Method and device for monitoring thickness of copper bar in real time

Abstract

The application is suitable for the technical field of copper bar production monitoring, and particularly relates to a copper bar thickness real-time monitoring method and device, which comprises the following steps: dividing copper billets of each rolling pass to obtain a plurality of monitoring areas; obtaining a target monitoring area according to the priority of each monitoring area; obtaining the thickness of the target monitoring area of each rolling pass; adjusting the priority of each monitoring area of the next rolling pass according to the thickness of the target monitoring area of the current rolling pass; obtaining the first thickness change trajectory of a first monitoring point and the second thickness change trajectory of at least one second monitoring point of the copper billet in the multi-pass rolling process according to the thickness of the target monitoring area of each rolling pass; and adjusting the rolling parameters of each rolling pass according to the first thickness change trajectory and the second thickness change trajectory. Therefore, the copper bar thickness real-time monitoring method provided by the application is beneficial to solve the problem of the thickness out-of-tolerance response lag of the copper bar in the copper bar production process.

Description

Technical Field

[0001] This application belongs to the field of copper busbar production monitoring technology, and in particular relates to a method and device for real-time monitoring of copper busbar thickness. Background Technology

[0002] Copper busbar production uses high-purity electrolytic copper as raw material, and is formed through core processes such as smelting, continuous casting, rolling, and heat treatment. Among these processes, molten copper is continuously cast into preliminary copper billets through a continuous casting machine. The copper billets are then rolled multiple times to gradually process them into copper busbars of the required specifications and shapes. The key to rolling lies in the precise control of the dimensions and tolerances of the copper billets.

[0003] In existing technologies, the control of the rolling process largely relies on fixed values ​​or empirical parameters. When a dimensional deviation occurs in a rolling pass, it can easily accumulate or amplify in subsequent rolling passes. Furthermore, existing copper busbar production monitoring systems tend to overlook potential locations of out-of-tolerance risks, only responding after the dimensional deviation actually occurs, further exacerbating the blindness and lag in parameter adjustments. Therefore, current real-time monitoring of the copper busbar production process suffers from a lag in response to thickness deviations. Summary of the Invention

[0004] This application provides a method and apparatus for real-time monitoring of copper busbar thickness, which can solve the problem of delayed response to thickness deviations in the copper busbar production process.

[0005] In a first aspect, embodiments of this application provide a method for real-time monitoring of copper busbar thickness, including:

[0006] The copper billets in each rolling pass are divided into multiple monitoring areas;

[0007] The target monitoring area is obtained according to the priority of each monitoring area; wherein, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass;

[0008] Obtain the thickness of the target monitoring area for each rolling pass;

[0009] The priority of each monitoring area in the next rolling pass is adjusted according to the thickness of the target monitoring area in the current rolling pass.

[0010] Based on the thickness of the target monitoring area in each rolling pass, the first thickness change trajectory of the copper billet at the first monitoring point and the second thickness change trajectory at at least one second monitoring point are obtained during the multi-pass rolling process; wherein, the first monitoring point refers to the location in the target monitoring area where the thickness is unqualified, and the second monitoring point refers to the location in the target monitoring area where the thickness is qualified;

[0011] The rolling parameters for each rolling pass are adjusted according to the first thickness change trajectory and the second thickness change trajectory; wherein, the rolling parameters include the roll gap and / or rolling speed.

[0012] The technical solutions described in this application embodiment have at least the following technical effects:

[0013] The copper busbar thickness real-time monitoring method provided in this application involves dividing the copper billet of each rolling pass into multiple monitoring areas; obtaining a target monitoring area based on the priority of each monitoring area; acquiring the thickness of the target monitoring area of ​​each rolling pass; adjusting the priority of each monitoring area in the next rolling pass based on the thickness of the target monitoring area of ​​the current rolling pass; obtaining a first thickness change trajectory of the copper billet at a first monitoring point and a second thickness change trajectory of at least one second monitoring point during multi-pass rolling based on the thickness of the target monitoring area of ​​each rolling pass; and adjusting the rolling parameters of each rolling pass based on the first thickness change trajectory and the second thickness change trajectory. Therefore, the real-time copper busbar thickness monitoring method provided in this application realizes targeted allocation of monitoring resources, avoiding the resource waste of traditional uniform monitoring; through the target area transfer mechanism across passes, a continuous chain of thickness control is constructed, so that historical out-of-tolerance information is continuously tracked in subsequent passes; dynamically adjusting the priority of monitoring areas can adaptively identify high-risk areas, which helps to improve the timeliness of defect warning; by establishing the thickness change trajectory of the first monitoring point and the second monitoring point, data support is provided for parameter optimization; through multi-pass data correlation analysis and dynamic priority management, it is beneficial to shorten the response time of thickness out-of-tolerance, while improving thickness uniformity, which helps to solve the problem of lag and insufficient accuracy in thickness control caused by complex rolling deformation in copper busbar production.

[0014] In one possible implementation of the first aspect, before obtaining the target monitoring area according to the priority of each of the monitoring areas, the method further includes:

[0015] Obtain historical rolling data; wherein, the historical rolling data includes the copper billet mass corresponding to each rolling pass;

[0016] Based on the historical rolling data and each of the monitoring areas, areas with frequent historical anomalies are obtained;

[0017] The edge region is obtained based on the location of each monitoring area;

[0018] The priority of each monitoring area is determined based on the edge area and the historically frequent abnormal areas.

[0019] In one possible implementation of the first aspect, adjusting the priority of each monitoring region in the next rolling pass based on the thickness of the target monitoring region in the current rolling pass includes:

[0020] If the thickness of the target monitoring area in the current rolling pass is determined to be acceptable, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area in the current rolling pass is reduced.

