059] The three-dimensional laser radar is connected to the server;

[0060] The air cooler is used to dissipate heat for the three-dimensional laser radar;

[0061] The server extracts and identifies features of the steel coil based on the point cloud of the steel coil end surface.

[0062] When the steel coil reaches the specified position, the three-dimensional laser radar starts to scan the end face of the steel coil operating side to obtain a point cloud of the steel coil end face, and transmits the obtained point cloud of the steel coil end face to the server. The server extracts and identifies the features of the steel coil based on the point cloud of the steel coil end face.

[0063] Furthermore, the installation height of the three-dimensional laser radar is located on the extension line of the center line of the steel coil, and the yoz scanning plane of the three-dimensional laser radar is perpendicular to the center line direction of the steel coil.

[0064] In the second aspect, the present application also provides a computing device, which has the function of implementing the method described in the first aspect above. The beneficial effects can be found in the description of the first aspect, which will not be repeated here. The function can be implemented by hardware, or by executing the corresponding software implementation through hardware. The hardware or software includes one or more modules corresponding to the above functions. In one possible design, the structure of the device includes an acquisition module, a training module, and optionally, a construction module. These modules can implement the function of the training node in the method example of the first aspect above. Please refer to the detailed description in the method example for details, which will not be repeated here.

[0065] In the third aspect, the present application also provides a computing device, which is used to implement the functions of the method described in the first aspect above. The beneficial effects can be found in the description of the first aspect and will not be repeated here. The structure of the computing device includes a processor and a memory, and the memory is used to store instructions and / or data. The memory is coupled to the processor, and when the processor executes the program instructions stored in the memory, the function of the training node in the example of the first aspect above can be implemented. The structure of the computing device also includes a communication interface for communicating with other devices.

[0066] In a fourth aspect, the present application also provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the method in the above-mentioned first aspect and various possible designs of the first aspect.

[0067] In a fifth aspect, the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method in the above-mentioned first aspect and various possible designs of the first aspect.

[0068] In a sixth aspect, the present application also provides a computing chip, which is connected to a memory, and the chip is used to read and execute a software program stored in the memory, and to execute the methods in the above-mentioned first aspect and various possible implementation methods of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present drawings or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present drawings. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0070] Figure 1 A flowchart of the steps of this application;

[0071] Figure 2 The original point cloud image collected by LiDAR;

[0072] Figure 3 This is the point cloud image of the saddle;

[0073] Figure 4 It is the point cloud image of the end face of the steel coil;

[0074] Figure 5 This is a point cloud image of a selected radius on the steel coil.

[0075] The purpose, features and advantages of this figure will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0077] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0078] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0079] Unless otherwise specified, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0080] If there is no special explanation, all steps of the present application can be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), which means that the method may include steps (a) and (b) performed sequentially, or may include steps (b) and (a) performed sequentially. For example, the method may further include step (c), which means that step (c) may be added to the method in any order. For example, the method may include steps (a), (b) and (c), or may include steps (a), (c) and (b), or may include steps (c), (a) and (b), etc.

[0081] If there is no special explanation, the "include" and "comprising" mentioned in this application are open-ended or closed-ended. For example, the "include" and "comprising" may mean that other components not listed may also be included or only the listed components may be included or only the listed components may be included.

[0082] If not specifically stated, in this application, the term "or" is inclusive. For example, the phrase "A or B" means "A, B, or both A and B". More specifically, any of the following conditions satisfies the condition "A or B": A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); or both A and B are true (or exist).

[0083] In order to better understand the solutions of the embodiments of the present application, some relevant terms and concepts that may be involved in the embodiments of the present application are first introduced below.

[0084] (1) 3D Laser Ranging and Detection (LiDAR) is an advanced optical sensing technology that acquires 3D spatial information of target objects by emitting laser pulses and receiving their reflected signals. This technology can generate high-precision and high-resolution point cloud data, thereby achieving accurate perception and modeling of the environment.

[0085] (2) PassThrough Filter is a commonly used filtering method in point cloud processing. Its main function is to filter out points that are not within the range of the value range on the specified dimension. The advantage of pass-through filtering is that the operation is simple and intuitive, and it is suitable for quickly removing outliers or unnecessary points that are not within a specific range. However, it also has some limitations, such as only filtering one dimension at a time. If you need to limit multiple dimensions at the same time, you need to apply the pass-through filter multiple times.

