A modulus analysis method for multi-system glass materials

Abstract

The present invention discloses a method for analyzing the modulus of multi-system glass materials, the method comprising: (1) establishing a new descriptor parameter selection strategy based on a weight factor optimization parameter correlation calculation model, which can not only target the situation of uneven sampling, but also combine reference prior knowledge and reduce the influence of outliers to select variable parameters with the highest correlation and the greatest independence with the modulus performance. (2) Constructing a descriptor that can accurately fit the modulus performance of glass materials, providing an effective research method for exploring the relationship between the microstructure of glass materials and their macroscopic properties. (3) Based on the constructed descriptor, the modulus of multi-system glass is calculated and predicted, and the modulus of new system glass can be accurately predicted. The present invention can calculate and predict the modulus of multi-system glass, and provide a research method for revealing the enhancement mechanism of glass modulus, which is of great significance to improving and developing the calculation and prediction ability of glass modulus.

Description

Technical Field

[0001] The invention belongs to the technical field of glass modulus performance prediction, in particular to a multi-system glass material modulus analysis method. Background Art

[0002] As an important engineering material, the mechanical properties of glass materials play a vital role in functional and structural design. Designing glass materials with high modulus has always been the focus and difficulty in related fields. At present, the quantitative structure-property relationship analysis method has significant advantages in calculating and predicting the modulus of glass materials. This method combines molecular simulation at the microscopic level with statistical models of macroscopic performance, which can achieve fast and efficient material performance prediction, greatly improve R&D efficiency and reduce costs.

[0003] At present, there have been many reports on the research on predicting and calculating material properties based on quantitative structure-property relationship analysis. For example, the patent application with publication number CN11623001A discloses a method for modifying the structure and improving the performance of natural ester molecules based on quantitative structure-property relationship analysis. This method is used to study the molecular relationship between the intrinsic molecular structure of natural ester insulating liquid and the impact on electrical and cold flow properties, and based on this, the natural ester molecules are structurally modified and the performance is improved. It fully demonstrates the important significance of quantitative structure-property relationship analysis methods in the field of materials research. The document "Journal of Non-Crystalline Solids, 2020, 530: 119772." For SiO 2- A quantitative structure-property relationship model was constructed for Al2O3-CaO glass. The square residual coefficient R 2 It can reach 0.98.

[0004] However, the current calculation and prediction of glass modulus based on quantitative structure-property relationship analysis methods are basically aimed at glass materials of a single system, and there is still a lack of relevant research on glass materials of multiple different systems. Since glass materials have many components, up to billions of species, it is difficult to build an independent quantitative structure-property relationship analysis model for glass materials of all systems. In view of this situation, it is necessary to develop a method that can calculate and predict the modulus of new multivariate glass materials based on several systems of glass. Summary of the invention

[0005] The purpose of the present invention is to provide a method for calculating and predicting the modulus of multi-system glass materials based on the quantitative structure-property relationship analysis method of oxide formation energy in view of the problems existing in the above-mentioned prior art. This method can construct a glass modulus performance descriptor with good fitting accuracy, which not only helps to reveal the strengthening mechanism of the glass material modulus, but also can more accurately predict the modulus of new system glass materials. This has important basic theoretical support and practical application value for the research and development and design of new system glass materials with high modulus characteristics.

[0006] The technical solution to achieve the purpose of the present invention is: a multi-system glass material modulus analysis method, the method comprising the following steps:

[0007] Step 1, collecting the formula, density and modulus parameters of the glass material, and constructing the corresponding glass molecular model;

[0008] Step 2, calculating the coordination number of the glass molecular model;

[0009] Step 3, constructing a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0010] Step 4, calculating the descriptors of glasses with different ratios and fitting them with their moduli;

[0011] Step 5: For the new multi-system glass material to be analyzed, calculate its descriptor and put it into the fitting result of step 4 to obtain the modulus of the new multi-system glass material.

[0012] Furthermore, step 2 includes calculating the coordination number between the cation and the oxygen atom.

[0013] Furthermore, the descriptor model based on the quantitative structure-performance relationship analysis method in step 3 is:

[0014] ;

[0015] In the formula, is the modulus descriptor, N is the total number of atoms in the model, Indicates The number of atoms of the cations, Indicates Anions, Represent the total number of cations and anions, respectively. express The average coordination number of atoms, represents the energy parameter, is the multiplication factor.

