A data processing method for laser thickness measurement of strip steel

Abstract

This application provides a data processing method for laser thickness measurement of strip steel. The method includes: acquiring initial laser thickness measurement data of strip steel; filtering and removing outliers and edge data of the strip steel to obtain preprocessed data; performing a first filtering on the preprocessed data to extract surface roughness data to obtain first-filtered data; performing a second filtering on the first-filtered data to extract waviness data to obtain second-filtered data; and calculating the second-filtered data according to at least one preset profile index to output data corresponding to at least one profile index. The technical solution provided by this application reduces the fluctuation of laser thickness measurement data to a certain extent.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a data processing method for laser thickness measurement of strip steel. Background Technology

[0002] For strip steel samples taken from the rolling mill, thickness is typically measured using a micrometer, which is inefficient. While laser sensors offer high efficiency and speed for thickness detection, the data fluctuates significantly due to variations in sheet quality, making them unsuitable as a direct basis for determining dimensional accuracy.

[0003] Therefore, those skilled in the art urgently need a data processing method for laser thickness measurement of strip steel in order to reduce the fluctuation of laser thickness measurement data to a certain extent. Summary of the Invention

[0004] The embodiments of this application provide a data processing method for laser thickness measurement of strip steel, thereby reducing the fluctuation of laser thickness measurement data to at least a certain extent.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a data processing method for laser thickness measurement of strip steel is provided. The method includes: acquiring initial laser thickness measurement data of strip steel, filtering and removing data anomalies and edge data of strip steel to obtain preprocessed data; performing a first filtering on the preprocessed data to extract surface roughness data to obtain first-filtered data; performing a second filtering on the first-filtered data to extract waviness data to obtain second-filtered data; calculating the second-filtered data according to at least one preset profile index, and outputting data corresponding to at least one profile index.

[0007] In some embodiments of this application, the process of filtering and removing data anomalies includes: calculating the average thickness of the strip based on the initial laser thickness measurement data; calculating the ratio of the initial laser thickness measurement data to the average thickness of the strip to obtain the fluctuation amplitude of each data point; and filtering and removing data anomalies based on the distribution pattern of the fluctuation amplitude.

[0008] In some embodiments of this application, the step of filtering and removing data outliers based on the distribution pattern of fluctuation amplitude includes: filtering and removing data outliers based on the distribution pattern of fluctuation amplitude according to the 3 sigma principle.

[0009] In some embodiments of this application, the step of filtering and removing strip edge data includes: calculating the average thickness of the strip based on the initial laser thickness measurement data; selecting a target strip edge data removal rule based on the average strip thickness; and filtering and removing strip edge data based on the target strip edge data removal rule.

[0010] In some embodiments of this application, the step of performing a first filtering on the preprocessed data to extract surface roughness data and obtain first-filtered data includes: performing filtering analysis on the preprocessed data using Gaussian filtering, extracting and separating the surface roughness data, and independently saving the surface roughness data to obtain first-filtered data.

[0011] In some embodiments of this application, the step of performing secondary filtering on the primary filtered data to extract waviness data and obtain secondary filtered data includes: using FIR filtering to perform filtering analysis on the primary filtered data, extracting and separating the waviness data of the strip, and independently saving the waviness data to obtain secondary filtered data.

[0012] In some embodiments of this application, the strip profile parameters include: thickness at each point from the transmission side to the operating side, strip crown, and strip edge drop.

[0013] According to one aspect of this application, a data processing device for laser thickness measurement of strip steel is provided. The device includes: an acquisition unit, configured to acquire initial laser thickness measurement data of strip steel, filter and remove data anomalies and strip edge data to obtain preprocessed data; a first filtering unit, configured to perform a first filtering on the preprocessed data to extract surface roughness data to obtain first-filtered data; a second filtering unit, configured to perform a second filtering on the first-filtered data to extract waviness data to obtain second-filtered data; and a calculation unit, configured to calculate the second-filtered data according to at least one preset profile index and output data corresponding to at least one profile index.

[0014] According to one aspect of this application, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the data processing method for laser thickness measurement of strip steel as described above.