[0021] If the thickness of the target monitoring area in the current rolling pass is found to be unqualified, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area of ​​the current rolling pass is increased.

[0022] In one possible implementation of the first aspect, obtaining the first thickness change trajectory of the copper billet at the first monitoring point and the second thickness change trajectory of at least one second monitoring point during multi-pass rolling based on the thickness of the target monitoring area of ​​each rolling pass further includes:

[0023] The monitoring points in the target monitoring area of ​​each rolling pass where the thickness gradient is greater than the gradient threshold or the thickness is not within the preset range are identified as the first monitoring points, and the other monitoring points are identified as the second monitoring points.

[0024] The thickness of the first monitoring point and the second monitoring point in each rolling pass is obtained based on the thickness of the target monitoring area;

[0025] Based on the thickness of the first monitoring point and the second monitoring point in each rolling pass, the first thickness change trajectory and the second thickness change trajectory are obtained by interpolation fitting.

[0026] In one possible implementation of the first aspect, obtaining the thicknesses of the first monitoring point and the second monitoring point in each rolling pass based on the thickness of the target monitoring area includes:

[0027] Obtain the initial dimensions of the copper billet before rolling and the real-time dimensions after each rolling pass; wherein the initial dimensions include length, width and thickness;

[0028] A global coordinate system and a volume conservation model are established based on the initial dimensions;

[0029] The position of the target monitoring area is converted into relative coordinates based on the global coordinate system, the volume conservation model, and the real-time dimensions; wherein, the relative coordinates refer to the percentage of distance from the head.

[0030] Update the relative positions of the first monitoring point and the second monitoring point according to the relative coordinates:

[0031] The thickness of the first monitoring point and the second monitoring point in each rolling pass is obtained based on the thickness of the target monitoring area and the relative position.

[0032] In one possible implementation of the first aspect, adjusting the rolling parameters for each rolling pass based on the first thickness variation trajectory and the second thickness variation trajectory includes:

[0033] The synchronization rate is obtained by performing cross-correlation analysis on the first thickness change trajectory and the second thickness change trajectory;

[0034] If it is determined that there is a monitoring point among the first monitoring points whose synchronization rate is less than the synchronization rate threshold, the monitoring point is identified as an abnormal monitoring point.

[0035] Multiple abnormal monitoring points are clustered to obtain abnormal regions;

[0036] The rolling parameters for each rolling pass are adjusted based on the third thickness change trajectory of the abnormal monitoring point and the abnormal region.

[0037] In one possible implementation of the first aspect, clustering the multiple anomaly monitoring points to obtain anomaly regions includes:

[0038] Multiple clusters are obtained by clustering based on the thickness gradient of each of the aforementioned abnormal monitoring points, and multiple region boundaries are calculated; wherein, the region boundaries are used to reflect the boundaries of the convex hulls and / or depressions on the copper billet surface;

[0039] If the thickness gradient change rate of the abnormal monitoring point in an adjacent rolling pass is greater than the change rate threshold, then the corresponding region boundary is expanded.

[0040] The abnormal region is obtained based on the expanded boundaries of each of the aforementioned regions.

[0041] In one possible implementation of the first aspect, adjusting the rolling parameters of each rolling pass based on the third thickness change trajectory of the abnormal monitoring point and the abnormal region includes:

[0042] Cluster analysis is performed on the third thickness variation trajectory to obtain the defect type; wherein, the defect type includes periodic fluctuation, overall shift and abrupt spike;

[0043] The rolling parameters for the corresponding rolling pass are adjusted according to the defect type and the location of the abnormal area.

[0044] In one possible implementation of the first aspect, the method further includes:

[0045] If the thickness of the target monitoring area in the current rolling pass is found to be unqualified, the rolling parameters for the next rolling pass are adjusted.

[0046] Secondly, embodiments of this application provide a real-time copper busbar thickness monitoring device, comprising:

[0047] The monitoring area module is used to divide the copper billet of each rolling pass into multiple monitoring areas;

[0048] The target monitoring area module is used to obtain the target monitoring area according to the priority of each monitoring area; wherein, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass;

[0049] The acquisition module is used to acquire the thickness of the target monitoring area for each rolling pass;

[0050] The first adjustment module is used to adjust the priority of each monitoring area in the next rolling pass according to the thickness of the target monitoring area in the current rolling pass;

[0051] The thickness change trajectory module is used to obtain the first thickness change trajectory of the first monitoring point and the second thickness change trajectory of at least one second monitoring point of the copper billet during the multi-pass rolling process based on the thickness of the target monitoring area of ​​each rolling pass; wherein, the first monitoring point refers to the position in the target monitoring area where the thickness is unqualified, and the second monitoring point refers to the position in the target monitoring area where the thickness is qualified.

[0052] The second adjustment module is used to adjust the rolling parameters of each rolling pass according to the first thickness change trajectory and the second thickness change trajectory; wherein the rolling parameters include the roll gap and / or rolling speed.

[0053] Thirdly, embodiments of this application provide a real-time copper busbar thickness monitoring 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 method as described in any one of the first aspects above.

[0054] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0055] Fifthly, embodiments of this application provide a computer program product that, when run on a copper busbar thickness real-time monitoring device, causes the copper busbar thickness real-time monitoring device to execute the method described in any one of the first aspects above.