[0016] Furthermore, the energy parameter The specific selection process includes:

[0017] Step 3-1, collecting glass material modulus data based on a glass material performance database, and selecting a variety of different energy parameters;

[0018] Step 3-2, calculating the correlation between different energy parameters and modulus, and selecting energy parameters whose correlation is higher than a first preset threshold;

[0019] Step 3-3, calculating the correlation between different energy parameters obtained by screening in step 3-2. If the correlation between two energy parameters is greater than a second preset threshold, it means that the two have a multicollinear relationship, and the energy parameters with relatively small correlation with the modulus are removed;

[0020] Step 3-4, establish a support vector machine regression model, calculate and evaluate the predictive ability of different energy parameters on glass modulus performance, and select the squared residual coefficient The maximum energy parameter is used as the energy parameter .

[0021] Furthermore, the energy parameter is the oxide formation energy.

[0022] Furthermore, the calculation formula for calculating the correlation between different energy parameters and modulus in step 3-2 is:

[0023]

[0024] In the formula, is the correlation coefficient between energy parameter and glass modulus, n is the number of energy parameters, is the i-th energy parameter, For x i The weight factor of is the average energy parameter, is the weight factor of the i-th modulus data, is the i-th modulus data, is the average of modulus data, and m is the number of modulus data.

[0025] Furthermore, the calculation formula for calculating the correlation between different energy parameters in step 3-3 is:

[0026]

[0027] In the formula, is the correlation coefficient between two different energy parameters, is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of .

[0028] Furthermore, the , and It conforms to normal distribution.

[0029] Furthermore, the second preset threshold is greater than the first preset threshold.

[0030] On the other hand, a multi-system glass material modulus analysis system is provided, the system comprising:

[0031] The first module is used to collect the formula, density and modulus parameters of glass materials and build the corresponding glass molecular model;

[0032] The second module is used to calculate the coordination number of the glass molecule model;

[0033] The third module is used to construct a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0034] The fourth module is used to calculate the descriptors of glasses with different ratios and fit them to their moduli;

[0035] The fifth module is used to calculate the descriptor of the new multi-system glass material to be analyzed and put it into the fitting result of the fourth module to obtain the modulus of the new multi-system glass material.

[0036] Compared with the prior art, the present invention has the following significant advantages:

[0037] (1) The present invention establishes a new descriptor parameter selection strategy based on a weight factor optimization parameter correlation calculation model, which can not only address the situation of uneven sampling, but also combine reference prior knowledge and reduce the influence of outliers to select variable parameters with the highest correlation and the greatest independence with modulus performance.

[0038] (2) The present invention constructs a descriptor based on the oxide formation energy that can accurately fit the modulus properties of glass materials , providing an effective research method for exploring the relationship between the microstructure of glass materials and their macroscopic properties.

[0039] (3) The glass modulus descriptor constructed by the present invention is not only applicable to a single system of glass materials, but also to a variety of different systems of glass materials, and can accurately calculate and predict the modulus of new system glass materials.

[0040] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 In one embodiment Schematic diagram of the energy parameter selection strategy flow of the descriptor.

[0042] Figure 2 Schematic diagram of the molecular model of glass material.

[0043] Figure 3 : are radial distribution functions T(r) (solid line) and coordination numbers (CN) (dashed line) of cation-oxygen atoms in different system glass materials in one embodiment, where Figure 3 (a) to (d) are schematic diagrams of the radial distribution function T (r) and coordination number of cation-oxygen atoms in SiO2, 33SiO2-33CaO-33Al2O3, 75SiO2-25K2O, and 15 SiO2-55 Al2O3-30Y2O3, respectively.

[0044] Figure 4 An example QSPR analysis descriptor The fitting results of the experimental moduli of multiple glass systems in the database.

[0045] Figure 5 An example QSPR analysis descriptor The fitting results of the experimental modulus of multiple glass systems in the experiment.

[0046] Figure 6 For an example, the test data falls into two Schematic diagram of the results in the fitting diagram with the database glass performance data. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0049] In one embodiment, the present invention provides a multi-system glass material modulus analysis method, the method comprising the following steps:

[0050] Step 1, collecting the formula, density and modulus parameters of the glass material, and constructing the corresponding glass molecular model;

[0051] Preferably, a glass performance database, such as INTERGLAD 8.3 or SciGlass next, is used to select glass materials with different components and extract their composition, density and modulus performance information.

