Digital-twin-based intelligent control method for machining precision of numerical control machine tool

Abstract

The application discloses a kind of based on digital twinning numerical control machine tool machining precision intelligent control method.The present application includes: firstly, the main shaft digital twin of integrated digital three-dimensional model, temperature information remodeling and incremental prediction function is established;Then, in the hot machine stage, temperature, displacement sensor is arranged, the main shaft temperature information is remodeled using digital twin, the synchronous measuring point that satisfies preset thermal error condition is determined and the temperature change thereof is predicted;Temperature information remodeling and incremental prediction process are repeated, and when the temperature of synchronous measuring point reaches the processing permission threshold, processing instruction is given, the linear regression model of synchronous measuring point temperature and thermal error is established and packaged in digital twin for thermal error prediction;Finally, into processing stage, synchronous measuring point temperature prediction information is integrated into main shaft thermal error prediction model, and main shaft thermal error is predicted in real time and compensated in time.The present application provides theoretical guidance for the hot machine process of machine tool, and controls the machining precision of machine tool under the condition that sensor cannot be arranged in the machining process of numerical control machine tool.

Description

Technical Field

[0001] This invention relates to a method for controlling the machining accuracy of CNC machine tools, and more particularly to an intelligent control method for the machining accuracy of CNC machine tools based on digital twins. Background Technology

[0002] As the mother machines of industry, the safe, stable operation and high-quality processes of machine tools are crucial for achieving intelligent manufacturing. The spindle is the core component determining the machining accuracy of CNC machine tools. Changes in the spindle's temperature field during machine tool operation can cause thermal deformation, resulting in thermal errors that severely affect machining quality. Precisely controlling the spindle system's temperature field distribution, thermal equilibrium time, and thermally induced errors significantly improves the manufacturing capabilities and machining accuracy of CNC machine tools.

[0003] Currently, there is relatively little research on spindle machining accuracy control both domestically and internationally. The commonly used method is to pre-establish a mapping relationship between several temperature measurement points and thermal errors, and then predict thermal errors based on the temperature information near the spindle during actual machining to control the machining accuracy of the machine tool. This method has the following problems: (1) Due to the inability to reserve space for sensor placement in specific locations and the interference of high-pressure coolant splashing with sensor readings, the sensor information during the machine tool machining process is lost. (2) There is a lack of theoretical guidance on the machine tool's thermal process. Ending the thermal process too early will lead to significant changes in spindle thermal errors during actual machining, increasing the difficulty of thermal error compensation and affecting machining quality; ending the thermal process too late will severely limit production efficiency. Summary of the Invention

[0004] To address the problems existing in the background technology, this invention proposes an intelligent control method for the machining accuracy of CNC machine tools based on digital twins. By establishing a digital twin system with functions such as comprehensive spindle temperature information reshaping, incremental prediction of temperature information at key measuring points, and prediction of thermal errors, it provides theoretical guidance for the machine tool thermomechanical process and accurately predicts spindle thermal errors, thereby regulating the machining accuracy of CNC machine tools.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] I. An Intelligent Control Method for Machining Accuracy of CNC Machine Tools Based on Digital Twin

[0007] S1: Construct a digital 3D model of the CNC machine tool spindle, establish a spindle temperature information reshaping model based on the spindle's thermal conduction characteristics, and integrate it with the spindle temperature information incremental prediction model into the digital twin of the CNC machine tool spindle;

[0008] S2: During the warm-up phase of the machine tool, temperature sensors are placed near the front cover of the spindle and the rear bearing, and displacement sensors are placed directly opposite the spindle end. Spindle temperature information and thermal error information are continuously collected. After obtaining spindle temperature information for a first preset time, S3 is executed.

[0009] S3: Based on the current spindle temperature information, the temperature information of each sampling point on the spindle is determined by reconstructing the model using the spindle temperature information. Then, the sampling points that meet the preset thermal error conditions are used as the synchronous measurement points of the spindle.