[0056] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating a method for real-time monitoring of copper busbar thickness provided in an embodiment of this application;

[0059] Figure 2 This is a schematic diagram of the implementation process of steps S200 and S400 in the real-time copper busbar thickness monitoring method provided in an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of the implementation process of steps S500 and S520 in the real-time copper busbar thickness monitoring method provided in an embodiment of this application;

[0061] Figure 4 This is a schematic diagram of the implementation process of steps S600, S630 and S640 in the real-time copper busbar thickness monitoring method provided in an embodiment of this application;

[0062] Figure 5 This is a schematic diagram of the structure of the real-time copper busbar thickness monitoring device provided in the embodiments of this application;

[0063] Figure 6 This is a schematic diagram of the structure of the copper busbar thickness real-time monitoring device provided in the embodiments of this application. Detailed Implementation

[0064] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0065] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0066] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0067] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0068] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0070] In related technologies, the control of the rolling process often relies on fixed values ​​or empirical parameters. When a dimensional deviation occurs in a certain rolling pass, it can easily accumulate or amplify in subsequent rolling passes. Furthermore, existing copper busbar production monitoring systems tend to overlook potential locations of out-of-tolerance risks, only responding after the dimensional deviation actually occurs, further exacerbating the blindness and lag in parameter adjustments. Therefore, current real-time monitoring of the copper busbar production process suffers from a lag in response to thickness deviations.

[0071] To address the aforementioned problems, this application provides a method and apparatus for real-time monitoring of copper busbar thickness. The method involves dividing the copper billet in each rolling pass into multiple monitoring areas; obtaining a target monitoring area based on the priority of each monitoring area; acquiring the thickness of the target monitoring area for each rolling pass; adjusting the priority of each monitoring area in the next rolling pass based on the thickness of the target monitoring area in the current rolling pass; obtaining a first thickness change trajectory at a first monitoring point and a second thickness change trajectory at at least one second monitoring point during multi-pass rolling based on the thickness of the target monitoring area in each rolling pass; and adjusting the rolling parameters for each rolling pass based on the first and second thickness change trajectories. Therefore, the real-time copper busbar thickness monitoring method provided in this application realizes targeted allocation of monitoring resources, avoiding the resource waste of traditional uniform monitoring; through the cross-pass target area transfer mechanism, a continuous chain of thickness control is constructed, so that historical out-of-tolerance information is continuously tracked in subsequent passes; dynamically adjusting the priority of monitoring areas can adaptively identify high-risk areas, which helps to improve the timeliness of defect warning; by establishing the thickness change trajectory of the first monitoring point and the second monitoring point, data support is provided for parameter optimization; through multi-pass data correlation analysis and dynamic priority management, it is beneficial to shorten the response time of thickness out-of-tolerance, while improving thickness uniformity, which is beneficial to improving the problem of thickness control lag and insufficient accuracy caused by complex rolling deformation in copper busbar production.

[0072] The real-time copper busbar thickness monitoring method provided in this application embodiment can be applied to a real-time copper busbar thickness monitoring device. In this case, the real-time copper busbar thickness monitoring device is the executing entity of the real-time copper busbar thickness monitoring method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of real-time copper busbar thickness monitoring device.

[0073] For example, the copper busbar thickness real-time monitoring device is communicatively connected to multiple thickness sensors used to monitor the thickness of the rolled copper billet in each rolling pass. The thickness sensors can be laser through-beam sensors, ultrasonic thickness gauges, microwave thickness sensors, etc. The copper busbar thickness real-time monitoring device can be a tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, desktop computer, computing device or other processing device connected to a wireless modem, computer, laptop computer, handheld computing device, etc., but is not limited to these.

[0074] To better understand the real-time copper busbar thickness monitoring method provided in this application embodiment, the specific implementation process of the real-time copper busbar thickness monitoring method provided in this application embodiment will be described by way of example below.

[0075] Figure 1This paper illustrates a schematic flowchart of a real-time copper busbar thickness monitoring method provided in an embodiment of this application. The real-time copper busbar thickness monitoring method includes:

[0076] S100 divides the copper billet of each rolling pass into multiple monitoring areas.

[0077] For example, the copper billet of each rolling pass can be divided into multiple monitoring areas by gridding. For instance, the surface of the copper billet can be divided into N equal-width areas along the width direction (e.g., N=10, area width = copper billet width / N), and divided along the length direction at fixed intervals.

[0078] S200, the target monitoring area is obtained according to the priority of each monitoring area. Among them, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area of ​​the current rolling pass.

[0079] It is understood that the target monitoring area may include multiple monitoring areas. The target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass (this monitoring area is determined based on the relative coordinates of the copper billet for the next rolling pass and the current rolling pass).

[0080] For example, the monitoring areas of the current rolling pass can be sorted in descending order of priority, the first M monitoring areas (e.g., M=5) can be selected, and adjacent monitoring areas can be merged into one monitoring area to determine the target monitoring area.

[0081] In one possible implementation, please refer to Figure 2 S200, before obtaining the target monitoring area based on the priority of each monitoring area, the method further includes:

[0082] S210, Obtain historical rolling data. This historical rolling data includes the copper billet quality corresponding to each rolling pass.

[0083] For example, historical rolling data can be extracted from the MES system or database, which may include: the dimensions of the copper billet for each rolling pass (width, length, thickness, etc.), quality inspection results (location of surface defects, areas exceeding thickness limits, etc.) and rolling parameters (roll gap, rolling speed, etc.).

[0084] S220, based on historical rolling data and monitoring areas, identifies areas with frequent historical anomalies.

[0085] For example, the coordinates of surface defect locations in historical rolling data can be mapped to a monitoring area grid, and the defect data (including the number of times defects occur and the area of ​​defects) of each monitoring area in historical rolling data can be counted. The defect rate per unit area of ​​each monitoring area can be calculated, and the defect rate per unit area can be compared with the defect rate threshold to obtain the historically frequently occurring abnormal areas.

[0086] S230, the edge area is obtained based on the location of each monitoring area.

[0087] For example, based on the location of each monitoring area, the two sides of each monitoring area along the width direction of the copper billet (such as 0-200mm and 1800-2000mm of a 2000mm wide billet) can be determined as edge areas. If the unit area defect rate of a certain edge area is greater than the defect rate threshold (such as >30%) for multiple consecutive rolling passes, then the width is extended to the center (such as extended to the 15% area).

