A bar tracking method, a bar tracking system, and a bar tracking device.

Abstract

This document relates to the field of bar production technology, and in particular to a bar tracking method, a bar tracking system, and a bar tracking device. The method includes: repeatedly scanning the bars located on a collection platform in the detection area to generate point cloud data of the bars; clustering and identifying the point cloud data to determine different bars on the collection platform and the center coordinates of the bars; and tracking the bars based on their center coordinates. The embodiments described in this specification can avoid material tracking problems caused by traditional instrument detection signal failures, bar slippage, jamming, and manual rejection, accurately achieving real-time tracking of bars in the collection platform area. This lays the foundation for realizing full-process material tracking of bars, meets the new needs of manufacturers for information-based management and intelligent data analysis of the production process, and has excellent application value.

Description

Technical Field

[0001] This article relates to the field of bar production technology, and in particular to a bar tracking method, bar tracking system and bar tracking device. Background Technology

[0002] The collection platform is a crucial material collection process in the bar production line. It consists of multiple chain conveyor sections, each driven by a motor and running on a track. By controlling the speed and direction of each chain section, it moves the bars from the loading side to the unloading side. The collection platform transports bars in bundles, each bundle consisting of multiple bars. On the loading side, a hydraulically operated lateral support arm places each bundle onto the first chain section, while on the unloading side, the bars fall into the bundling chute under their own weight.

[0003] Currently, manufacturers increasingly rely on precise material tracking throughout the entire bar rolling production process. While relatively accurate material tracking has been achieved in areas such as heating, multi-stand continuous rolling, multiple-length cutting, cooling bed cooling, and fixed-length cutting, reliable equipment action signals are scarce in the collection platform area. The stability of detection instrument signals is poor, and frequent malfunctions or slippage and jamming of the bars, along with delayed speed handover between multiple chain segments, all contribute to inaccurate position calculations and poor tracking accuracy.

[0004] Manually removing defective bars based solely on visual observation and operator judgment presents problems. It is time-consuming, labor-intensive, and has extremely high labor costs. It also suffers from low accuracy and poor timeliness, making it difficult to provide timely warnings and hindering the automatic tracking of bar stock.

[0005] Existing technologies also include image analysis tracking methods, which track rods by identifying the ROI region. A yellow primer is applied to the tracking area to increase the image contrast between the rod and the background color, followed by image analysis for tracking. However, the yellow primer in the tracking area needs to be repainted periodically; otherwise, tracking accuracy will be affected, and maintenance will be difficult. External factors, such as light intensity and dust, may also affect the tracking results.

[0006] In summary, existing material tracking systems cannot achieve the automation and information integration of numerous individual devices across the entire production line, nor can they achieve a high degree of informatization, high intelligence, and reduced manpower in the production line. Summary of the Invention

[0007] To address the problem that existing material tracking technologies cannot achieve automation and information integration of numerous individual devices across the entire production line, this specification provides a bar tracking method, a bar tracking system, and a bar tracking device.

[0008] This specification provides a bar tracking method, which includes: scanning a bar located on a collection platform in a detection area multiple times to generate point cloud data of the bar; clustering and identifying the point cloud data to determine different bars on the collection platform and the center coordinates of the bars; and tracking the bars according to the center coordinates of the bars.

[0009] According to one aspect of the embodiments herein, clustering identification of the point cloud data of the bar includes: calculating the distance between each point cloud in the point cloud data of the bar and other point clouds; and classifying point clouds whose distance does not exceed a preset distance threshold as a class of point cloud data.

[0010] According to one aspect of the embodiments herein, determining the different rods on the collection platform and the center coordinates of the rods further includes: calculating the average of the sum of the distances of each point cloud to all other point clouds in each type of point cloud data; reading the point cloud with the smallest average value, and using the coordinates corresponding to the point cloud as the center coordinates of the rod.

