Ice layer thickness measuring method and device

Abstract

The invention discloses an ice layer thickness measuring method and device and relates to the field of radar technology and electromagnetism. According to an existing ice thickness detection technology, additional instruments need to be arranged on an ice cover, the complexity and cost of equipment are increased, the requirement for the environment is high, and in order to solve the technical defects existing in the prior art, the technical scheme provided by the invention is that the ice layer thickness measurement method comprises the steps of collecting a preset algorithm and algorithm parameters; a step of preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm; carrying out peak detection on the preprocessed high-frequency electromagnetic wave echo signal; a step of obtaining a horizon detection result of the preprocessed high-frequency electromagnetic wave echo signal according to a peak value detection result; and obtaining the thickness of the ice layer according to the result of the layer position detection. The method can be applied to ice layer thickness measurement and ice cover bearing capacity calculation.

Description

Technical Field

[0001] It involves the fields of radar technology and electromagnetics. Background Art

[0002] The existing ice thickness detection technologies mainly include: manual drilling and coring to measure the thickness of the ice layer, as well as the use of seismic waves and resistivity methods.

[0003] Manual core drilling for ice thickness measurement has its drawbacks. It requires on-site drilling, which is labor-intensive, time-consuming, and labor-intensive. Manual drilling requires tools such as drill rods and drill bits, and requires personnel to operate, making it prohibitively labor-intensive for measuring large ice areas. During the drilling process, the drill bit cuts into the ice, potentially breaking or deforming it, affecting the accuracy of the measurement. Furthermore, it only provides localized ice thickness information.

[0004] Measuring ice sheet thickness using seismic wave velocity. By placing seismic instruments on the ice sheet and recording the time and velocity of seismic wave propagation, the thickness of the ice sheet can be inferred. This method offers high precision and accuracy, enabling real-time monitoring of ice sheet thickness. However, this method requires placing seismic instruments on the ice sheet, increasing the complexity and cost of the equipment, and also placing high demands on the environment in which seismic waves propagate.

[0005] The thickness of the ice sheet is estimated using georesistivity measurements. By placing georesistivity instruments on the ice sheet, changes in the subsurface resistivity are measured, thereby inferring the thickness of the ice sheet. This method offers high precision and accuracy, enabling real-time monitoring of ice sheet thickness. However, this method requires placing georesistivity instruments on the ice sheet, increasing the complexity and cost of the equipment, and placing high demands on the subsurface resistivity measurements.

[0006] In summary, the disadvantages of existing technical means for ice thickness detection are that additional instruments need to be placed on the ice sheet, which increases the complexity and cost of the equipment, has high environmental requirements, and has low measurement efficiency. Summary of the Invention

[0007] The disadvantages of existing ice thickness detection technology are that it requires additional instruments to be placed on the ice sheet, which increases the complexity and cost of the equipment and places high demands on the environment. To address these technical shortcomings of the existing technology, the present invention provides the following technical solutions:

[0008] A method for measuring ice thickness, the method comprising:

[0009] Steps for collecting preset algorithms and algorithm parameters;

[0010] The step of preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm;

[0011] A step of performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal;

[0012] A step of obtaining a layer detection result of the preprocessed high-frequency electromagnetic wave echo signal according to the result of the peak detection;

[0013] The step of obtaining the thickness of the ice layer according to the result of the layer detection.

[0014] Furthermore, a preferred embodiment is provided, wherein the preset algorithm is peak detection and correlation detection.

[0015] Furthermore, a preferred embodiment is provided, wherein the preset algorithm parameters include a reading path, a header byte length, a filter order, a filter type, and a cutoff frequency.

[0016] Furthermore, a preferred embodiment is provided, wherein the preprocessing specifically comprises filtering the collected high-frequency electromagnetic wave echo signal through an FIR filter.

[0017] Furthermore, a preferred embodiment is provided, in which the peak signal obtained by peak detection is used as a layer reference signal, and the layer detection result is obtained through correlation calculation using the layer reference signal.

[0018] Furthermore, a preferred embodiment is provided, in which the thickness of the ice layer is obtained based on the layer detection results, combined with the sampling rate and the electromagnetic parameters of the ice.