[0010] S4: Based on the temperature information of the current synchronous measuring point of the spindle, use the spindle temperature information incremental prediction model to predict the future temperature change of the synchronous measuring point, and record it as the temperature prediction information of the current synchronous measuring point;

[0011] S5: Repeat S3-S4 at the second preset time interval, continuously update the synchronous measuring point and its temperature prediction information until the thermal steady state process of the current synchronous measuring point reaches the processing permission threshold, the digital twin gives the permission processing instruction, and then constructs the spindle thermal error prediction model.

[0012] S6: Remove the temperature sensor and displacement sensor, start the actual machining process of the machine tool, call the temperature prediction information of the current synchronous measurement point in S5 and integrate it with the spindle thermal error prediction model, predict the spindle thermal error in real time, and then give compensation instructions to ensure the machining accuracy of the machine tool.

[0013] In S1, the specific construction method of the spindle temperature information reshaping model is as follows:

[0014] First, the principal axis model is simplified to a one-dimensional heat conduction model to obtain the principal axis heat transfer equation. Then, the initial and boundary conditions of the principal axis are set. The loss function of the physical information neural network model is constructed using the principal axis heat transfer equation, the initial and boundary conditions of the principal axis. Finally, the physical information neural network model is used as the principal axis temperature information reshaping model.

[0015] The heat transfer equation is as follows:

[0016]

[0017]

[0018] in, Represents the temperature distribution function. This indicates the thermal diffusivity of the spindle. This indicates the thermal conductivity of the spindle. Indicates principal axis density, This indicates the specific heat capacity of the spindle.

[0019] The loss function of the physical information neural network model The formula is as follows:

[0020]

[0021] in, and These represent the sizes of the boundary point training set and the initial point training set, respectively. The size of the training set for the partial differential equation is configured to satisfy the following conditions: , Indicates the point number in the training set;

[0022] Given boundary conditions Position along the main axis at all times The temperature solution obtained from the one-dimensional heat conduction equation Loss on the partial differential equation of heat transfer at any target point The formula is as follows:

[0023]

[0024] in, Indicates the thermal diffusivity of the spindle;

[0025] Loss due to initial conditions The formula is as follows:

[0026]

[0027] in, The spindle temperature under initial conditions. Indicates the condition under given boundary conditions Position along the main axis at all times The temperature solution obtained from the one-dimensional heat conduction equation is... The spindle temperature under initial conditions;

[0028] Boundary loss at the front end of the spindle Boundary loss at the back end of the spindle The formulas are as follows:

[0029]

[0030]

[0031] in, , These respectively indicate the spindle at Axial position at time and The temperature at that location Main axis length, and They are respectively in Always along the direction of the main axis and The temperature solution is obtained from the one-dimensional heat conduction equation.

[0032] The specific construction method of the spindle temperature information incremental prediction model in S1 is as follows:

[0033] First, temperature information near the front cover and rear bearing of the spindle is collected using a temperature sensor. Then, the collected temperature information is fused with the spindle temperature information reshaping model to obtain the temperature distribution information along the spindle axis. Next, the temperature information of the measuring points in the preset position range along the spindle axis is fused with the long short-term memory network to obtain the spindle temperature information incremental prediction model.

[0034] In step S3, if there is no sampling point that meets the preset thermal error condition among the current sampling points, the sampling interval is reduced to obtain a new set of sampling points. The sampling point that meets the preset thermal error condition is searched in the new set of sampling points until a sampling point that meets the preset thermal error condition is found and used as the synchronous measurement point of the main axis.

[0035] In S5, the spindle thermal error prediction model is a linear regression model between the temperature of the current synchronous measuring point and the spindle thermal error.

[0036] Specifically, S6 is:

[0037] S61: After the warm-up phase is over, remove the temperature sensor and displacement sensor, then dress the grinding wheel, set the tool and begin machining;

[0038] S62: Utilizing the temperature prediction information of the current synchronous measurement point in S5, the spindle thermal error is predicted in real time by calling the spindle thermal error prediction model in the digital twin. Combined with the actual machining accuracy requirements of the workpiece, a compensation command is given before the spindle thermal error accumulates to the point of not meeting the machining accuracy requirements, thereby controlling the machining accuracy of the machine tool.

[0039] II. A computer device

[0040] The computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the method.