[0011] According to one aspect of the embodiments herein, tracking the rod based on its center coordinates includes: obtaining the center coordinates of the rod on the collection platform at the previous scanning time as the first center coordinates; obtaining the center coordinates of the rod on the collection platform at the current scanning time as the second center coordinates; determining whether the displacement difference between the first center coordinates and the second center coordinates is within a preset displacement range; if yes, determining that the second center coordinates and the first center coordinates are the center coordinates of the same rod at different scanning times; if no, determining that the first center coordinates and the second center coordinates are the center coordinates of different rods, and determining that a new rod has appeared on the collection platform.

[0012] According to one aspect of the embodiments herein, before generating the point cloud data of the bar, the method further includes: placing a standard bar on a collection platform in the detection area; scanning the standard bar to generate initial point cloud data of the standard bar; determining an angular offset between the scanning device and the detection area based on the angle between the initial point cloud data and the horizontal direction of the collection platform; calibrating the initial point cloud data based on the offset; and scanning the bar located on the collection platform in the detection area based on the data calibration to generate the point cloud data of the bar.

[0013] This embodiment provides a bar tracking system, comprising: a collection platform including a multi-segment conveyor in operation, with bars placed on top of the conveyors; a scanning device for scanning the length of the collection platform to generate point cloud data of the bars on the collection platform above the conveyors; a control unit electrically connected to the scanning device for repeatedly scanning the bars located on the collection platform in the detection area to generate point cloud data of the bars; clustering and identifying the point cloud data to determine different bars on the collection platform and the center coordinates of the bars; and tracking the bars based on their center coordinates.

[0014] This specification provides a bar tracking device, comprising: a point cloud data generation unit for repeatedly scanning bars located on a collection platform in a detection area to generate point cloud data of the bars; a center coordinate calculation unit for clustering and identifying the point cloud data of the bars to determine different bars on the collection platform and their center coordinates; and a bar tracking unit for tracking the bars based on their center coordinates.

[0015] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the bar tracking method.

[0016] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the bar tracking method.

[0017] This embodiment provides a computer program product, which includes a computer program that, when executed by a processor, implements the bar tracking method.

[0018] This solution lays the foundation for achieving full-process material tracking of bar stock. It is highly reliable and adaptable, meeting the new needs of manufacturers for information-based management and intelligent data analysis of the production process. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments or prior art described herein, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this article. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The diagram shown is a flowchart of a bar tracking method according to an embodiment of this paper;

[0021] Figure 2 The diagram shown is a flowchart of a method for clustering and identifying point cloud data of bar stock according to an embodiment of this paper.

[0022] Figure 3 The diagram shown is a flowchart of a method for determining the center coordinates of a bar according to an embodiment of this paper;

[0023] Figure 4 The diagram shown is a flowchart of a method for tracking bar stock according to an embodiment of this paper;

[0024] Figure 5 The diagram shown is a flowchart of a data calibration method according to an embodiment of this paper;

[0025] Figure 6 The diagram shown is a schematic diagram of a bar tracking system according to an embodiment of this article;

[0026] Figure 7 The diagram shown is a structural schematic of a bar tracking device according to an embodiment of this paper;

[0027] Figure 8A The image shown is a schematic diagram of an initial point cloud data in an embodiment of this paper;

[0028] Figure 8B The figure shown is a schematic diagram of the angle formed between initial point cloud data and the horizontal direction in an embodiment of this paper;

[0029] Figure 8C The image shown is a schematic diagram of point cloud data after data calibration, according to an embodiment of this paper.

[0030] Figure 9 The diagram shown is a structural schematic of a computer device according to an embodiment of this article.