[0088] S240, the priority of each monitoring area is determined based on the edge area and the area with frequent historical anomalies.

[0089] For example, it can be determined whether each monitoring area contains edge areas and whether it is a historically frequent abnormal area. Based on the weight of the above two cases, the priority of each monitoring area is determined before each batch of copper busbar rolling production.

[0090] Through steps S210 to S240, potential risk areas can be identified in advance through historical data mining. Compared with traditional uniform detection schemes, this improves defect detection efficiency and shortens the response time for thickness exceeding limits. The dynamic expansion mechanism of the edge region enables the system to automatically adapt to process fluctuations in different batches of copper billets, avoiding missed detections caused by fixed boundaries. The dual-weight model overcomes the limitations of single-factor priority.

[0091] S300, obtain the thickness of the target monitoring area for each rolling pass.

[0092] For example, multiple monitoring areas can be obtained based on the copper billet of each rolling pass: the surface of the copper billet is divided into N equal-width areas along the width direction and into areas with fixed spacing along the length direction. The number of thickness sensors (such as laser beam sensors) to be deployed is determined based on the N equal-width areas. The sampling frequency (i.e., the number of monitoring points) of each monitoring area can be determined based on the priority of each monitoring area within the target monitoring area and the length of the monitoring area of ​​each rolling pass. The thickness of the copper billet in the target monitoring area is collected in real time according to the sampling frequency. After filtering, the average thickness of all monitoring points in each monitoring area within the target monitoring area is taken as the thickness of the target monitoring area.

[0093] S400 adjusts the priority of each monitoring area in the next rolling pass based on the thickness of the target monitoring area in the current rolling pass.

[0094] For example, the absolute deviation |Δh| of the thickness of each monitoring area within the target monitoring area from the target thickness (e.g., 2.0 mm) corresponding to the current rolling pass can be calculated. The priority of each monitoring area in the next rolling pass can be adjusted according to the absolute deviation. If the |Δh| of a certain monitoring area in the target monitoring area exceeds a preset threshold (e.g., >0.1 mm), the priority of its adjacent areas can be increased (e.g., +1 level).

[0095] In one possible implementation, please refer to Figure 2 S400, adjusts the priority of each monitoring area in the next rolling pass according to the thickness of the target monitoring area in the current rolling pass, including:

[0096] S410, if the thickness of the target monitoring area in the current rolling pass is determined to be acceptable, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area in the current rolling pass is reduced.

[0097] For example, a thickness range can be defined as: target thickness ± allowable deviation (e.g., target thickness 10mm, allowable deviation ±0.1mm → acceptable range [9.9, 10.1]mm). For the target monitoring area, thickness data is continuously collected multiple times during the rolling process (e.g., sampling 5 times for each monitoring area in the target monitoring area, with a sampling interval of 0.5 seconds). The mean and standard deviation of the thickness data are calculated. If the mean is within the acceptable range and the standard deviation is <0.05mm, the thickness is determined to be acceptable, and the priority of the monitoring area corresponding to the target monitoring area of ​​the current rolling pass in the next rolling pass is reduced. For example, the priority P of the next rolling pass... i,t+1 =α P i,t Where α is the priority attenuation coefficient (a configurable parameter, ranging from 0.5 to 0.9), P i,t It is the priority of the current rolling pass.

[0098] S420, if it is determined that the thickness of the target monitoring area in the current rolling pass is unqualified, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area of ​​the current rolling pass is increased.

[0099] For example, the thickness of the target monitoring area may be determined to be unqualified if the average thickness of the target monitoring area in the current rolling pass is greater than the upper limit of the thickness range (e.g., 10.2 mm), or the average thickness is less than the lower limit of the thickness range (e.g., 9.8 mm), or the standard deviation is greater than or equal to a preset threshold (e.g., 0.05 mm). If the thickness of the target monitoring area in the current rolling pass is determined to be unqualified, the severity of the unqualified condition is graded, and the priority of the monitoring area corresponding to the target monitoring area in the next rolling pass is increased. For example, the priority P of the next rolling pass... i,t+1 =β P i,t Where β is the priority boosting factor (a configurable parameter, ranging from 1.0 to 2.0), P i,t This is the priority of the current rolling pass. Slight deviation: the mean value exceeds the acceptable range by ±0.05mm → priority enhancement coefficient β=1.5; Severe deviation: the mean value exceeds the acceptable range by ±0.1mm or the standard deviation is ≥0.1mm → priority enhancement coefficient β=2.0.

[0100] Through steps S410 to S420, over-monitoring of stable areas is avoided by using attenuation coefficients, allowing monitoring resources to be concentrated on high-risk areas. Rapid downgrading of consecutive qualified passes allows the system to quickly "forget" stable areas and respond to newly emerging risks. Differentiated responses through severity grading enable timely intervention. Synchronous priority increases for adjacent areas can detect defect propagation trends early. Recorded priority adjustment history can be analyzed to identify patterns where "a certain monitoring area is prone to exceeding tolerances after rolling pass X," guiding pre-adjustment of rolling process parameters (e.g., reducing the roll gap in pass X+1 in advance).

[0101] S500: Based on the thickness of the target monitoring area in each rolling pass, the first thickness change trajectory of the copper billet at the first monitoring point and the second thickness change trajectory at at least one second monitoring point are obtained during the multi-pass rolling process. The first monitoring point refers to the location in the target monitoring area where the thickness is unacceptable, and the second monitoring point refers to the location in the target monitoring area where the thickness is acceptable.

[0102] It can be understood that the first monitoring point is a point whose thickness is outside the preset thickness range (e.g., [1.95mm, 2.05mm]). The second monitoring point is a point whose thickness is within the preset thickness range.