[0019] Further, a preferred embodiment is provided, wherein the method further comprises:

[0020] In the continuous acquisition mode, when the peak detection result data is abnormal, the abnormal step is skipped by expanding the search range and re-detecting or using the previous detection data.

[0021] Based on the same inventive concept, the present invention also provides an ice thickness measuring device, comprising:

[0022] A module for collecting preset algorithms and algorithm parameters;

[0023] A module for preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm;

[0024] A module for performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal;

[0025] A module for obtaining a layer detection result of the pre-processed high-frequency electromagnetic wave echo signal according to the result of the peak detection;

[0026] According to the result of the layer detection, a module of ice layer thickness is obtained.

[0027] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method described above.

[0028] Based on the same inventive concept, the present invention also provides a computer, comprising a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method described above.

[0029] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0030] The ice thickness measurement method provided by the present invention utilizes electromagnetic principles to enable ground-penetrating radar to efficiently and non-destructively detect the distribution of underground media. This operating principle enables the solution to accurately determine the distribution of underground objects.

[0031] The ice thickness measurement method provided by this invention uses peak detection and correlation detection algorithms to achieve real-time tracking of ice thickness. By setting algorithm parameters and performing data processing, the ice thickness can be accurately calculated, reducing the need for manual operation and improving measurement accuracy and efficiency. This algorithmic process enables the solution to automatically identify and calculate ice thickness.

[0032] The ice layer thickness measurement method provided by the present invention can be applied to ice layer thickness measurement and ice cover bearing capacity calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the working principle of ground penetrating radar;

[0034] Figure 2 This is a schematic diagram of the ground penetrating radar echo curve;

[0035] Figure 3 This is a schematic diagram of the electrical structure of the ice detection radar system;

[0036] Figure 4 This is a schematic diagram of the operation process of the ice detection radar. DETAILED DESCRIPTION

[0037] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0038] Embodiment 1: This embodiment provides a method for measuring ice thickness, the method comprising:

[0039] Steps for collecting preset algorithms and algorithm parameters;

[0040] The step of preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm;

[0041] A step of performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal;

[0042] A step of obtaining a layer detection result of the preprocessed high-frequency electromagnetic wave echo signal according to the result of the peak detection;

[0043] The step of obtaining the thickness of the ice layer according to the result of the layer detection.

[0044] Step 1: Algorithm parameter setting

[0045] Configure parameters related to data files, such as storage path, header byte length, etc.

[0046] Set the filter parameters, including the FIR filter order, filter type, and cutoff frequency.

[0047] Set the initial layer position and roughly set the radar wave layer positions for the upper and lower layers of the ice surface.

[0048] Set the number of A-Scan data channels collected at each measuring point, generally 2 to the power of N.

[0049] Step 2: Data preprocessing

[0050] The data format processed by the algorithm is multi-channel A-Scan data with time as the horizontal axis, and each channel of A-Scan data contains 1024 sampling points.

[0051] The sampled data is filtered using a bandpass FIR filter to remove high-frequency and low-frequency noise.

[0052] Step 3: Ice surface peak detection

[0053] Peak detection is used on each A-scan data channel to search near the initially set upper layer of the ice surface. The extreme value is recorded as the upper layer reference signal. The ice surface position detected by multiple data channels is averaged to obtain the ice layer position. This signal is also recorded as the ice surface position reference signal.

[0054] Step 4: Ice bottom peak detection algorithm

[0055] The background of multiple A-Scan data is removed, and then each A-Scan data is processed and back-differentiated to obtain a differential sequence.

[0056] The difference function is binarized to obtain a binarized sequence.

[0057] Perform the difference operation on the binarized sequence again to obtain the peak sequence.

[0058] Traverse each positive and negative peak, take the initially set reference point of the bottom of the ice layer as the starting point, and search for the maximum value on both sides to determine the peak position of the bottom of the ice layer.

[0059] The ice bottom position detected by multiple channels of data is averaged and recorded as the ice bottom position. This signal is then used as the ice bottom position reference signal.