[0052] Preferably, material molecular modeling software, such as Packmol, is used to model different component glasses based on the glass material density information. The specific method is as follows: based on the collected glass formula, different oxide molecules are randomly placed into a simulation box to obtain a .data model file, wherein the box size is calculated based on the density of the glass.

[0053] Step 2, calculating the coordination number of the glass molecular model;

[0054] Preferably, the molecular dynamics software Lammps and VMD are used to calculate the coordination number of the glass molecular model. The specific method is as follows: first, the Lammps software is used to read the .data model file through the read_data command, and the temperature is raised and lowered through the fix command to obtain a glass molecular model file with an amorphous structure and stability, and the output format is a standard .xyz file. After that, the VMD software is used to read the .xyz file through the new molecular command, and then the Extensions→Analysis→Radial Pari Distribution Function g(r) command is used to calculate the coordination number of different cations and oxygen atoms.

[0055] Step 3, constructing a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0056] The descriptor model based on the quantitative structure-performance relationship analysis method is:

[0057]

[0058] In the formula, is the modulus descriptor, N is the total number of atoms in the model, Indicates The number of atoms of the cations, Indicates Anions ( = Si, Al, Mg, Na, Ca...; = O), Represent the total number of cations and anions, respectively. express The average coordination number of atoms, represents the energy parameter, It is a multiplicative factor used to evaluate the effect of cations on the glass network, which represents the maximum number of SiO4 units connected with iO bonds and is used to better evaluate the contribution of each bond to the overall network strength.

[0059] Step 4, calculating the descriptors of glasses with different ratios and fitting them with their moduli;

[0060] Preferably, the Original software is used to fit the modulus and modulus descriptors of different types of glass materials. , draw the fitting curve.

[0061] Step 5: For the new multi-system glass material to be analyzed, calculate its descriptor and put it into the fitting result of step 4 to obtain the modulus of the new multi-system glass material.

[0062] Further, in one embodiment, in combination Figure 1 , the energy parameter The specific selection process includes:

[0063] Step 3-1, collecting glass material modulus data based on the glass material performance database, and selecting a variety of different energy parameters, including cation radius, formation energy, polymerization enthalpy, atomic single bond strength, diatomic molecular bond strength, bond dissociation energy in diatomic cations, lattice energy, formation enthalpy, oxidation reaction enthalpy, oxidation reaction free energy, heat capacity, formation entropy, reaction enthalpy, activation energy, ionization energy, binding energy, standard molar formation enthalpy, standard formation free energy, solution heat, melting enthalpy, etc.;

[0064] Step 3-2, calculate the correlation between different energy parameters and modulus, and select energy parameters with correlation higher than the first preset threshold; here, the correlation calculation formula is:

[0065]

[0066] In the formula, is the correlation coefficient between energy parameter and glass modulus, n is the number of energy parameters, is the i-th energy parameter, For x i The weight factor of is the average energy parameter, is the weight factor of the i-th modulus data, is the i-th modulus data, is the average of modulus data, m is the number of modulus data;

[0067] Step 3-3, calculating the correlation between different energy parameters obtained by screening in step 3-2. If the correlation between two energy parameters is greater than a second preset threshold, it means that the two have a multicollinear relationship, and the energy parameters with relatively small correlation with the modulus are removed;

[0068] Here, the correlation calculation formula is:

[0069]

[0070] In the formula, is the correlation coefficient between two different energy parameters, is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of .

[0071] Preferably, the , and It conforms to normal distribution.

[0072] Preferably, the second preset threshold is greater than the first preset threshold.

[0073] Preferably, the first preset threshold is 0.3, and the second preset threshold is 0.9.

[0074] Step 3-4, establish a support vector machine regression model, calculate and evaluate the predictive ability of different energy parameters on glass modulus performance, and select The maximum energy parameter is used as the energy parameter .

[0075] The calculation results show that the oxide formation energy (PFE) has the strongest correlation with the modulus properties of glass materials, so the formation energy is used to construct the modulus descriptor of glass materials based on the quantitative structure-property analysis method.

[0076] Preferably, in some embodiments, the formation energy and multiplication factor values ​​of different oxides are respectively as shown in Table 1 below.