[0103] For example, the thickness values ​​of the first and second monitoring points in the target monitoring area of ​​each rolling pass can be stored, and time series curves can be plotted according to the rolling pass number to obtain the first and second thickness change trajectories. The first thickness change trajectory can be highlighted in red.

[0104] In one possible implementation, please refer to Figure 3 S500, based on the thickness of the target monitoring area of ​​each rolling pass, obtains the first thickness change trajectory of the copper billet at the first monitoring point and the second thickness change trajectory of at least one second monitoring point during multi-pass rolling, and further includes:

[0105] S510, the monitoring points in the target monitoring area of ​​each rolling pass where the thickness gradient is greater than the gradient threshold or the thickness is not within the preset range are determined as the first monitoring points, and the other monitoring points are determined as the second monitoring points.

[0106] For example, the thickness of each monitoring point in the target monitoring area can be calculated to obtain the thickness gradient (i.e., the maximum absolute value of the thickness difference). The monitoring points in the target monitoring area of ​​each rolling pass whose thickness gradient is greater than the gradient threshold or whose thickness is not within the preset range are determined as the first monitoring points, and the other monitoring points are determined as the second monitoring points.

[0107] S520, based on the thickness of the target monitoring area, obtain the thickness of the first and second monitoring points in each rolling pass.

[0108] For example, the relative coordinates and corresponding thickness of the first monitoring point in each rolling pass, and the relative coordinates and corresponding thickness of the second monitoring point in each rolling pass can be stored according to the thickness of the target monitoring area.

[0109] Optionally, please refer to Figure 3 S520, based on the thickness of the target monitoring area, the thickness of the first and second monitoring points in each rolling pass is obtained, including:

[0110] S521, obtain the initial dimensions of the copper billet before rolling and the real-time dimensions after each rolling pass. The initial dimensions include length, width, and thickness.

[0111] For example, the initial dimensions of the copper billet before rolling can be measured by a laser rangefinder or an ultrasonic sensor, and the real-time dimensions after each rolling pass can be determined in advance based on the initial dimensions and historical rolling data.

[0112] S522 establishes a global coordinate system and a volume conservation model based on the initial dimensions.

[0113] For example, a global coordinate system can be established with the head of the copper billet as the origin (0,0,0), the X-axis along the rolling direction, the Y-axis along the width direction, and the Z-axis along the thickness direction, based on the initial dimensions; and a volume conservation model can be determined based on the initial dimensions: V=LWH, where V is the volume, W is the width, and H is the thickness.

[0114] S523 converts the position of the target monitoring area into relative coordinates based on the global coordinate system, the volume conservation model, and real-time dimensions. The relative coordinates refer to the percentage of distance from the head.

[0115] For example, the proportion of the distance between the location of the target monitoring area and the copper billet head position (0,0,0) can be determined based on the global coordinate system, the volume conservation model, and the real-time dimensions, and the location of the target monitoring area can be converted into relative coordinates.

[0116] S524, Update the relative positions of the first and second monitoring points based on their relative coordinates:

[0117] For example, the relative positions of the first and second monitoring points in each rolling pass can be updated according to the relative coordinates, and the sampling position of the thickness sensor can be determined according to the relative position.

[0118] S525, the thickness of the first and second monitoring points in each rolling pass is obtained based on the thickness and relative position of the target monitoring area.

[0119] For example, the thickness of the first and second monitoring points in each rolling pass can be determined based on the relative position of the target monitoring area and the thickness of the target monitoring area obtained by the thickness sensor according to the sampling position.

[0120] Through steps S521 to S525, the relative coordinate transformation makes the defect location of the copper billet continuously traceable during multi-pass rolling. Dynamically adjusting the monitoring point position can reduce sensor redundancy and lower costs.

[0121] S530, based on the thickness of the first monitoring point and the second monitoring point in each rolling pass, interpolation fitting is used to obtain the first thickness change trajectory and the second thickness change trajectory.

[0122] For example, the thickness of the first and second monitoring points in each rolling pass can be sorted by timestamp. For the first monitoring point, a cubic spline interpolation trajectory is used to fit the trajectory, preserving local fluctuation characteristics. For the second monitoring point, a quadratic polynomial fitting is used. If a monitoring point has missing data in some passes (such as sensor failure), then only the valid pass segments are fitted.

[0123] Through steps S510 to S530 above, the dual threshold precisely locates the defect initiation point, and spline interpolation can capture changes in the defect evolution rate (such as accelerated thickness increase). The polynomial fitting trajectory of the second monitoring point can serve as a reference line for process stability (e.g., a slope of P(x) < 0.01 mm / pass is considered stable). By calculating the deviation between the first and second thickness change trajectories (e.g., root mean square error RMSE), the degree of process fluctuation is quantified. Based on the first and second thickness change trajectories for each rolling pass, the defect propagation path can be analyzed.

[0124] S600 adjusts the rolling parameters for each rolling pass based on the first and second thickness variation trajectories. These rolling parameters include the roll gap and / or rolling speed.

[0125] For example, the presence of over-rolling / under-rolling issues can be determined based on the first thickness change trajectory. If over-rolling / under-rolling is determined, the roll gap is increased or decreased accordingly based on the average thickness difference between the first monitoring point and the target thickness of the corresponding rolling pass. The rolling speed can be determined based on the average thickness difference and the adjustment amount of the roll gap. The presence of periodic thickness changes can be determined based on the first and second thickness change trajectories. If periodic thickness changes are determined, the thickness fluctuation period is obtained based on the first and second thickness change trajectories, and the rolling speed is adjusted based on the thickness change period.

[0126] In one possible implementation, please refer to Figure 4 S600, adjusts the rolling parameters for each rolling pass according to the first and second thickness change trajectories, including:

[0127] S610, perform cross-correlation analysis on the first thickness change trajectory and the second thickness change trajectory to obtain the synchronization rate.