[0015] According to one aspect of this application, an electronic device is provided, characterized in that the electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to perform the operations performed by the data processing method for laser thickness measurement of strip steel as described above. Based on the above scheme, this application has at least the following advantages or progresses:

[0016] In some embodiments of this application, the technical solutions provided utilize the thickness measurement information of the laser sensor to the maximum extent by filtering and noise reduction twice, separates and saves all thickness data information, avoids data distortion caused by the quality of the plate surface in laser thickness measurement, and realizes the use of laser thickness measurement for dimensional accuracy determination.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0020] In the attached diagram:

[0021] Figure 1 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown;

[0022] Figure 2 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown;

[0023] Figure 3 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown;

[0024] Figure 4 A schematic diagram of the strip thickness according to an embodiment of this application is shown;

[0025] Figure 5 A simplified diagram of the Gaussian filtering principle according to one embodiment of this application is shown;

[0026] Figure 6 A simplified diagram of the FIR filtering principle according to one embodiment of this application is shown;

[0027] Figure 7 A comparison chart of quantitative indicators of plate profile is shown in one embodiment of this application;

[0028] Figure 8 A diagram of the processed data interface according to one embodiment of this application is shown;

[0029] Figure 9A comparison graph of secondary filtered data and initial laser thickness measurement data according to one embodiment of this application is shown;

[0030] Figure 10 A simplified structural diagram of a data processing apparatus for laser thickness measurement of strip steel according to one embodiment of this application is shown;

[0031] Figure 11 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0033] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0034] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0035] Please see Figure 1 .

[0036] Figure 1 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown, the method including steps S101-S104:

[0037] Step S101: Obtain initial laser thickness measurement data of the strip, filter and remove abnormal data points and strip edge data to obtain preprocessed data.

[0038] Step S102: Filter the preprocessed data once to extract surface roughness data and obtain filtered data.

[0039] Step S103: Perform secondary filtering on the primary filtered data to extract ripple data and obtain secondary filtered data.

[0040] Step S104: Calculate the secondary filtered data according to at least one preset profile index, and output the data corresponding to at least one profile index.

[0041] In this application, by filtering and noise reduction twice, the thickness measurement information of the laser sensor is utilized to the maximum extent, all thickness data information is separated and saved, and the data results of laser thickness measurement are avoided due to the quality of the plate surface. This enables the use of laser thickness measurement for dimensional accuracy determination.

[0042] Please see Figure 2 .

[0043] Figure 2 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown. The method for filtering and removing outliers may include steps S201-S203.

[0044] Step S201: Calculate the average thickness of the strip based on the initial laser thickness measurement data.

[0045] Step S202: Calculate the ratio of the initial laser thickness measurement data to the average thickness of the strip steel to obtain the fluctuation amplitude of each data point.

[0046] Step S203: Based on the distribution pattern of fluctuation amplitude, filter and remove data outliers.

[0047] In this application, a data processing program can first be developed based on Python to save the raw data of the strip thickness obtained by laser measurement to an Excel file and then read it using the Python program to automatically extract the initial data of the laser thickness measurement. Next, the ratio of the initial laser thickness measurement data to the average thickness of the strip is calculated to obtain the fluctuation amplitude of each data point, and outliers are removed based on the fluctuation amplitude.

[0048] In this embodiment, the method for filtering and removing data outliers based on the distribution pattern of fluctuation amplitude may include: filtering and removing data outliers according to the 3 sigma principle based on the distribution pattern of fluctuation amplitude.

[0049] Please see Figures 3-4 .

[0050] Figure 3 A flowchart of a data processing method for laser thickness measurement of strip steel according to an embodiment of this application is shown. The method for filtering and removing data from the edges of the strip steel may include steps S301-S303:

[0051] Step S301: Calculate the average thickness of the strip based on the initial laser thickness measurement data.

[0052] Step S302: Select the target strip edge data removal rule based on the average thickness of the strip.

[0053] Step S303: Based on the target strip edge data removal rules, filter and remove strip edge data.

[0054] Figure 4 A schematic diagram of the strip thickness according to one embodiment of this application is shown, as follows: Figure 4 As shown, considering that the edges of the strip are arc-shaped after rolling, and due to the action of the edge trimmer, the data from the strip edges has low reference value. Therefore, the following deletion principle can be established:

[0055] Assuming the outer edge arc is a semicircle with a diameter of h, then at least h / 2 of the strip width on each side should be removed. To ensure sufficient removal, a further allowance is needed, and the removal distance Bco is specified as follows:

[0056] When h ≤ 1.5 mm, Bco = 1 mm;

[0057] When 1.5mm < h ≤ 2.5mm, Bco = 2mm;

[0058] When 2.5mm < h ≤ 3.5mm, Bco = 3mm;

[0059] When 3.5mm < h ≤ 4.5mm, Bco = 4mm;

[0060] When 4.5mm < h ≤ 5.5mm, Bco = 5mm;

[0061] When h > 5.5 mm, Bco = 6 mm.