[0086] like Figure 1 As shown, in this embodiment, taking the enterprise's 2250mm hot rolling production line as an example, a method for determining the overturning risk of a walking beam transport chain vertically transporting a steel coil includes the following steps:

[0087] Step 1: Obtain the rolling width B of the steel coil, the radius R of the steel coil and the saddle width b of the walking beam transport chain;

[0088] Since the walking beam needs to be inserted into the transition saddle to connect the coil, the width b of the walking beam saddle is generally only about half of the rolled width B of the steel coil.

[0089] In this embodiment, the rolled width B of the steel coil is 2013 mm, the saddle width b of the walking beam transport chain is 956 mm, and the radius R of the steel coil is 700 mm.

[0090] Step 2: Obtain the point cloud data P1 of the steel coil and the point cloud data P2 of the saddle used to support the steel coil through the laser radar arranged on one side of the steel coil transport chain;

[0091] LiDAR is a sensor system that uses lasers to detect and measure target position, speed and other characteristic quantities. Its working principle is to calculate the distance and three-dimensional structure information of the object by emitting a laser beam and receiving the reflected signal.

[0092] The original point cloud image obtained by LiDAR is as follows: Figure 2 As shown in FIG. 1 , the collected original point cloud is segmented, and the point cloud data P2 of the saddle segmented by setting the straight-through filter range [3700, 3800] in the x-axis direction of the laser radar coordinate system is as follows: Figure 3 As shown in the figure, the point cloud data P1 of the steel coil end surface segmented by setting the straight-through filter range [2000, 6000] in the x-axis direction is as follows Figure 4 shown.

[0093] Step 3: Obtain the average distance Z1 between the end face of the steel coil and the radar and the average distance Z2 between the end face of the saddle and the radar through the steel coil point cloud data P1 and the saddle point cloud data P2;

[0094] In this embodiment, the number of points in the steel coil end face point cloud data obtained after straight-through filtering is counted, the x-coordinate values ​​of all end face points are calculated and averaged, and the average of the x-coordinate values ​​of all end face point clouds is used to represent the average distance from the steel coil end face to the laser radar. The average distance Z1 = 3650 mm between the steel coil end face and the radar is calculated by dividing the sum of the x-coordinate values ​​of all end face point clouds by the number of points.

[0095] Count the number of points in the point cloud data of the saddle obtained after straight-through filtering, calculate the average x-coordinate value of the point cloud of all saddles, use the average x-coordinate value of the point cloud of all saddles to represent the average distance from the end face of the saddle to the laser radar, and divide the sum of the x-coordinate values ​​of the point cloud of all saddles by the number of points to calculate the average distance Z2 = 3710 mm between the end face of the steel coil and the radar.

[0096] Step 4: Calculate the deviation distance d between the center line of the steel coil and the center line of the saddle by the average distance Z1 and Z2. The formula is as follows:

[0097]

[0098] In this embodiment, the deviation distance d between the center line of the steel coil and the center line of the saddle is calculated to be 68.5 mm.

[0099] Step 5: Based on the straight-through filtering, the point cloud data set related to the outer circle in the steel coil point cloud data P1 is obtained, and the radius corresponding to the point cloud data set is subjected to feature extraction to determine the coil shape of the steel coil, and the outer circle overflow amount c and overflow width L of the steel coil are calculated through the maximum value of the radius;

[0100] Step 6: Determine whether the steel coil has a risk of overturning based on the relevant data obtained in step 1, the deviation distance d between the center line of the steel coil and the center line of the saddle, the outer ring overflow amount c and the overflow width L of the steel coil.

[0101] Furthermore, step 2 specifically includes:

[0102] Step 2.1: Place the 3D laser radar at a height level with the center of the end surface of the steel coil, and align the x-axis of the laser radar coordinate system with the center of the end surface of the steel coil;

[0103] Step 2.2: Fix the three-dimensional laser radar in the laser radar installation box and install it together on the transmission side of the walking beam transport chain, and the three-dimensional laser radar is connected to the laser radar server;

[0104] Step 2.3: Scan the walking beam vertical transport chain saddle and the upper steel coil transmission side through a three-dimensional laser radar to obtain three-dimensional point cloud data of the walking beam transport chain saddle and the steel coil;

[0105] Step 2.4: When the server receives the shooting signal and basic information such as the hot-rolled steel coil number sent from the production system, the server communicates with the three-dimensional laser radar and sends a start instruction to the three-dimensional laser radar;

[0106] Step 2.5: After receiving the start command, the 3D laser radar starts scanning the end face of the hot-rolled steel coil, and transmits the point cloud data to the server after scanning for a certain period of time;

[0107] Step 2.6: The laser radar server processes and stores the obtained three-dimensional point cloud data to obtain the steel coil point cloud data P1 and the saddle point cloud data P2.