[0128] For example, the first thickness variation trajectory (P1) and the second thickness variation trajectory (P2) can be denoised (e.g., by moving average filtering). If the sampling frequencies of the first thickness variation trajectory (P1) and the second thickness variation trajectory (P2) are different, the data can be aligned to the same time point using interpolation (e.g., cubic spline interpolation), and then cross-correlation analysis can be performed to obtain the synchronization rate. Among them, the cross-correlation coefficient R 12 (τ) represents the similarity between P1 and P2 at time shift τ. P1 and P2 The mean thickness is given, N is the number of sampling points, and then the peak value of the cross-correlation function R is calculated. max =max(∣R 12 (τ)∣), the synchronization rate is S=R max ×100%, if S≥90%, the two trajectories are considered to be synchronous; if S<90%, then they are asynchronous.

[0129] S620, if it is determined that there is a monitoring point in the first monitoring point whose synchronization rate is less than the synchronization rate threshold, the monitoring point is identified as an abnormal monitoring point.

[0130] For example, if it is determined that there are monitoring points in the first monitoring point whose synchronization rate is less than the synchronization rate threshold (e.g., 90%), the monitoring point can be identified as an abnormal monitoring point.

[0131] S630 clusters multiple abnormal monitoring points to obtain abnormal regions.

[0132] For example, the abnormal regions can be clustered using the DBSCAN clustering method based on the coordinates of each abnormal monitoring point.

[0133] Optionally, please refer to Figure 4 S630 clusters multiple anomaly monitoring points to obtain anomaly regions, including:

[0134] S631, multiple clusters are obtained by clustering based on the thickness gradient of each abnormal monitoring point, and multiple region boundaries are calculated. Among them, the region boundaries are used to reflect the boundaries of the bulges and / or depressions on the copper billet surface.

[0135] For example, for each anomaly monitoring point Pi, its thickness gradient (Gi) can be calculated based on the thickness and spacing of adjacent anomaly monitoring points. Multiple clusters can be obtained based on the thickness gradient (Gi), and multiple region boundaries can be calculated. For example, if Gi > 0 (convexity) or Gi < 0 (concaveness), clustering is performed respectively, and clusters with the same gradient direction and spatial adjacency are merged (such as merging convex clusters with adjacent convex clusters). For each cluster, algorithms such as Alpha Shapes are used to generate region boundaries.

[0136] S632, if the thickness gradient change rate of an abnormal monitoring point in an adjacent rolling pass is greater than the change rate threshold, then the corresponding region boundary is expanded.

[0137] For example, for each abnormal monitoring point Pi, its gradient change rate in adjacent passes (such as the nth pass and the (n+1th pass) can be calculated. If the thickness gradient change rate of an abnormal monitoring point in an adjacent rolling pass is greater than the change rate threshold, a new region boundary is obtained by extending a certain boundary distance in the normal direction with Pi as the center.

[0138] S633, the abnormal region is obtained based on the expanded boundaries of each region.

[0139] For example, B-spline interpolation can be used to obtain the corresponding continuous boundaries of each expanded region. If there is self-intersection (such as the intersection of convex hull boundary and concave boundary), it is divided into multiple sub-regions to obtain the abnormal region.

[0140] Through the above steps S631 to S633, from gradient clustering to boundary expansion, the dynamic growth process of convex hull / concave is tracked, improving rolling stability and multi-scale adaptability, and supporting full-scale analysis from micro-gradients to macro-regions.

[0141] S640 adjusts the rolling parameters of each rolling pass based on the third thickness change trajectory and abnormal area of ​​the abnormal monitoring point.

[0142] For example, the mean deviation (ΔH) between the third thickness change trajectory of the abnormal monitoring point and the normal thickness range can be calculated. The roll gap can be adjusted according to the mean deviation (ΔH). For example, if ΔH > 0 (thickness is too large), the roll gap of the next pass can be reduced by Δd = −k. ΔH (k is a proportionality coefficient, such as 0.5). Furthermore, the rolling speed can be adjusted accordingly based on the location of the abnormal area, the mean deviation, and the adjustment amount of the roll gap.

[0143] Through steps S610 to S640 above, closed-loop control based on real-time thickness trajectory enables dynamic optimization of rolling parameters. Precise compensation in abnormal areas reduces scrap rate, thus helping to improve rolling efficiency and lower the scrap rate.

[0144] Optionally, please refer to Figure 4 S640, adjust the rolling parameters of each rolling pass according to the third thickness change trajectory and abnormal area of ​​the abnormal monitoring point, including:

[0145] S641, cluster analysis was performed on the third thickness variation trajectory to obtain the defect types. Among them, the defect types include periodic fluctuations, overall shifts, and abrupt spikes.

[0146] For example, the autocorrelation coefficient, linear regression slope, and second difference can be calculated based on multiple third thickness variation trajectories, and compared with the corresponding thresholds to determine whether the defect type is periodic fluctuation, overall shift, or abrupt spike.

[0147] S642, adjust the rolling parameters of the corresponding rolling pass according to the defect type and the location of the abnormal area.

[0148] For example, the rolling parameters of the corresponding rolling pass can be adjusted according to the defect type and the location of the abnormal area. For instance, if the defect type of a certain abnormal area is periodic fluctuation, then the roll gap and rolling speed can be adjusted according to the average fluctuation amplitude and fluctuation frequency of the abnormal area. The roll gap adjustment amount = a × fluctuation amplitude, and the rolling speed adjustment amount = b × fluctuation frequency, where a and b are adjustment coefficients, such as a = -0.05 and b = -0.1.

[0149] Through the above steps S641 to S642, full automation is achieved from defect detection to parameter adjustment, while adjusting the roll gap and speed, thus overcoming the limitations of single-parameter control.