[0060] In single-point acquisition mode, proceed directly to step 7. In continuous acquisition mode, steps 5 and 6 are also required:

[0061] Step 5: Related calculations

[0062] Use the FFT algorithm to perform correlation calculations between the reference signal and the acquired signal to quickly determine the layer position.

[0063] Fine positioning correction is performed through peak detection to reduce positioning errors.

[0064] Step 6: Exception handling

[0065] In the continuous acquisition mode, the algorithm handles situations such as tracking loss and abnormal jumps, including expanding the search range to re-detect, using the previous detection data and skipping detection, etc., to enhance the robustness of the algorithm.

[0066] Step 7: Calculate ice thickness

[0067] The ice thickness is calculated by combining the average ice surface and ice bottom layers calculated from the multi-channel A-Scan data obtained in steps 3 and 4, and combining the sampling rate and the electromagnetic parameters of the ice.

[0068] Implementation method 2: This implementation method further limits the ice thickness measurement method provided in implementation method 1, and the preset algorithms are peak detection and correlation detection.

[0069] Implementation method three: This implementation method further limits the ice thickness measurement method provided in implementation method two. The preset algorithm parameters include a reading path, header byte length, filter order, filter type, and cutoff frequency.

[0070] Implementation 4: This implementation further limits the ice thickness measurement method provided in Implementation 1. The preprocessing specifically includes filtering the collected high-frequency electromagnetic wave echo signal through an FIR filter.

[0071] Implementation method five: This implementation method further limits the ice thickness measurement method provided in implementation method one. The peak signal obtained by peak detection is used as a layer reference signal. The layer detection result is obtained through the layer reference signal and correlation calculation.

[0072] Implementation method 6: This implementation method further limits the ice thickness measurement method provided in implementation method 5. The ice thickness is obtained based on the layer detection results, combined with the sampling rate and the electromagnetic parameters of the ice.

[0073] Embodiment 7: This embodiment further limits the ice thickness measurement method provided in Embodiment 1, and the method further includes:

[0074] In the continuous acquisition mode, when the peak detection result data is abnormal, the abnormal step is skipped by expanding the search range and re-detecting or using the previous detection data.

[0075] Embodiment 8: This embodiment provides an ice thickness measuring device, comprising:

[0076] A module for collecting preset algorithms and algorithm parameters;

[0077] A module for preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm;

[0078] A module for performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal;

[0079] A module for obtaining a layer detection result of the pre-processed high-frequency electromagnetic wave echo signal according to the result of the peak detection;

[0080] According to the result of the layer detection, a module of ice layer thickness is obtained.

[0081] Implementation method 9: This implementation method provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method provided in implementation method 1.

[0082] Implementation 10: This implementation provides a computer, including a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method provided in Implementation 1.

[0083] Implementation Method 11: Combination Figure 1-4 This embodiment further describes the above technical solution in detail and completely through specific examples, specifically:

[0084] Ground penetrating radar is different from traditional radar. It is a detection instrument for detecting the distribution of underground media. It has the characteristics of high efficiency and non-destructiveness. Its working principle is as follows Figure 1Utilizing electromagnetic principles, the radar transmits high-frequency electromagnetic waves underground, receives reflected echoes, and determines the distribution of underground objects based on the echo characteristics. The received echoes are stored as data in a host computer, where various data processing operations can be performed and then displayed using data visualization methods.

[0085] Echo curve Figure 2 As shown in Figure 2, when electromagnetic waves propagate underground, the distribution and composition of the underground medium will cause the echo to change. Therefore, the underground medium can be interpreted based on the characteristics of the echo, such as time delay and amplitude.

[0086] The handheld ice detection radar equipment system based on peak detection consists of a pulse receiving / transmitting unit, a signal acquisition unit (i.e., ice detection signal acquisition unit), and display and control peripherals. The signal acquisition unit is the core part of the system, and its main functions are: the signal acquisition unit receives information sent by peripherals such as buttons, and sets the equipment, including setting parameters and control commands, such as sampling frequency, number of sampling points, dielectric constant, detection mode, average channel number and other acquisition parameters, while completing the timing control of the entire system, radar signal conditioning, acquisition, data buffering, preprocessing and other transmission and control functions, and can transmit data to the cloud server in an orderly manner through the WIFI module, playing a role in controlling the operation of the entire radar system. Figure 3 shown.