[0077] Table 1 Formation energy and multiplication factor values ​​of different oxides

[0078]

[0079] In one embodiment, a multi-system glass material modulus analysis system is provided, the system comprising:

[0080] The first module is used to collect the formula, density and modulus parameters of glass materials and build the corresponding glass molecular model;

[0081] The second module is used to calculate the coordination number of the glass molecule model;

[0082] The third module is used to construct a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0083] The fourth module is used to calculate the descriptors of glasses with different ratios and fit them to their moduli;

[0084] The fifth module is used to calculate the descriptor of the new multi-system glass material to be analyzed and put it into the fitting result of the fourth module to obtain the modulus of the new multi-system glass material.

[0085] For the specific definition of the multi-system glass material modulus analysis system, please refer to the definition of the multi-system glass material modulus analysis method mentioned above, which will not be repeated here. Each module in the above-mentioned multi-system glass material modulus analysis system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following is achieved:

[0087] Step 1, collecting the formula, density and modulus parameters of the glass material, and constructing the corresponding glass molecular model;

[0088] Step 2, calculating the coordination number of the glass molecular model;

[0089] Step 3, constructing a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0090] Step 4, calculating the descriptors of glasses with different ratios and fitting them with their moduli;

[0091] Step 5: For the new multi-system glass material to be analyzed, calculate its descriptor and put it into the fitting result of step 4 to obtain the modulus of the new multi-system glass material.

[0092] For the specific limitations of each step, please refer to the limitations of the multi-system glass material modulus analysis method mentioned above, which will not be repeated here.

[0093] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0094] Step 1, collecting the formula, density and modulus parameters of the glass material, and constructing the corresponding glass molecular model;

[0095] Step 2, calculating the coordination number of the glass molecular model;

[0096] Step 3, constructing a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number;

[0097] Step 4, calculating the descriptors of glasses with different ratios and fitting them with their moduli;

[0098] Step 5: For the new multi-system glass material to be analyzed, calculate its descriptor and put it into the fitting result of step 4 to obtain the modulus of the new multi-system glass material.

[0099] For the specific limitations of each step, please refer to the limitations of the multi-system glass material modulus analysis method mentioned above, which will not be repeated here.

[0100] As a specific example, in one of the embodiments, the present invention is further verified and explained.

[0101] The multi-system glass material modulus analysis method proposed in the present invention specifically includes:

[0102] (1) Collect the composition, density and modulus data of 9 different glass systems in the glass properties database.

[0103] The relevant data are shown in Table 2 below:

[0104] Table 2 Composition, density and modulus data of different glass systems

[0105]

[0106] (2) Based on the component parameters of the glass performance data, the initial molecular models of different glass materials were established using Packmol and VMD software. According to the molar ratio of different types of oxides, the oxide molecules were randomly placed into the model box to generate the glass molecular model glass.data. The total number of all oxide molecules was set to 10,000, and the initial size of the simulation box was set to 42Å×42Å×42Å.

[0107] (3) Use LAMMPS software to simulate the melting-cooling process of glass. First, use LAMMPS software to read the above glass.data file through the read_data command. Then, based on the glass density data, calculate the size parameters that the glass molecular model should reach, and use the deform command to compress the simulation box until the box size is consistent with the calculation parameters. Then, use the fix nvt command to minimize the energy of the model and relax it for 100 picoseconds (ps) at 1K and 300K to reduce the high internal energy caused by the initial random structure. Then, melt the model at 6000K for 100 ps and re-equilibrate at 5000K for 100 ps. After that, quench the temperature to 300K at a cooling rate of 5 K / ps. Finally, relax the model at 300K for 100 ps in NVT and NVE ensembles respectively to eliminate unreasonable structures and internal stresses that may exist in the model. The glass molecular model is as follows Figure 2 shown.

[0108] (4) VMD software was used to calculate the radial distribution function and coordination number of different cations and oxygen atoms in the glass molecular model. Some of the results are as follows: Figure 3 The coordination number refers to the number of oxygen atoms around the cation within the cutoff radius, and the cutoff radius is the first minimum value on the radial distribution function. The calculation results are shown in Table 3 below:

[0109] Table 3 Coordination number calculation results

[0110]

[0111] (5) The descriptor calculation formula of the quantitative structure-performance analysis method is used to construct a descriptor that can calculate and predict the glass model modulus. The relevant formula is as follows:

[0112]

[0113] In the formula, is the QSPR descriptor, N is the total number of atoms in the model, n i Indicates the number of atoms of the ith substance, CN ij represents the average coordination number of ij to atom (i = Si, Al, Mg, Na, Ca…; j = O), PFE ij is the formation energy of different oxides, m ij Is the multiplication factor. The formation energy and multiplication factor of different oxides refer to the following Table 4:

[0114] Table 4 Formation energy and multiplication factor of different oxides

[0115]

[0116] The descriptor calculation results are shown in Table 5 below:

[0117] Table 5 Descriptor calculation results

[0118]

[0119] (6) Use the original software to fit the modulus and descriptors of glass materials ,like Figure 4 shown.