[0150] In one possible implementation, please refer to Figure 2 The methods also include:

[0151] S001, if the thickness of the target monitoring area in the current rolling pass is found to be unqualified, adjust the rolling parameters for the next rolling pass.

[0152] For example, if the thickness of the target monitoring area of ​​the current rolling pass is found to be unqualified based on the average thickness deviation of the target monitoring area of ​​the current rolling pass, a correlation model (such as support vector regression model, multilayer perceptron model, long short-term memory network model, etc.) between the thickness of each rolling pass and the roll gap and rolling speed can be established based on historical rolling data. The roll gap and / or rolling speed of the next rolling pass can be pre-adjusted based on the correlation model and the average thickness deviation of the target monitoring area of ​​the current rolling pass.

[0153] Through the above steps S001, the "detection-diagnosis-compensation" of thickness defects is fully automated through real-time monitoring and dynamic adjustment. Pre-compensation is performed based on the current trend of thickness defects, which helps to improve production efficiency and product quality.

[0154] It should be understood that the sequence number of each step in the above embodiments 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 application.

[0155] Corresponding to the copper busbar thickness real-time monitoring method described in the above embodiments, this application also provides a copper busbar thickness real-time monitoring device, the various modules of which can realize the various steps of the copper busbar thickness real-time monitoring method. Figure 5 A structural block diagram of the copper busbar thickness real-time monitoring device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0156] Reference Figure 5 The device includes:

[0157] The monitoring area module is used to divide the copper billet of each rolling pass into multiple monitoring areas;

[0158] The target monitoring area module is used to obtain the target monitoring area according to the priority of each monitoring area; wherein, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass;

[0159] The acquisition module is used to acquire the thickness of the target monitoring area for each rolling pass;

[0160] The first adjustment module is used to adjust the priority of each monitoring area in the next rolling pass according to the thickness of the target monitoring area in the current rolling pass;

[0161] The thickness change trajectory module is used to obtain the first thickness change trajectory of the first monitoring point and the second thickness change trajectory of at least one second monitoring point of the copper billet during the multi-pass rolling process based on the thickness of the target monitoring area of ​​each rolling pass; wherein, the first monitoring point refers to the position in the target monitoring area where the thickness is unqualified, and the second monitoring point refers to the position in the target monitoring area where the thickness is qualified.

[0162] The second adjustment module is used to adjust the rolling parameters of each rolling pass according to the first thickness change trajectory and the second thickness change trajectory; wherein the rolling parameters include the roll gap and / or rolling speed.

[0163] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0164] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0165] This application also provides a copper busbar thickness real-time monitoring device. Figure 6 This is a schematic diagram of the structure of a real-time copper busbar thickness monitoring device provided in one embodiment of this application. Figure 6 As shown, the copper busbar thickness real-time monitoring device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown in the image), at least one memory 61 ( Figure 6 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the copper busbar thickness real-time monitoring device 6 to implement the steps in any of the above embodiments of the copper busbar thickness real-time monitoring method, or causes the copper busbar thickness real-time monitoring device 6 to implement the functions of each module / unit in the above embodiments of the apparatus.

[0166] For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 62 in the copper busbar thickness real-time monitoring device 6.

[0167] The copper busbar thickness real-time monitoring device 6 can be a desktop computer, laptop, handheld computer, or other computing device. This real-time copper busbar thickness monitoring device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of the copper busbar thickness real-time monitoring device 6 and does not constitute a limitation on the copper busbar thickness real-time monitoring device 6. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0168] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0169] In some embodiments, the memory 61 can be an internal storage unit of the copper busbar thickness real-time monitoring device 6, such as a hard disk or memory of the copper busbar thickness real-time monitoring device 6. In other embodiments, the memory 61 can also be an external storage device of the copper busbar thickness real-time monitoring device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the copper busbar thickness real-time monitoring device 6. Further, the memory 61 can include both internal storage units and external storage devices of the copper busbar thickness real-time monitoring device 6. The memory 61 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0170] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0171] This application provides a computer program product that, when run on a copper busbar thickness real-time monitoring device, enables the copper busbar thickness real-time monitoring device to implement the steps in any of the above method embodiments.

[0172] 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, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the copper busbar thickness real-time monitoring device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0173] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0174] 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, or a combination of computer software and electronic hardware. 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 application.

[0175] In the embodiments provided in this application, it should be understood that the disclosed copper busbar thickness real-time monitoring device and method can be implemented in other ways. For example, the copper busbar thickness real-time monitoring device embodiments described above are merely illustrative. For instance, the division of modules or 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 displayed or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0176] 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 this embodiment according to actual needs.

[0177] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for real-time monitoring of copper busbar thickness, characterized in that, include: The copper billets in each rolling pass are divided into multiple monitoring areas; The target monitoring area is obtained according to the priority of each monitoring area; wherein, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass; Obtain the thickness of the target monitoring area for each rolling pass; The priority of each monitoring area in the next rolling pass is adjusted according to the thickness of the target monitoring area in the current rolling pass. The process of obtaining a first thickness change trajectory of a first monitoring point and a second thickness change trajectory of at least one second monitoring point during multi-pass rolling of a copper billet is based on the thickness of the target monitoring area in each rolling pass. This includes: identifying monitoring points in the target monitoring area of ​​each rolling pass where the thickness gradient is greater than a gradient threshold or the thickness is outside a preset range as first monitoring points, and identifying other monitoring points as second monitoring points; obtaining the thickness of the first and second monitoring points in each rolling pass based on the thickness of the target monitoring area; and interpolating and fitting the first and second thickness change trajectories based on the thickness of the first and second monitoring points in each rolling pass. Wherein, the first monitoring point refers to a location in the target monitoring area where the thickness is unqualified, and the second monitoring point refers to a location in the target monitoring area where the thickness is qualified. The rolling parameters for each rolling pass are adjusted according to the first thickness change trajectory and the second thickness change trajectory; wherein, the rolling parameters include the roll gap and / or rolling speed.