[0031] Explanation of symbols in the attached drawings:

[0032] 601. Collection platform;

[0033] 6011, Segmented Chain;

[0034] 602. Scanning equipment;

[0035] 603. Control unit;

[0036] 604, bar stock;

[0037] 701. Point cloud data generation unit;

[0038] 702. Center coordinate calculation unit;

[0039] 703. Bar tracking unit;

[0040] 902. Computer equipment;

[0041] 904, Processor;

[0042] 906. Memory;

[0043] 908. Drive mechanism;

[0044] 910. Input / Output Module;

[0045] 912. Input devices;

[0046] 914. Output devices;

[0047] 916. Presentation equipment;

[0048] 918. Graphical User Interface;

[0049] 920. Network interface;

[0050] 922. Communication link;

[0051] 924. Communication bus. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments herein will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments herein, and not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this document.

[0053] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings herein are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0054] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0055] It should be noted that the bar tracking method described in this paper can be used in the field of bar production technology, as well as in fields such as intelligent manufacturing and mechanical equipment. This paper does not limit the application areas of the bar tracking method and device.

[0056] Figure 1 The diagram shown is a flowchart of a bar tracking method according to an embodiment of this paper, which specifically includes the following steps:

[0057] Step 101: Scan the bar material located on the collection platform in the detection area multiple times to generate point cloud data of the bar material. In this step, the bar material is scanned multiple times within a certain period using a scanning device. The bar material is placed on the collection platform in the detection area. The collection platform includes multiple chain conveyor segments and drive motors, and each chain conveyor segment can be composed of chains. Each chain conveyor segment moves under the action of the drive motor. By controlling the running speed and running direction of the chains in each chain conveyor segment, the movement of the bar material on the collection platform is achieved.

[0058] In some embodiments of this specification, at least one bundle of bars is conveyed on the collection platform, and each bundle of bars includes at least one bar. The loading side of the collection platform places each bundle of bars onto a chain conveyor via a hydraulically traversing support arm, and the unloading side of the collection platform allows the bars to enter the bundling chute by their own gravity, thereby completing the collection of the bars.

[0059] In this step, scanning the bar multiple times within a certain period using a scanning device involves continuously scanning the bar on the collection platform of the detection area multiple times within a certain time while keeping the scanning angle and scanning area constant. For example, scanning 10 times at equal time intervals within 100 milliseconds; or scanning 5 times at equal time intervals within 100 milliseconds, etc. Multiple scans yield point cloud data of the bar on the collection platform of the detection area. The collection platform can contain one or multiple bars; therefore, the generated point cloud data can represent the point cloud data of one bar on the collection platform or the point cloud data of multiple bars on the collection platform.

[0060] Step 102: Cluster the point cloud data to determine the different bars on the collection platform and the center coordinates of the bars. In this step, by performing cluster analysis on the point cloud data of the bars, the number of bars on the collection platform and the center coordinates of the bars can be determined. For details on the method for determining the center coordinates of the bars, please refer to [link to relevant documentation]. Figure 3 Description. The center coordinates of the bar can be used to identify the movement of the bar on the collection platform, or to identify whether a new bar has appeared on the collection platform.

[0061] Step 103: Track the center coordinates of the bar. In the embodiments of this specification, when the collection platform is in operation, the bar on the collection platform will move under the drive of the motor, thus causing displacement of the bar. Under normal circumstances, the displacement distance of the bar over a period of time can be determined according to the transport speed and time length of the chain conveyor of the collection platform. Therefore, the movement of a certain bar can be determined based on the center coordinates of the bar obtained by scanning at different times. In addition, it can also be determined whether a new bar has been placed on the loading side of the collection platform. This achieves bar tracking.

[0062] In some embodiments of this specification, bar tracking can be achieved using the Kalman filter algorithm. The Kalman filter tracking algorithm combines the prediction of the object with the actual scan data to continuously correct the estimation of the object's position or state, thereby achieving accurate tracking of the object.

[0063] First, based on the speed of the collecting platform chain, predict the position of the existing bars on the platform at the next moment. This prediction is based on the estimate obtained from the previous state estimation.