[0041] III. A computer-readable storage medium

[0042] A computer program is stored on a computer-readable storage medium, which, when executed by a processor, implements the steps of the method.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1) This invention arranges a small number of temperature sensors during the machine tool's warm-up phase, identifies the synchronous measurement point with the highest thermal error linearity through the spindle digital twin, and establishes a linear thermal error prediction model. The operation and calculation are simple and the prediction accuracy is high.

[0045] 2) This invention provides theoretical guidance for the machine tool's thermomechanical process through a spindle digital twin, ensuring that machining begins when the spindle's thermal balance process meets requirements. This not only guarantees that thermal error changes during machining are not significant and reduces the difficulty of compensation, but also ensures economic benefits.

[0046] 3) This invention makes full use of the temperature and thermal error information collected during the heat engine stage. Since coolant splashing during the machine tool processing stage will affect information collection, sensors are not placed during the machine tool processing process, thereby ensuring effective prediction and control of thermal errors during the processing stage. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention.

[0048] Figure 2 The simplified one-dimensional heat conduction model of the main axis system.

[0049] Figure 3 This is the experimental environment for an embodiment of the present invention.

[0050] Figure 4 The results show the temperature field reconstruction of the machine tool spindle system.

[0051] Figure 5 This is a flowchart illustrating the iterative determination process for synchronous measurement points in this invention. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and specific examples:

[0053] like Figure 1 As shown, the present invention includes the following steps:

[0054] S1: Construct a digital 3D model of the CNC machine tool spindle, establish a spindle temperature information reshaping model based on the spindle's heat conduction characteristics, and integrate the digital 3D model of the CNC machine tool spindle, the spindle temperature information reshaping model, and the spindle temperature information incremental prediction model into a digital twin of the CNC machine tool spindle;

[0055] In S1, the specific construction method of the spindle temperature information reshaping model is as follows:

[0056] First, the spindle model is simplified to a one-dimensional heat conduction model with one end fixed and the other end freely expanding. It is assumed that the heat source is transferred to the spindle from the fixed end via heat conduction. Figure 2 As shown, the heat transfer equation of the main shaft is obtained;

[0057] The heat transfer equation is as follows:

[0058]

[0059]

[0060] in, Represents the temperature distribution function. This indicates the thermal diffusivity of the spindle. This indicates the thermal conductivity of the spindle. Indicates principal axis density, This indicates the specific heat capacity of the spindle.

[0061] Next, the initial and boundary conditions of the principal axis are set, and then the loss function of the physical information neural network model is constructed using the principal axis heat transfer equation, the initial and boundary conditions of the principal axis. Finally, the physical information neural network model is used as the principal axis temperature information reshaping model.

[0062] The initial conditions for the spindle are as follows: The internal temperature of the spindle at that time was The formula is as follows:

[0063]

[0064] in, express Position along the main axis at all times The temperature at that location , , Indicates the spindle length. The spindle temperature is the initial temperature.

[0065] The boundary condition formulas for the principal axis are as follows:

[0066]

[0067] Since temperature sensors cannot be placed on the spindle during machine tool operation, temperature sensors are placed near the front end cover and the rear bearing of the spindle, respectively. Figure 3 As shown, as the front axle end and rear axle end The boundary temperature information, i.e. the boundary conditions of the principal axis.

[0068] Loss function of physical information neural network model The formula is as follows:

[0069]

[0070] in, and These represent the sizes of the boundary point training set and the initial point training set, respectively. The size of the training set for the partial differential equation is configured to satisfy the following conditions: , Indicates the point number in the training set;

[0071] Given boundary conditions Position along the main axis at all times The temperature solution obtained from the one-dimensional heat conduction equation Loss on the partial differential equation of heat transfer at any target point The formula is as follows:

[0072]

[0073] It is generally assumed that the spindle is in an initial state with a uniform temperature distribution, meaning the temperature is the same at all axial positions. Losses due to initial conditions. The formula is as follows:

[0074]

[0075] in, Indicates the condition under given boundary conditions Position along the main axis at all times The temperature solution obtained from the model reconstructed using the principal axis temperature information;

[0076] For the two boundaries at the front and rear ends of the spindle, the boundary loss at the front end of the spindle... Boundary loss at the back end of the spindle The formulas are as follows:

[0077]

[0078]

[0079] in, , They represent in Position along the main axis at all times and The temperature at that location Main axis length, and They are respectively in Always along the direction of the main axis and The temperature solution is obtained from the one-dimensional heat conduction equation.