[0062] In one embodiment of this application, the step of filtering the preprocessed data to extract surface roughness data and obtain first-filtered data may include: performing filtering analysis on the preprocessed data using Gaussian filtering, extracting and separating the surface roughness data, and independently saving the surface roughness data to obtain first-filtered data.

[0063] Please see Figure 5 , Figure 5 A simplified diagram illustrating the principle of Gaussian filtering according to one embodiment of this application is shown. Figure 5 As shown, Gaussian filtering can perform a weighted average of each point and several adjacent points. The weight of each point follows a Gaussian distribution with respect to its distance from the point. In other words, the closer a point is to the point, the greater its weight. The sum of the total weights is 1.

[0064] In one embodiment of this application, the method of performing secondary filtering on the primary filtered data to extract waviness data and obtain secondary filtered data may include: using FIR filtering to perform filtering analysis on the primary filtered data, extracting and separating the waviness data of the strip, and independently saving the waviness data to obtain secondary filtered data.

[0065] Please see Figure 6 , Figure 6 A simplified diagram of an FIR filter principle according to one embodiment of this application is shown. The FIR filter works by multiplying a series of constants by a series of the latest n data samples and summing the cells of the resulting array. By changing the weighting of the coefficients and the number of filter taps, the FIR filter can achieve virtually any desired frequency response characteristic.

[0066] In this application, the FIR filtering method can be used to further analyze the Gaussian-filtered data. A low-pass FIR filter is applied to the data after roughness information removal to separate the strip waviness information and profile information. The waviness after roughness removal is superimposed with shape error information and then filtered. The waviness meets the national standard requirement (shape error in the range of 1mm to 10mm), therefore a low-pass filter is used, with a cutoff wavelength of 10mm. MATLAB's FIR filter can be used, with an order of 200. To avoid excessive filtering errors at the data edges, the original data is periodically extended to ensure filtering accuracy.

[0067] In one embodiment of this application, the strip profile parameters include: thickness at each point from the transmission side to the operating side, strip crown, and strip edge drop.

[0068] In this application, data extraction requirements are formulated based on the plate outline description indicators, some of which are described below:

[0069] (1) CW_40 uses 40mm from the edge as a reference point to calculate the center convexity. It is the difference between the thickness at the center point of the strip width and the average thickness of the strip at 40mm from both edges. H_40S is the thickness at the starting end of the measurement 40mm from the edge, and H_40E is the thickness at the ending end of the measurement 40mm from the edge. That is:

[0070] CW 40 =H center ―(H 40S +H 40E ) / 2

[0071] (2) CW_25 is calculated using the 25mm edge as a reference point, with the center convexity calculated by subtracting the average thickness of the strip's two edges from the thickness at the center point of the strip width. H_25S is the thickness measured at the starting end 25mm from the edge, and H_25E is the thickness measured at the ending end 25mm from the edge.

[0072] CW 25 =H center ―(H 25S +H 25E ) / 2

[0073] (3) EDS_40 uses a reference point of 40mm from the edge at the start of the measurement to calculate the edge drop. The difference is calculated by subtracting the strip thickness at the same point (40mm from the edge) from the strip thickness at 100mm from the edge at the start of the measurement. That is:

[0074] EDS 40 =H 100S ―H 40S

[0075] Please see Figure 7 .

[0076] Figure 7 A comparison chart of quantitative indicators of plate profiles according to one embodiment of this application is shown.

[0077] Please see Figure 8 .

[0078] Figure 8 A diagram of the processed data interface according to one embodiment of this application is shown.

[0079] Please see Figure 9 .

[0080] Figure 9 A comparison graph of secondary filtered data and initial laser thickness measurement data according to one embodiment of this application is shown.

[0081] The device embodiments of this application will now be described in conjunction with the accompanying drawings.

[0082] Please see Figure 10 .

[0083] Figure 10 A simplified structural diagram of a data processing apparatus for laser thickness measurement of strip steel according to one embodiment of this application is shown. The apparatus 1000 may include: an acquisition unit 1001, a first filtering unit 1002, a second filtering unit 1003, and a calculation unit 1004.