2. The method for real-time monitoring of copper busbar thickness as described in claim 1, characterized in that, Before obtaining the target monitoring area based on the priority of each monitoring area, the method further includes: Obtain historical rolling data; wherein, the historical rolling data includes the copper billet mass corresponding to each rolling pass; Based on the historical rolling data and each of the monitoring areas, areas with frequent historical anomalies are obtained; The edge region is obtained based on the location of each monitoring area; The priority of each monitoring area is determined based on the edge area and the historically frequent abnormal areas.

3. The method for real-time monitoring of copper busbar thickness as described in claim 1, characterized in that, The step of adjusting the priority of each monitoring region in the next rolling pass based on the thickness of the target monitoring region in the current rolling pass includes: If the thickness of the target monitoring area in the current rolling pass is determined to be acceptable, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area in the current rolling pass is reduced. If the thickness of the target monitoring area in the current rolling pass is found to be unqualified, the priority of the monitoring area in the next rolling pass corresponding to the target monitoring area of ​​the current rolling pass is increased.

4. The method for real-time monitoring of copper busbar thickness as described in claim 1, characterized in that, The step of obtaining the thickness of the first monitoring point and the second monitoring point in each rolling pass based on the thickness of the target monitoring area includes: Obtain the initial dimensions of the copper billet before rolling and the real-time dimensions after each rolling pass; wherein the initial dimensions include length, width and thickness; A global coordinate system and a volume conservation model are established based on the initial dimensions; The position of the target monitoring area is converted into relative coordinates based on the global coordinate system, the volume conservation model, and the real-time dimensions; wherein, the relative coordinates refer to the percentage of distance from the head. Update the relative positions of the first monitoring point and the second monitoring point according to the relative coordinates: The thickness of the first monitoring point and the second monitoring point in each rolling pass is obtained based on the thickness of the target monitoring area and the relative position.

5. The method for real-time monitoring of copper busbar thickness as described in claim 1, characterized in that, The adjustment of rolling parameters for each rolling pass based on the first thickness change trajectory and the second thickness change trajectory includes: The synchronization rate is obtained by performing cross-correlation analysis on the first thickness change trajectory and the second thickness change trajectory; If it is determined that there is a monitoring point among the first monitoring points whose synchronization rate is less than the synchronization rate threshold, the monitoring point is identified as an abnormal monitoring point. Multiple abnormal monitoring points are clustered to obtain abnormal regions; The rolling parameters for each rolling pass are adjusted based on the third thickness change trajectory of the abnormal monitoring point and the abnormal region.

6. The method for real-time monitoring of copper busbar thickness as described in claim 5, characterized in that, The step of clustering multiple anomaly monitoring points to obtain anomaly regions includes: Multiple clusters are obtained by clustering based on the thickness gradient of each of the aforementioned abnormal monitoring points, and multiple region boundaries are calculated; wherein, the region boundaries are used to reflect the boundaries of the convex hulls and / or depressions on the copper billet surface; If the thickness gradient change rate of the abnormal monitoring point in an adjacent rolling pass is greater than the change rate threshold, then the corresponding region boundary is expanded. The abnormal region is obtained based on the expanded boundaries of each of the aforementioned regions.

7. The method for real-time monitoring of copper busbar thickness as described in claim 5, characterized in that, The step of adjusting the rolling parameters of each rolling pass based on the third thickness change trajectory of the abnormal monitoring point and the abnormal region includes: Cluster analysis is performed on the third thickness variation trajectory to obtain the defect type; wherein, the defect type includes periodic fluctuation, overall shift and abrupt spike; The rolling parameters for the corresponding rolling pass are adjusted according to the defect type and the location of the abnormal area.

8. The method for real-time monitoring of copper busbar thickness as described in claim 1, characterized in that, The method further includes: If the thickness of the target monitoring area in the current rolling pass is found to be unqualified, the rolling parameters for the next rolling pass are adjusted.

9. A real-time copper busbar thickness monitoring device, characterized in that, include: The monitoring area module is used to divide the copper billet of each rolling pass into multiple monitoring areas; The target monitoring area module is used to obtain the target monitoring area according to the priority of each monitoring area; wherein, the target monitoring area for the next rolling pass includes the monitoring area corresponding to the target monitoring area for the current rolling pass; The acquisition module is used to acquire the thickness of the target monitoring area for each rolling pass; The first adjustment module is used to adjust the priority of each monitoring area in the next rolling pass according to the thickness of the target monitoring area in the current rolling pass; The thickness variation trajectory module is used to obtain a first thickness variation trajectory of a first monitoring point and a second thickness variation trajectory of at least one second monitoring point during the multi-pass rolling process of a copper billet, based on the thickness of the target monitoring area in each rolling pass. The module includes: identifying monitoring points in the target monitoring area of ​​each rolling pass where the thickness gradient is greater than a gradient threshold or the thickness is outside a preset range as first monitoring points, and identifying other monitoring points as second monitoring points; obtaining the thickness of the first and second monitoring points in each rolling pass based on the thickness of the target monitoring area; and interpolating and fitting the first and second thickness variation trajectories based on the thickness of the first and second monitoring points in each rolling pass. The first monitoring point refers to a location in the target monitoring area where the thickness is unqualified, and the second monitoring point refers to a location in the target monitoring area where the thickness is qualified. The second adjustment module is used to adjust the rolling parameters of each rolling pass according to the first thickness change trajectory and the second thickness change trajectory; wherein the rolling parameters include the roll gap and / or rolling speed.

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