[0108] Furthermore, step three specifically includes:

[0109] The target point cloud data P1 and P2 are segmented by adopting a straight-through filtering method to obtain an average distance Z1 between the end surface point cloud of the steel coil and the radar and an average distance Z2 between the end surface point cloud of the saddle and the radar.

[0110] Furthermore, the target point cloud data P1 and P2 are segmented by using a straight-through filtering method, which specifically includes:

[0111] By setting the filtering threshold range on the x-axis of the three-dimensional laser radar coordinate system, the point cloud data of the steel coil end surface and the point cloud data of the saddle end surface are obtained respectively. The average value Z1 of the x-coordinates of all points in the steel coil end surface point cloud data is used to represent the average distance from the steel coil end surface to the radar. The average value Z2 of the x-coordinates of all points in the saddle end surface point cloud data is used to represent the average distance from the saddle end surface to the radar.

[0112] Furthermore, feature extraction is performed on the radius corresponding to the point cloud data set to determine the coil shape of the steel coil, including:

[0113] Step 5.1: Obtain i points within the range of 1 cm above and below z0 on the end surface point cloud P1 of the steel coil through straight-through filtering, and change all the z-axis values ​​of the obtained point cloud to 0, while keeping the x and y coordinate values ​​unchanged, to obtain the diameter point cloud data set of the steel coil;

[0114] Step 5.2: Obtain the point cloud radius data set with y coordinate values ​​greater than 0 through straight-through filtering;

[0115] In this embodiment, the point cloud data of one radius of the steel coil is as follows: Figure 5 shown.

[0116] Step 5.3: Rotate the steel coil end surface point cloud dataset by a certain angle to obtain a new steel coil end surface point cloud dataset, and re-execute the above steps to obtain a new point cloud radius dataset;

[0117] Step 5.4: Perform feature extraction on the radius corresponding to each point cloud radius data set, extract the point cloud of the outer 1 / 3 of the current point cloud radius data set, calculate the distance between the point cloud with the minimum x-direction value and the point cloud with the maximum x-direction value in the extracted point cloud, and if the calculated value is greater than the overflow standard, the current volume is judged to be an outer circle overflow volume.

[0118] Furthermore, the outer ring overflow amount c and overflow width L of the steel coil are calculated by the maximum value of the radius, specifically including:

[0119] Extract the point cloud of the outer 1 / 3 of the current point cloud radius dataset, calculate the distance x1 between the point cloud with the minimum value and the point cloud with the maximum value in the x direction and the distance y1 between the point cloud with the minimum value and the point cloud with the maximum value in the y direction, x1 is the outer circle overflow amount c, y1 is the overflow width L.

[0120] In this embodiment, the outer ring overflow amount of the steel coil is calculated to be c=11cm, the overflow width is L=20cm, and the steel coil overflow standard is 10cm. Since the outer ring overflow amount is greater than the overflow standard, it is judged to be an outer ring overflow coil, and it is necessary to further judge whether this steel coil has the risk of overturning.

[0121] Furthermore, step six specifically includes:

[0122] The criteria for judging the rollover risk of a steel coil are as follows. If the corresponding data of a steel coil meets at least one of the criteria, it means that the steel coil has a rollover risk:

[0123] Judgment criteria 1:

[0124] d>α1*b;

[0125] Criteria 2:

[0126] c*L>β1*b*R;

[0127] Judgment Criteria 3:

[0128] (d>α2*b)∩(c*L>β2*b*R);

[0129] In this embodiment, the value of α1 is 0.25; the value range of β1 is 0.06; the value range of α2 is 0.1; the value range of β2 is 0.05. The value of the determination coefficient in the determination standard is related to the radius of the steel coil and the transmission speed.

[0130] Judgment criterion 1: d = 68.5, α1*b = 239, the inequality does not hold;

[0131] Judgment criterion 2: c*L=22000, β1*b*R=40152, the inequality does not hold;

[0132] Judgment criterion 3: α2*b=95.6, β2*b*R=33460, both inequalities on both sides of the union symbol do not hold;

[0133] In summary, in this embodiment, the three judgment criteria for the overturning risk of the steel coil are not met, so the steel coil has no overturning risk.