[0080] In S1, the specific construction method of the spindle temperature information incremental prediction model is as follows:

[0081] First, temperature information near the front cover and rear bearing of the spindle is collected using temperature sensors. Then, this collected temperature information is fused with a spindle temperature information reconstruction model to obtain the temperature distribution information along the spindle axis. Next, the temperature information from measurement points within a preset range along the spindle axis is fused with a long short-term memory network to obtain an incremental prediction model for spindle temperature information, used to analyze the spindle's temperature distribution within the axial position range. Different times within) The temperature distribution under [the following conditions]. In this embodiment, through extensive experiments, it was determined that the synchronous measuring points are typically distributed in [the following locations]. Between, based on temperature information, the model is reconstructed to obtain multiple sets of data located in... The temperature information from the measurement points is integrated with a long short-term memory network to construct an incremental prediction model for the main axis temperature information.

[0082] S2: During the warm-up phase of the machine tool under test, temperature sensors are placed near the front cover of the spindle and the rear bearing, and displacement sensors are placed directly opposite the spindle end. Spindle temperature information and thermal error information are continuously collected. After obtaining spindle temperature information for a first preset time (e.g., the first 15 minutes), S3 is executed.

[0083] In practice, during the warm-up phase before machining, four temperature sensors are placed around the front cover of the spindle, and two temperature sensors are placed near the rear bearing of the spindle. An eddy current displacement sensor is positioned directly opposite the spindle end to characterize the axial thermal error of the spindle by the change in the sensor's position relative to the spindle end. Figure 3 As shown, the collected temperature and thermal error information is input into the spindle digital twin in real time, and the average temperature of the four temperature sensors near the front cover is taken as the front shaft end boundary temperature, and the average temperature of the two temperature sensors near the rear bearing is taken as the rear shaft end boundary temperature.

[0084] S3: Integrate the current spindle temperature information with the spindle temperature information reshaping model. Specifically, input the temperature information near the front and rear ends of the spindle into the spindle temperature information reshaping model in the digital twin, and take 10mm as the sampling interval to obtain the temperature information of each sampling point along the axis of the spindle. This will reconstruct and determine the temperature information of each sampling point on the spindle at 10mm intervals. Then, the sampling points that meet the preset thermal error conditions will be used as the synchronous measurement points of the spindle.

[0085] S3 specifically refers to:

[0086] S31: After the machine tool has warmed up for 15 minutes, based on the input initial internal spindle temperature information and the front and rear spindle end boundary temperature information after 15 minutes, it is fused with the spindle temperature information reshaping model described in S1 to reconstruct the temperature distribution at each point along the spindle axis, such as... Figure 4As shown. Considering the temperature sensor used in the experiment is 10mm, the temperature reconstruction interval is set to 10mm.

[0087] S32: In this embodiment, the distance between the front and rear bearings of the spindle is 516mm. A total of 52 sampling points are established along the spindle axis at 10mm sampling intervals. The linear correlation coefficient between each sampling point and the axial thermal error is calculated. The sampling point with the highest linearity to the axial thermal error is selected, and it is determined whether the linearity exceeds 0.995. If it does, this point is used as the synchronous measurement point for the spindle under this operating condition. If it does not exceed 0.995, the sampling interval is reduced to 5mm. The sampling point with the highest linear correlation coefficient to the axial thermal error is then determined. This process is iteratively repeated until a sampling point with a linearity to the axial thermal error exceeding 0.995 is found. The iterative process is as follows: Figure 5 As shown.

[0088] S4: Based on the temperature information of the current synchronous measuring point of the spindle, the incremental prediction model of spindle temperature information is used to predict the future temperature change of the synchronous measuring point, and it is recorded as the temperature prediction information of the current synchronous measuring point; during the first prediction, the incremental prediction model of spindle temperature information predicts the temperature change in the next K1 hours, such as K1=4.