[0064] Furthermore, the point cloud data scanned by the scanning device at the next scanning moment is obtained, and this point cloud data is the observation value.

[0065] The predicted value is corrected based on the difference between the observed value and the predicted value. Specifically, the predicted value is adjusted using the difference. Therefore, the embodiments of this specification can more accurately estimate the position or state of the bar, thereby achieving bar tracking.

[0066] Figure 2 The diagram shown is a flowchart of a method for clustering and identifying point cloud data of bar stock according to an embodiment of this paper, which specifically includes the following steps:

[0067] Step 201: Calculate the distance between each point cloud and other point clouds in the point cloud data of the bar. In this step, the distance between each point cloud and other point clouds in the point cloud data of the bar is calculated separately. The Euclidean distance formula can be used to calculate the distance between each point cloud and other point clouds.

[0068] Step 202: Point clouds with a distance not exceeding a preset distance threshold are classified as a type of point cloud data.

[0069] This step determines a preset distance threshold based on at least one of the typical width range of the bar and the density of the point cloud data generated by scanning. In some embodiments of this specification, the distance between each coordinate in the point cloud data of the same bar and the coordinates of other point cloud data of the same bar will not differ significantly. For example, the point cloud data of the same bar may have 10 points located at the same horizontal position, each with corresponding coordinates. The distance between the two farthest points among these 10 points does not exceed the width range of the bar. However, the distance between coordinates of different point cloud data of different bars may be larger. Therefore, the embodiments in this paper determine the preset distance threshold based on the typical width range of the bar.

[0070] For example, when a bar is laid flat on the collection platform in the detection area, the width it occupies is generally between 30 cm and 150 cm. In the embodiments of this specification, the width occupied by the bar on the collection platform can also be between 30 cm and 80 cm, or between 30 cm and 150 cm, etc. This application does not limit the width range occupied by the bar when it is laid flat. In the embodiments of this specification, given the known conventional width range of the bar, after converting the coordinates of the point cloud data generated by the scanning device to the world coordinate system, the maximum width range of the bar is used as a preset distance threshold. For example, if the conventional width range of the bar is between 30 cm and 150 cm, then the preset distance threshold can be determined to be 150 cm. Furthermore, clustering is used to identify point clouds in the point cloud data of the bar whose distance from other point clouds does not exceed 150 cm as a class of point cloud data.

[0071] In some other embodiments of this specification, a preset distance threshold is determined based on the density of the point cloud data. The density of the point cloud data is related to the resolution of the scanning device; higher resolution results in higher point cloud data density, and vice versa. Based on the horizontal distribution density of the point cloud data, regions with significantly higher density than other regions are identified, and these regions are defined as the areas where the rods are located. The coordinate system of the scanning device is transformed into the world coordinate system, and the length range occupied by the rod-located region in the world coordinate system is calculated. The average length range occupied by all rods on the collection platform in the world coordinate system is taken as the preset distance threshold.

[0072] For example, observations show that the point cloud data of the bars is line laser data, with the following distribution characteristics: the point cloud data density is high at the actual placement location of the bars, and extremely low at idle locations on the collection platform. Therefore, the location of the bars can be determined based on the density distribution of the line laser data. Furthermore, after converting the coordinate system of the scanning equipment to the world coordinate system, a preset distance threshold is determined in the world coordinate system based on the number of identified bars and the length occupied by their respective locations.

[0073] Furthermore, point clouds with a distance not exceeding a preset distance threshold are classified as one type of point cloud data; point clouds with a distance exceeding a preset distance are classified as a different type of point cloud data.