[0134] Furthermore, the installation arrangement of the laser radar mainly includes a three-dimensional laser radar, a mounting bracket, an air cooler, and a server;

[0135] The three-dimensional laser radar and the air cooler are both installed in the mounting bracket;

[0136] The mounting bracket is placed on the operating side at a preset distance from the static position of the steel coil when it is off the line;

[0137] The three-dimensional laser radar is connected to the server;

[0138] The air cooler is used to dissipate heat for the three-dimensional laser radar;

[0139] The server extracts and identifies the features of the steel coil based on the point cloud of the steel coil end surface.

[0140] When the steel coil reaches the specified position, the three-dimensional laser radar starts to scan the end face of the steel coil operating side to obtain a point cloud of the steel coil end face, and transmits the obtained point cloud of the steel coil end face to the server. The server extracts and identifies the features of the steel coil based on the point cloud of the steel coil end face.

[0141] Furthermore, the installation height of the three-dimensional laser radar is located on the extension line of the center line of the steel coil, and the yoz scanning plane of the three-dimensional laser radar is perpendicular to the center line direction of the steel coil.

[0142] It should be noted that the present application is not limited to the above-mentioned embodiments. The above-mentioned embodiments are only examples, and the embodiments having the same structure as the technical idea and exerting the same effect within the scope of the technical solution of the present application are all included in the technical scope of the present application. In addition, without departing from the scope of the main purpose of the present application, various modifications that can be thought of by those skilled in the art to the embodiments and other methods of combining some of the constituent elements in the embodiments are also included in the scope of the present application.

Claims

1. A method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically, characterized in that: The following steps are involved: Step 1: Obtain the rolling width B of the steel coil, the radius R of the steel coil and the saddle width b of the walking beam transport chain; Step 2: Obtain the point cloud data P1 of the steel coil and the point cloud data P2 of the saddle used to support the steel coil through the laser radar arranged on one side of the steel coil transport chain; Step 3: Obtain the average distance Z1 between the end face of the steel coil and the radar and the average distance Z2 between the end face of the saddle and the radar through the steel coil point cloud data P1 and the saddle point cloud data P2; Step 4: Calculate the deviation distance d between the center line of the steel coil and the center line of the saddle by the average distance Z1 and Z2. The formula is as follows: Where: d is the deviation distance between the center line of the steel coil and the center line of the saddle; abs is the absolute value function; Z1 is the average distance between the end face of the steel coil and the radar; Z2 is the average distance between the saddle end face and the radar; B is the rolling width of the steel coil; b is the saddle width of the walking beam transport chain; Step 5: Based on the straight-through filtering, the point cloud data set related to the outer circle in the steel coil point cloud data P1 is obtained, and the feature extraction is performed on the radius corresponding to the point cloud data set to determine the coil shape of the steel coil, and the outer circle overflow amount c and overflow width L of the steel coil are calculated according to the maximum value of the radius; Step 6: Determine whether the steel coil has a risk of overturning based on the relevant data obtained in step 1, the deviation distance d between the center line of the steel coil and the center line of the saddle, the outer ring overflow amount c and the overflow width L of the steel coil.

2. The method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically according to claim 1, characterized in that: The step 2 specifically includes: Step 2.1: Place the 3D laser radar at a height level with the center of the end surface of the steel coil, and align the x-axis of the laser radar coordinate system with the center of the end surface of the steel coil; Step 2.2: Fix the three-dimensional laser radar in the laser radar installation box and install it together on the transmission side of the walking beam transport chain, and the three-dimensional laser radar is connected to the laser radar server; Step 2.3: Scan the walking beam vertical transport chain saddle and the upper steel coil transmission side through a three-dimensional laser radar to obtain three-dimensional point cloud data of the walking beam transport chain saddle and the steel coil; Step 2.4: When the server receives the shooting signal and basic information such as the hot-rolled steel coil number sent from the production system, the server communicates with the three-dimensional laser radar and sends a start instruction to the three-dimensional laser radar; Step 2.5: After receiving the start command, the 3D laser radar starts scanning the end face of the hot-rolled steel coil, and transmits the point cloud data to the server after scanning for a certain period of time; Step 2.6: The laser radar server processes and stores the obtained three-dimensional point cloud data to obtain the steel coil point cloud data P1 and the saddle point cloud data P2.