[0089] S5: Repeat S3-S4 at second preset intervals (e.g., 6 minutes). In each repetition, all currently collected spindle temperature information is fused with the spindle temperature information remodeling model to continuously update the synchronous measuring point and predict its temperature information within the next K2 time period, where K2 = K1 - 0.1 * m, and m is the interval number, until the thermal steady-state process of the current synchronous measuring point reaches the processing permission threshold. In specific implementation, the processing permission threshold is when the temperature of the current synchronous measuring point reaches 80% of its steady-state temperature. The digital twin issues a permission processing command, establishes a linear regression model between the temperature of the current synchronous measuring point and the spindle thermal error, and uses it as the spindle thermal error prediction model.

[0090] S5 specifically refers to:

[0091] S51: The collected temperature and thermal error information is transmitted and stored in the digital twin in real time. The spindle temperature information in the digital twin is re-called every 6 minutes to reshape the model. The temperature information of the synchronous measuring point is determined through the steps described in S3.

[0092] S52: Based on the incremental prediction model of spindle temperature information established in S1, input the temperature information of the current synchronous measuring point and predict its temperature change in the next K hours.

[0093] S53: The spindle temperature information is cyclically updated to reshape and incrementally predict the model. The digital twin monitors the current temperature of the synchronous measuring point in real time. When the current synchronous measuring point temperature reaches 80% of the steady-state temperature, the digital twin issues a permissible machining command. A linear regression model between the current synchronous measuring point temperature and the spindle thermal error is established, and its expression is:

[0094]

[0095] in, and These are two regression coefficients, both of which are variable coefficients. To provide synchronous temperature information of the spindle measuring points at various times, and to meet the requirements of... , For the axial thermal error information of the spindle at each moment, satisfying , , They are time points Temperature information of the synchronous measuring point of the spindle and axial thermal error information of the spindle.

[0096] The established linear regression model is used as the spindle thermal error prediction model and stored in the spindle digital twin for spindle thermal error prediction during the machine tool machining stage.

[0097] S6: Remove the temperature sensor and displacement sensor, and start the actual machining process of the machine tool. Based on the temperature prediction information of the current synchronous measurement point in S5, use the spindle thermal error prediction model to predict the spindle thermal error in real time, and then give compensation instructions in a timely manner to ensure the machining accuracy of the machine tool.

[0098] S6 specifically refers to:

[0099] S61: After the warm-up phase is over, remove the temperature sensor and displacement sensor, then dress the grinding wheel, set the tool and begin machining;

[0100] S62: Utilizing the temperature prediction information of the current synchronous measurement point in S5, the spindle thermal error is predicted in real time by calling the spindle thermal error prediction model in the digital twin. Combined with the actual machining accuracy requirements of the workpiece, compensation instructions and calculation results are given before the spindle thermal error accumulates to the point of not meeting the machining accuracy requirements, thereby controlling the machining accuracy of the machine tool.