[0087] The ice detection radar device based on peak detection is powered by 12V voltage. The working process of the whole device is as follows: the device is powered on by long pressing the switch button. After power on, the signal acquisition unit communicates with the display screen through the display control part. The operator sends parameters and working instructions to the ice detection radar device based on peak detection by pressing buttons, etc. The FPGA receives the instructions, controls the DDS chip to generate the transceiver control signal and controls the AD chip to collect data. The collected data is sent to the internal cache of the FPGA, equivalent sampling splicing, filtering and other processing, and then displayed on the screen through the display control module. At the same time, the data can be sent to the cloud platform for storage, processing and display through the WIFI module. Figure 3 shown.

[0088] The ice thickness measurement algorithm mainly relies on peak detection and correlation detection, which can realize real-time tracking of ice thickness.

[0089] When setting the algorithm parameters, you need to configure parameters related to the data file, the filter, and the initial layer position. Data file parameters include the read path and header byte length. Correctly setting these parameters ensures accurate reading of A-Scan data. Filter parameters involve the FIR filter order, filter type, and cutoff frequency. These parameters are used to calculate the FIR filter response. The initial layer position is set for the radar wave layer in the first data entry for the direct wave, upper ice layer, and lower ice layer. Excessive precision is not required, as these are automatically corrected later.

[0090] To reduce the sudden interference errors associated with collecting only one channel of data, the algorithm processes data in the form of multiple A-Scans with time as the horizontal axis. Each A-Scan contains 1024 sampling points. The number of A-Scan channels collected can be set on the device, typically in powers of 2, with a default of 32. Because the data is raw, preliminary processing is required. The algorithm uses a bandpass FIR filter to filter the sampled data to remove high- and low-frequency noise, thereby improving the signal-to-noise ratio.

[0091] During the first detection, since there is no reference signal and a fixed layer, it is necessary to use peak detection to search near the initially set layer and take the extreme value as the layer reference position. After positioning, the signal is intercepted as the reference signal. The peak detection calculation steps are as follows:

[0092] First, perform backward difference according to the following formula: f(k) is the original sampling sequence, and g(k) is the difference sequence.

[0093]

[0094] When detecting the peak, the difference function can be simply binarized. m(k) is the binarized sequence.

[0095]

[0096] Then, we differentiate m(k) again to get the peak sequence n(k). The value of 1 indicates a positive peak, and the value of -1 indicates a negative peak.

[0097]

[0098] Traverse each positive and negative peak and find the maximum value.

[0099] Starting from the second detection, the reference signal and the acquired signal are correlated using the FFT algorithm to quickly determine the layer position. Based on this, fine positioning correction is performed through peak detection to reduce positioning errors. Based on the current positioning position, the intercepted signal is used as the newly generated reference signal, mixed with the original signal at a weight of 1:9, and the reference signal is updated for subsequent detection. The relevant calculation steps are as follows:

[0100] According to the parity and even-imaginary-real properties, we can know that for real sequence x(n):

[0101] DFT(x(n))=X(k) DFT(x(-n))=X * (k);

[0102] FFT can be considered as linear convolution if the following equation is satisfied: where N1 is the original length of the x(i) sequence, N2 is the original length of the y(i) sequence, and N is the length of the x(i) and y(i) sequences after zero padding.

[0103] N1+N2-1≤N;

[0104] Therefore, the correlation calculation can be written as follows. Where x(i) is the sequence to be detected, y(i) is the reference signal sequence, r xy (n) is the sequence of correlation functions.

[0105]

[0106] From the above formula, we can know that we can calculate the FFT transforms X(k) and Y(k) of x(n) and y(n) respectively, take the conjugate of Y(k), and then multiply them to get the inverse transform.

[0107] The exception handling section is used to handle situations such as tracking loss and abnormal jumps in continuous acquisition mode. Solutions include expanding the search range and retesting, using the last detected data and skipping detection, etc. Exception handling can enhance the robustness of the algorithm.

[0108] Ultimately, by integrating the layer calculated from each A-Scan data, combined with the sampling rate and the electromagnetic parameters of the ice, the ice thickness can be calculated, achieving the goal of the automatic ice thickness identification algorithm.