3. The method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically according to claim 1, characterized in that: The step three specifically includes: The target point cloud data P1 and P2 are segmented by adopting a straight-through filtering method to obtain an average distance Z1 between the end surface point cloud of the steel coil and the radar and an average distance Z2 between the end surface point cloud of the saddle and the radar.

4. The method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically according to claim 3, characterized in that: The target point cloud data P1 and P2 are segmented by using a straight-through filtering method, specifically including: By setting the filtering threshold range on the x-axis of the three-dimensional laser radar coordinate system, the steel coil end face point cloud data and the saddle end face point cloud data are obtained respectively. The x-coordinate average value Z1 of all points in the steel coil end face point cloud data is used to represent the average distance between the steel coil end face and the radar. The x-coordinate average value Z2 of all points in the saddle end face point cloud data is used to represent the average distance between the saddle end face and the radar.

5. The method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically according to claim 1, characterized in that: The feature extraction of the radius corresponding to the point cloud data set to determine the coil shape of the steel coil specifically includes: Step 5.1: Obtain i points within the range of 1 cm above and below z0 on the end surface point cloud P1 of the steel coil through straight-through filtering, and change all the z-axis values ​​of the obtained point cloud to 0, while keeping the x and y coordinate values ​​unchanged, to obtain the diameter point cloud data set of the steel coil; Step 5.2: Obtain the point cloud radius data set with y coordinate values ​​greater than 0 through straight-through filtering; Step 5.3: Rotate the steel coil end surface point cloud dataset by a certain angle to obtain a new steel coil end surface point cloud dataset, and re-execute the above steps to obtain a new point cloud radius dataset; Step 5.4: Perform feature extraction on the radius corresponding to each point cloud radius data set, extract the point cloud of the outer 1 / 3 of the current point cloud radius data set, calculate the distance between the point cloud with the minimum x-direction value and the point cloud with the maximum x-direction value in the extracted point cloud, and if the calculated value is greater than the overflow standard, the current volume is judged to be an outer circle overflow volume.

6. The method for determining the overturning risk of a walking beam conveyor chain in vertical transport of a steel coil according to claim 1, characterized in that: The outer ring overflow amount c and overflow width L of the steel coil are calculated by using the maximum value of the radius, specifically including: Extract the point cloud of the outer 1 / 3 of the current point cloud radius dataset, calculate the distance x1 between the point cloud with the minimum value and the point cloud with the maximum value in the x direction and the distance y1 between the point cloud with the minimum value and the point cloud with the maximum value in the y direction, x1 is the outer circle overflow amount c, y1 is the overflow width L.

7. The method for determining the overturning risk of a walking beam conveyor chain transporting a steel coil vertically according to claim 1, characterized in that: The step six specifically includes: The criteria for judging the rollover risk of a steel coil are as follows. If the corresponding data of a steel coil meets at least one of the criteria, it means that the steel coil has a rollover risk: Judgment criteria 1: d>α1*b; Criteria 2: c*L>β1*b*R; Judgment Criteria 3: (d>α2*b)∩(c*L>β2*b*R); Where: d is the deviation distance between the center line of the steel coil and the center line of the saddle; b is the saddle width of the walking beam transport chain; α1 is the determination proportional coefficient of determination criterion 1; c is the overflow of the outer ring of the steel coil; L is the overflow width of the outer ring of the steel coil; β1 is the determination proportionality coefficient of determination criterion 2; R is the radius of the steel coil; α2 and β2 are both determination proportional coefficients of determination criterion 3.

8. The method for determining the overturning risk of a walking beam conveyor chain in vertical transport of a steel coil according to claim 1, characterized in that: The installation arrangement of the laser radar mainly includes a three-dimensional laser radar, a mounting bracket, an air cooler and a server; The three-dimensional laser radar and the air cooler are both installed in the mounting bracket; The mounting bracket is placed on the operating side at a preset distance from the static position of the steel coil when it is offline; The three-dimensional laser radar is connected to the server; The air cooler is used to dissipate heat for the three-dimensional laser radar; The server extracts and identifies features of the steel coil based on the point cloud of the steel coil end surface.

9. The method for determining the overturning risk of a walking beam conveyor chain in vertical transport of a steel coil according to claim 8, characterized in that: The installation height of the three-dimensional laser radar is located on the extension line of the center line of the steel coil, and the yoz scanning plane of the three-dimensional laser radar is perpendicular to the center line direction of the steel coil.

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