[0101] Finally, it should be noted that the above embodiments and descriptions are only used to illustrate the technical solutions of the present invention and not to limit it. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the disclosure of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for intelligent control of machining accuracy in CNC machine tools based on digital twins, characterized in that, Includes the following steps: S1: Construct a digital 3D model of the CNC machine tool spindle, establish a spindle temperature information reshaping model based on the spindle's thermal conduction characteristics, and integrate it with the spindle temperature information incremental prediction model into the digital twin of the CNC machine tool spindle; In S1, the specific construction method of the spindle temperature information reshaping model is as follows: First, the principal axis model is simplified to a one-dimensional heat conduction model to obtain the principal axis heat transfer equation. Then, the initial and boundary conditions of the principal axis are set. The loss function of the physical information neural network model is constructed using the principal axis heat transfer equation, the initial and boundary conditions of the principal axis. Finally, the physical information neural network model is used as the principal axis temperature information reshaping model. The loss function of the physical information neural network model The formula is as follows: in, and These represent the sizes of the boundary point training set and the initial point training set, respectively. The size of the training set for the partial differential equation is configured to satisfy the following conditions: , Indicates the point number in the training set; Given boundary conditions Position along the main axis at all times The temperature solution obtained from the one-dimensional heat conduction equation Loss on the partial differential equation of heat transfer at any target point The formula is as follows: in, This indicates the thermal diffusivity of the spindle; Loss due to initial conditions The formula is as follows: in, The spindle temperature under initial conditions. Indicates the condition under given boundary conditions Position along the main axis at all times The temperature solution obtained from the one-dimensional heat conduction equation is... The spindle temperature under initial conditions; Boundary loss at the front end of the spindle Boundary loss at the back end of the spindle The formulas are as follows: in, , These respectively indicate the spindle at Axial position at time and The temperature at that location Main axis length, and They are respectively in Always along the direction of the main axis and The temperature solution obtained from the one-dimensional heat conduction equation; S2: During the warm-up phase of the machine tool, temperature sensors are placed near the front cover of the spindle and the rear bearing, and displacement sensors are placed directly opposite the spindle end. Spindle temperature information and thermal error information are continuously collected. After obtaining spindle temperature information for a first preset time, S3 is executed. S3: Based on the current spindle temperature information, the temperature information of each sampling point on the spindle is determined by reconstructing the model using the spindle temperature information. Then, the sampling points that meet the preset thermal error conditions are used as the synchronous measurement points of the spindle. S4: Based on the temperature information of the current synchronous measuring point of the spindle, use the spindle temperature information incremental prediction model to predict the future temperature change of the synchronous measuring point, and record it as the temperature prediction information of the current synchronous measuring point; S5: Repeat S3-S4 at the second preset time interval, continuously update the synchronous measuring point and its temperature prediction information until the thermal steady state process of the current synchronous measuring point reaches the processing permission threshold, the digital twin gives the permission processing instruction, and then constructs the spindle thermal error prediction model. S6: Remove the temperature sensor and displacement sensor, start the actual machining process of the machine tool, call the temperature prediction information of the current synchronous measurement point in S5 and integrate it with the spindle thermal error prediction model, predict the spindle thermal error in real time, and then give compensation instructions to ensure the machining accuracy of the machine tool.

2. The intelligent control method for machining accuracy of CNC machine tools based on digital twins according to claim 1, characterized in that, The heat transfer equation is as follows: in, Represents the temperature distribution function. This indicates the thermal diffusivity of the spindle. This indicates the thermal conductivity of the spindle. Indicates principal axis density, This indicates the specific heat capacity of the spindle.

3. The intelligent control method for machining accuracy of CNC machine tools based on digital twins according to claim 1, characterized in that, The specific construction method of the spindle temperature information incremental prediction model in S1 is as follows: First, temperature information near the front cover and rear bearing of the spindle is collected using a temperature sensor. Then, the collected temperature information is fused with the spindle temperature information reshaping model to obtain the temperature distribution information along the spindle axis. Next, the temperature information of the measuring points in the preset position range along the spindle axis is fused with the long short-term memory network to obtain the spindle temperature information incremental prediction model.

4. The intelligent control method for machining accuracy of CNC machine tools based on digital twins according to claim 1, characterized in that, In step S3, if there is no sampling point that meets the preset thermal error condition among the current sampling points, the sampling interval is reduced to obtain a new set of sampling points. The sampling point that meets the preset thermal error condition is searched in the new set of sampling points until a sampling point that meets the preset thermal error condition is found and used as the synchronous measurement point of the main axis.

5. The intelligent control method for machining accuracy of CNC machine tools based on digital twins according to claim 1, characterized in that, In S5, the spindle thermal error prediction model is a linear regression model between the temperature of the current synchronous measuring point and the spindle thermal error.

6. The intelligent control method for machining accuracy of CNC machine tools based on digital twins according to claim 1, characterized in that, Specifically, S6 is: S61: After the warm-up phase is over, remove the temperature sensor and displacement sensor, then dress the grinding wheel, set the tool and begin machining; S62: Utilizing the temperature prediction information of the current synchronous measurement point in S5, the spindle thermal error is predicted in real time by calling the spindle thermal error prediction model in the digital twin. Combined with the actual machining accuracy requirements of the workpiece, a compensation command is given before the spindle thermal error accumulates to the point of not meeting the machining accuracy requirements, thereby controlling the machining accuracy of the machine tool.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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