[0074] Figure 3 The diagram shown is a flowchart of a method for determining the center coordinates of a bar according to an embodiment of this paper, which specifically includes the following steps:

[0075] Step 301: Calculate the average of the sum of the distances of each coordinate to all other coordinates in each type of point cloud data. In some embodiments of this specification, the distance of each coordinate to all other coordinates is calculated in each type of point cloud data. The distance can be calculated using the Euclidean distance algorithm, which will not be elaborated here. Specifically, if a type of point cloud data contains 5 coordinates, namely (x1, y1, z1), (x2, y2, z2), (x3, y3, z3), (x4, y4, z4), and (x5, y5, z5), then the distances d1, d2, d3, and d4 between each coordinate and the other four coordinates are calculated respectively. Further, the average of the sum of the distances between this coordinate and the other four coordinates is calculated. This average reflects the degree of deviation of this coordinate from the other coordinates in this type of point cloud data. The larger the average, the greater the deviation of this coordinate from the other coordinates in this type of point cloud data; conversely, the smaller the average, the smaller the deviation.

[0076] Step 302: Read the coordinate with the smallest average value and use it as the center coordinate of the bar.

[0077] Figure 4 The diagram shown is a flowchart of a method for tracking bar stock according to an embodiment of this paper, which specifically includes the following steps:

[0078] Step 401: Obtain the center coordinates of the bar on the collection platform at the previous scanning time, and use them as the first center coordinates. In some embodiments of this specification, the scanning device scans the bar on the collection platform at a certain frequency. Using the current scanning time as a standard, obtain the point cloud data of the bar on the collection platform scanned by the scanning device at the previous scanning time, and then... Figure 3 The steps in the process determine the center coordinates in the bar point cloud data and use them as the first center coordinates.

[0079] In some embodiments of this specification, the point cloud data of the rods collected on the collection platform at the previous scan time includes point cloud data of at least one rod. This point cloud data can be the point cloud data of a single rod or the point cloud data of multiple rods. Correspondingly, the center coordinates of the rods obtained in this step also include the center coordinates of at least one rod. These center coordinates can be the center coordinates of a single rod or the center coordinates of multiple rods. When the center coordinates are the center coordinates of multiple rods, the first center coordinates is a set of coordinates.

[0080] Step 402: Obtain the center coordinates of the bar on the collection platform at the current scanning moment, and use them as the second center coordinates. Similar to the method for determining the first center coordinates in step 401, obtain the point cloud data of the bar on the collection platform scanned by the scanning device at the current scanning moment, and then... Figure 3 The steps in the process determine the center coordinates of the bar point cloud data, which are then used as the second center coordinates. Because the bar moves on the collection platform while in operation, its position at the current scan time differs from its position on the collection platform at the previous scan time. Therefore, the first and second center coordinates differ in the world coordinate system. Specifically, the first and second center coordinates have a displacement difference in the x-coordinate direction of the world coordinate system, while remaining unchanged in the y-coordinate and z-coordinate directions.

[0081] Step 403: Determine whether the displacement difference between the first center coordinate and the second center coordinate is within a preset displacement range. In this step, the preset displacement can be determined based on the moving speed of the segmented chains in the multi-segment chain transport mechanism of the collection platform, the angular velocity of the drive motor, and the scanning frequency of the scanning device. For example, if the scanning device scans 10 times at equal time intervals within 100 milliseconds, and the scanning frequency of the scanning device is 10 milliseconds / scan, and the moving speed of the segmented chains is 0.2 cm / millisecond, then the interval between two scans by the scanning device is 10 milliseconds, and the distance the same rod moves between the two scan intervals on the collection platform is 2 cm. Therefore, the preset displacement can be determined to be 2 cm, or a value slightly greater than 2 cm.

[0082] Step 404: If yes, determine that the second center coordinate and the first center coordinate are the center coordinates of the same rod at different scanning times. In this embodiment of the specification, if multiple rods are placed on the collection platform, the interval between the rods is relatively large, and the interval is greater than the displacement distance of the rods on the collection platform per unit time. The unit time can be the interval between two consecutive scanning times of the scanning device, or it can be a multiple of the interval between two consecutive scanning times of the scanning device. For example, the unit time can be the interval between two consecutive scanning times of the scanning device, which is 10ms, or it can be a multiple of the interval, such as 20ms, 80ms, 1s, etc. Correspondingly, the preset displacement can be determined as 4 cm, 32 cm, etc.