[0084] The specific configuration of the device 1000 can be as follows: an acquisition unit 1001, used to acquire initial laser thickness measurement data of strip steel, filter and remove data anomalies and strip steel edge data to obtain preprocessed data; a first filtering unit 1002, used to perform a first filtering on the preprocessed data to extract surface roughness data to obtain first-filtered data; a second filtering unit 1003, used to perform a second filtering on the first-filtered data to extract waviness data to obtain second-filtered data; and a calculation unit 1004, used to calculate the second-filtered data according to at least one preset profile index and output data corresponding to at least one profile index.

[0085] Please see Figure 11 .

[0086] Figure 11 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0087] It should be noted that, Figure 11 The computer system 1100 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0088] like Figure 11 As shown, the computer system 1100 includes a Central Processing Unit (CPU) 1101, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1102 or programs loaded from storage portion 1108 into Random Access Memory (RAM) 1103, such as performing the methods described in the above embodiments. Various programs and data required for system operation are also stored in RAM 1103. The CPU 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. An Input / Output (I / O) interface 1105 is also connected to bus 1104.

[0089] The following components are connected to I / O interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to I / O interface 1105 as needed. Removable media 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1110 as needed so that computer programs read from them can be installed into storage section 1108 as needed.

[0090] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by central processing unit (CPU) 1101, it performs various functions defined in the system of this application.

[0091] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0093] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0094] In another aspect, this application also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the data processing method for strip laser thickness measurement described in the above embodiments.

[0095] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the data processing method for strip laser thickness measurement described in the above embodiments.

[0096] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0097] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0099] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data processing method for laser thickness measurement of strip steel, characterized in that, The method includes: Obtain initial laser thickness measurement data for the strip, filter and remove outlier data and data from the edge of the strip to obtain preprocessed data; The preprocessed data is filtered once to extract surface roughness data, resulting in filtered data. The primary filtered data is then subjected to secondary filtering to extract ripple data, resulting in secondary filtered data. The secondary filtered data is calculated based on at least one preset plate profile index, and the data corresponding to at least one plate profile index is output. The filtering and removal of data outliers includes: The average thickness of the strip is calculated based on the initial laser thickness measurement data. Calculate the ratio of the initial laser thickness measurement data to the average thickness of the strip steel to obtain the fluctuation amplitude of each data point; Based on the distribution pattern of fluctuation amplitude, outliers in the data are filtered out.

2. The method according to claim 1, characterized in that, The process of filtering and removing outliers based on the distribution pattern of fluctuation amplitude includes: Based on the distribution pattern of fluctuation amplitude, outliers are filtered and removed according to the 3 sigma principle.

3. The method according to claim 1, characterized in that, The filtering and removal of strip edge data includes: The average thickness of the strip is calculated based on the initial laser thickness measurement data. Select the target strip edge data removal rule based on the average strip thickness; Based on the target strip edge data removal rules, filter and remove strip edge data.

4. The method according to claim 1, characterized in that, The step of filtering the preprocessed data to extract surface roughness data and obtain filtered data includes: The preprocessed data is filtered and analyzed using Gaussian filtering to extract and separate surface roughness data. The surface roughness data is then saved independently to obtain first-stage filtered data.

5. The method according to claim 1, characterized in that, The step of performing a second filtering on the first-filtered data to extract ripple data and obtain second-filtered data includes: The primary filtered data is analyzed using FIR filtering to extract and separate the strip waviness data. The waviness data is then saved independently to obtain the secondary filtered data.

6. The method according to claim 1, characterized in that, The strip profile parameters include: thickness at each point from the transmission side to the operating side, strip crown, and strip edge drop.

7. A data processing device for laser thickness measurement of strip steel, characterized in that, The device includes: The acquisition unit is used to acquire the initial laser thickness measurement data of the strip, filter and remove data anomalies and data from the edge of the strip, and obtain preprocessed data; The first filtering unit is used to filter the preprocessed data once to extract surface roughness data and obtain filtered data. The second filtering unit is used to perform secondary filtering on the primary filtered data to extract ripple data and obtain secondary filtered data. The calculation unit is used to calculate the secondary filtered data according to at least one preset plate profile index and output the data corresponding to at least one plate profile index. The filtering and removal of data outliers includes: The average thickness of the strip is calculated based on the initial laser thickness measurement data. Calculate the ratio of the initial laser thickness measurement data to the average thickness of the strip steel to obtain the fluctuation amplitude of each data point; Based on the distribution pattern of fluctuation amplitude, outliers in the data are filtered out.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations of the data processing method for strip laser thickness measurement as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operations performed by the data processing method for strip laser thickness measurement as described in any one of claims 1 to 6.

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