[0109] This embodiment also provides a method for calculating the ice sheet bearing capacity, including:

[0110] P=Ah 2 ;

[0111] Where P is the allowable bearing capacity, in t; h is the effective thickness of ice, in m; A is a coefficient determined by the ice strength, crack coefficient, and temperature influence coefficient. The specific formula is as follows:

[0112]

[0113] Where N represents the strength and crack coefficient. The default value is N = 1 for cracks and N = 1.143 for no cracks. K represents the temperature influence coefficient, and e is the absolute value of the average temperature over the past three days. K only works when the average temperature is below zero. For zero and above, K = 1.

[0114] The above bearing capacity calculation formula is for intact, well-frozen ice sheets and is not applicable to accumulated ice sheets or ice sheets with cracks. Furthermore, the ice sheet thickness for a single person walking on it should be at least 10 cm, for a snowmobile at least 18 cm, and for a 4x4 vehicle at least 38 cm.

[0115] In January 2024, the equipment was tested on the surface of the Songhua River in Harbin, Heilongjiang Province, and a set of data was collected. The average temperature for the first three days was -18°C. A drilling verification was performed. The drilling results were consistent with the handheld ice detection radar measurement results, with an absolute measurement error of less than 0.5cm. As shown in the following table:

[0116]

[0117] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0118] The descriptions in this specification refer only to preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Furthermore, reference to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples" implies that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative descriptions of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples described in this specification, as well as features from different embodiments or examples, unless otherwise specified. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed to indicate or imply relative importance or to implicitly specify the number of the technical features indicated. Therefore, features designated "first" or "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined. Any process or method description in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code comprising one or more executable instructions for implementing a custom logic function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed in a different order than shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of the present invention pertain. The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logic function, can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatuses, or devices. For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution systems, apparatuses, or devices. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or N wirings (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, then editing, interpreting, or processing in other suitable ways as necessary, and then storing it in a computer memory. It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one of the following technologies known in the art or their combination can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] Those skilled in the art will appreciate that all or part of the steps carried out in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment. In addition, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

Claims

1. A method for measuring ice thickness, characterized in that: The method comprises: Steps for collecting preset algorithms and algorithm parameters; The step of preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm; A step of performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal; A step of obtaining a layer detection result of the preprocessed high-frequency electromagnetic wave echo signal according to the result of the peak detection; The step of obtaining the thickness of the ice layer according to the result of the layer detection.

2. The ice thickness measurement method according to claim 1, characterized in that: The preset algorithms are peak detection and correlation detection.

3. The ice thickness measurement method according to claim 2, characterized in that: The preset algorithm parameters include a reading path, a header byte length, a filter order, a filter type, and a cutoff frequency.

4. The ice thickness measurement method according to claim 1, characterized in that: The preprocessing specifically includes filtering the collected high-frequency electromagnetic wave echo signal through an FIR filter.

5. The ice thickness measurement method according to claim 1, characterized in that: The peak signal obtained by the peak detection is used as a layer reference signal, and the layer detection result is obtained through the layer reference signal and correlation calculation.

6. The ice thickness measurement method according to claim 5, characterized in that: The ice layer thickness is obtained based on the layer detection results, combined with the sampling rate and the electromagnetic parameters of the ice.

7. The ice thickness measurement method according to claim 1, characterized in that: The method also includes: In the continuous acquisition mode, when the peak detection result data is abnormal, the abnormal step is skipped by expanding the search range and re-detecting or using the previous detection data.

8. Ice thickness measuring device, characterized in that: The device comprises: A module for collecting preset algorithms and algorithm parameters; A module for preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm; A module for performing peak detection on the pre-processed high-frequency electromagnetic wave echo signal; A module for obtaining a layer detection result of the pre-processed high-frequency electromagnetic wave echo signal according to the result of the peak detection; According to the result of the layer detection, a module of ice layer thickness is obtained.

9. A computer storage medium for storing a computer program, characterized in that When the computer program is read by a computer, the computer executes the method according to claim 1 .

10. A computer comprising a processor and a storage medium, characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method according to claim 1 .

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