[0083] Step 405: If not, determine that the first center coordinates and the second center coordinates are the center coordinates of different bars, and determine that a new bar has appeared on the collection platform. In some embodiments of this specification, if a new bar appears on the feeding side of the collection platform, the new bar will follow the material placement rules during placement and will maintain a preset distance from the currently placed bar closest to the top side on the collection platform. This preset distance is greater than the displacement of the bar per unit time. For example, the maximum displacement of the bar per unit time does not exceed 32 cm, and the distance between at least one existing bar on the collection platform and other adjacent bars is not less than 1 meter. Therefore, when the new bar is placed on the feeding side, it will be at least 1 meter away from the currently closest existing bar on the feeding side, thus ensuring that the distance between the new bar and the existing bar is more than 1 meter.

[0084] If the displacement difference between the first center coordinate and the second center coordinate between the previous scan time and the next scan time exceeds the preset displacement, it can be determined that the first center coordinate and the second center coordinate do not correspond to the same bar. If the interval between the two bars corresponding to the first center coordinate and the second center coordinate is large, it can be considered that a new bar has appeared on the current collection platform.

[0085] The embodiments in this manual can avoid material tracking problems caused by traditional instrument detection signal failure, rod slippage, jamming, and manual rejection, and can accurately realize real-time tracking of rods in the collection platform area. This lays the foundation for realizing full-process material tracking of rods, meets the new needs of manufacturers for information-based management and intelligent data analysis of the production process, and has excellent application value.

[0086] Figure 5 The diagram shown is a flowchart of a data calibration method according to an embodiment of this paper, which specifically includes the following steps:

[0087] Step 501: Place a standard bar on the collection platform in the testing area. In this step, the standard bar is a tool used to calibrate the coordinate system. Specifically, place the standard bar on one side of the collection platform, at which point there are no other bars on the collection platform.

[0088] Step 502: Scan the standard bar to generate initial point cloud data of the standard bar. In this step, the scanning device scans the standard bar on the collection platform at a frequency of 10 scans within a 10ms interval, generating point cloud data of the standard bar. This point cloud data is referred to as the initial point cloud data. The initial point cloud data is in the coordinate system of the scanning device, and since there is an angle between the scanning device and the horizontal direction of the collection platform, the initial point cloud data needs to be further converted into point cloud data in the world coordinate system.

[0089] In some other embodiments of this specification, the point cloud data of the collection platform can also be obtained by scanning the platform when no rods are placed in the detection area. The initial point cloud data is as follows: Figure 8A As can be seen, the initial point cloud data consists of discontinuous, sloping line segments, and there is a certain angle between these segments and the x-axis of the world coordinate system.

[0090] Step 503: Determine the angular offset between the scanning device and the detection area based on the angle between the initial point cloud data and the horizontal direction of the collection platform. In this step, the straight line formed by the initial point cloud data in step 502 is fitted using the least squares method, and the angle α between the straight line formed by the initial point cloud data and the horizontal direction is calculated. This angle α is the angular offset between the scanning device and the flower detection area. A schematic diagram of the angle formed between the initial point cloud data and the horizontal direction in the embodiments of this specification is shown below. Figure 8B As stated above.

[0091] Step 504: The initial point cloud data is calibrated based on the offset. The rod located on the collection platform in the detection area is scanned based on the calibrated data to generate point cloud data for the rod. In this step, the initial point cloud data is rotated by an angle α based on the angular offset calculated in step 503 to generate calibrated point cloud data, as shown below. Figure 8C As shown.

[0092] Figure 6 The diagram shown is a schematic of a bar tracking system according to an embodiment of this article. The system includes: a collection platform 601, a scanning device 602, and a control unit 603.

[0093] The collection platform 601 includes a multi-segment chain conveyor in operation. The chain conveyor consists of a drive motor (not shown in the figure) and multiple segmented chains 6011. At least one bar 604 is placed on top of the collection platform 601. Each segment of the chain conveyor can carry at least one bar.

[0094] The scanning device 602 is positioned directly above the collection platform in the test area to scan the horizontal extension of the collection platform and generate point cloud data of the bars above the conveyor on the collection platform.

[0095] The control unit 603 (not shown in the figure) is electrically connected to the scanning device 602 and is used to control the scanning device 602 to scan the rods 604 located on the collection platform 601 in the detection area multiple times, generating point cloud data of the rods 604. The control unit 603 can cluster and identify the point cloud data to determine the different rods on the collection platform and the center coordinates of the rods; and track the rods 604 according to the center coordinates of the rods.

[0096] like Figure 7 The diagram shown is a schematic representation of a bar tracking device according to an embodiment of this paper. The basic structure of the bar tracking device is illustrated in this figure. The functional units and modules can be implemented in software, or bar tracking can be achieved using general-purpose chips or specific chips. Specifically, the bar tracking device includes:

[0097] The point cloud data generation unit 701 is used to scan the rods located on the collection platform in the detection area multiple times to generate point cloud data of the rods;

[0098] The center coordinate calculation unit 702 is used to cluster and identify the point cloud data of the rod, and to determine the center coordinates of different rods on the collection platform.

[0099] The bar tracking unit 703 is used to track the bar according to the center coordinates of the bar.

[0100] This solution lays the foundation for achieving full-process material tracking of bar stock. It is highly reliable and adaptable, meeting the new needs of manufacturers for information-based management and intelligent data analysis of the production process.

[0101] Figure 8A The diagram illustrates an initial point cloud data set as described in this embodiment. The figure shows the initial point cloud data generated by the scanning device before data calibration. This initial point cloud data corresponds to the case where there are no rods on the collection platform.

[0102] Figure 8B The diagram illustrates the angle between initial point cloud data and the horizontal direction in one embodiment of this paper. The diagram shows the angle α between the line formed by fitting the initial point cloud data using the least squares method and the horizontal direction.

[0103] Figure 8CThe diagram shown is a schematic of point cloud data after data calibration according to an embodiment of this paper. Based on the angular offset calculated in step 503, the initial point cloud data is rotated by an angle α to generate the point cloud data after data calibration as shown in the figure.

[0104] like Figure 9 As shown, a computer device provided in this embodiment can perform the bar tracking method described herein. The computer device 902 may include one or more processors 904, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 902 may also include any memory 906 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 906 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 902. In one case, when the processor 904 executes associated instructions stored in any memory or combination of memories, the computer device 902 can perform any operation of the associated instructions. The computer device 902 also includes one or more drive mechanisms 909 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0105] Computer device 902 may also include an input / output module 910 (I / O) for receiving various inputs (via input device 912) and providing various outputs (via output device 914). A specific output mechanism may include a presentation device 916 and an associated graphical user interface (GUI) 918. In other embodiments, the input / output module 910 (I / O), input device 912, and output device 914 may be omitted, and the device may function solely as a computer device within a network. Computer device 902 may also include one or more network interfaces 920 for exchanging data with other devices via one or more communication links 923. One or more communication buses 924 couple the components described above together.

[0106] Communication link 923 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 923 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0107] Corresponding to Figures 1 to 5In addition to the methods described above, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described methods.

[0108] This embodiment also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the following: Figures 1 to 5 The method shown.

[0109] It should be understood that in the various embodiments of this document, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0110] It should also be understood that, in the embodiments herein, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0115] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] This document uses specific embodiments to illustrate the principles and implementation methods of this document. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this document. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this document. Therefore, the content of this specification should not be construed as a limitation of this document.

Claims

1. A method for tracking bar stock, characterized in that, The method includes: The rods located on the collection platform in the detection area are scanned multiple times to generate point cloud data of the rods; Clustering and identifying the point cloud data to determine different rods on the collection platform and the center coordinates of the rods, wherein clustering and identifying the point cloud data of the rods includes: calculating the distance between each point cloud in the point cloud data of the rods and other point clouds; and classifying point clouds whose distance does not exceed a preset distance threshold as a class of point cloud data. Determining the different rods on the collection platform and the center coordinates of the rods further includes: calculating the average of the sum of the distances of each coordinate to all other coordinates in each type of point cloud data; Read the coordinate with the smallest average value and use that coordinate as the center coordinate of the bar. Tracking the bar based on its center coordinates includes: acquiring the center coordinates of the bar on the collection platform at the previous scanning time, and using them as the first center coordinates; Obtain the center coordinates of the bar on the collection platform at the current scanning moment, and use them as the second center coordinates; Determine whether the displacement difference between the first center coordinate and the second center coordinate is within a preset displacement range; If so, determine that the second center coordinate and the first center coordinate are the center coordinates of the same bar at different scanning times; If not, determine that the first center coordinates and the second center coordinates are the center coordinates of different bars, and then determine that a new bar has appeared on the collection platform.

2. The bar tracking method according to claim 1, characterized in that, Before generating the point cloud data of the bar, the method further includes: A standard bar is placed on the collection rack in the testing area; Scan the standard bar to generate initial point cloud data of the standard bar; Based on the angle between the initial point cloud data and the horizontal direction of the collection platform, determine the angular offset between the scanning device and the detection area; The initial point cloud data is calibrated based on the offset, and the bar located on the collection platform in the detection area is scanned based on the data calibration to generate the point cloud data of the bar.

3. A bar tracking system, characterized in that, The system includes: A collection platform, comprising a multi-segment conveyor in operation, with bars placed on top of the conveyors; The scanning device is used to scan the length of the collection platform and generate point cloud data of the bars above the conveyor on the collection platform. The control unit, electrically connected to the scanning device, is used to scan the rods located on the collection platform in the detection area multiple times to generate point cloud data of the rods; to cluster and identify the point cloud data to determine different rods on the collection platform and the center coordinates of the rods; and to track the rods according to their center coordinates. The clustering and identification of the point cloud data includes: calculating the distance between each point cloud in the point cloud data of the rods and other point clouds; and classifying point clouds whose distance does not exceed a preset distance threshold as a single type of point cloud data.

4. A bar tracking device, characterized in that, The device includes: A point cloud data generation unit is used to scan the rods located on the collection platform in the detection area multiple times to generate point cloud data of the rods; The center coordinate calculation unit is used to cluster and identify the point cloud data of the rods. The clustering and identification of the point cloud data determines the different rods on the collection platform and the center coordinates of the rods. The clustering and identification of the point cloud data of the rods includes: calculating the distance between each point cloud in the point cloud data of the rods and other point clouds; and classifying point clouds whose distance does not exceed a preset distance threshold as a class of point cloud data. Determining the different rods on the collection platform and the center coordinates of the rods further includes: calculating the average of the sum of the distances of each coordinate to all other coordinates in each type of point cloud data; Read the coordinate with the smallest average value and use that coordinate as the center coordinate of the bar. A bar tracking unit is used to track the bar according to the center coordinates of the bar, and includes: acquiring the center coordinates of the bar on the collection platform at the previous scanning time as the first center coordinates; Obtain the center coordinates of the bar on the collection platform at the current scanning moment, and use them as the second center coordinates; Determine whether the displacement difference between the first center coordinate and the second center coordinate is within a preset displacement range; If so, determine that the second center coordinate and the first center coordinate are the center coordinates of the same bar at different scanning times; If not, determine that the first center coordinates and the second center coordinates are the center coordinates of different bars, and then determine that a new bar has appeared on the collection platform.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 2.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 2.

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