[0120] (7) Carry out glass melting experiments to obtain bulk glass and measure its modulus. Based on the glass composition, calculate the coordination number of its oxide cation-oxygen atom and thus calculate the descriptor .

[0121] (8) Fitting test to obtain the modulus and descriptors of glass materials ,like Figure 5 shown.

[0122] (9) Putting the experimental data into the modulus and descriptors of multi-system glass In the fitting results, Figure 6 As shown, it can be seen that the test data are distributed around the fitting curve, and its development trend is similar to the fitting curve, which strongly confirms the important role of the disclosed glass material modulus analysis method in predicting glass modulus properties.

[0123] The present invention can calculate and predict the modulus of multiple glass systems, and provide a research method for revealing the enhancement mechanism of glass modulus, which is of great significance to improving and developing the ability to calculate and predict the modulus of glass.

[0124] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-system glass material modulus analysis method, characterized in that: The method comprises the following steps: Step 1, collecting the formula, density and modulus parameters of the glass material, and constructing the corresponding glass molecular model; Step 2, calculating the coordination number of the glass molecular model; Step 3, constructing a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number; The energy parameters The specific selection process includes: Step 3-1, collecting glass material modulus data based on a glass material performance database, and selecting a variety of different energy parameters; Step 3-2, calculating the correlation between different energy parameters and modulus, and selecting energy parameters whose correlation is higher than a first preset threshold; Step 3-3, calculating the correlation between different energy parameters obtained by screening in step 3-2. If the correlation between two energy parameters is greater than a second preset threshold, it means that the two have a multicollinear relationship, and the energy parameters with relatively small correlation with the modulus are removed; Step 3-4, establish a support vector machine regression model, calculate and evaluate the predictive ability of different energy parameters on glass modulus performance, and select the energy parameter with the largest square error coefficient as the energy parameter ; The energy parameters is the oxide formation energy; Step 4, calculating the descriptors of glasses with different ratios and fitting them with their moduli; Step 5: For the new multi-system glass material to be analyzed, calculate its descriptor and put it into the fitting result of step 4 to obtain the modulus of the new multi-system glass material.

2. The multi-system glass material modulus analysis method according to claim 1, characterized in that: Step 2 involves calculating the coordination number of the cation to the oxygen atom.

3. The multi-system glass material modulus analysis method according to claim 1, characterized in that: The descriptor model based on the quantitative structure-performance relationship analysis method in step 3 is: ； In the formula, is the modulus descriptor, N is the total number of atoms in the model, Indicates The number of atoms of the cations, Indicates Anions, Represent the total number of cations and anions, respectively. express The average coordination number of atoms, represents the energy parameter, is the multiplication factor.

4. The multi-system glass material modulus analysis method according to claim 1, characterized in that: The calculation formula for calculating the correlation between different energy parameters and modulus in step 3-2 is: ； In the formula, is the correlation coefficient between energy parameter and glass modulus, n is the number of energy parameters, is the i-th energy parameter, For x i The weight factor of is the average energy parameter, is the weight factor of the i-th modulus data, is the i-th modulus data, is the average of modulus data, and m is the number of modulus data.

5. The multi-system glass material modulus analysis method according to claim 1, characterized in that: The calculation formula for calculating the correlation between different energy parameters in step 3-3 is: ； In the formula, is the correlation coefficient between two different energy parameters, is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of is the energy parameter The i-th parameter in for The weight factor of is the energy parameter The average of .

6. The multi-system glass material modulus analysis method according to claim 5, characterized in that: Said , and It conforms to normal distribution.

7. The multi-system glass material modulus analysis method according to claim 1, characterized in that: The second preset threshold is greater than the first preset threshold.

8. A multi-system glass material modulus analysis system based on the method according to any one of claims 1 to 7, characterized in that: The system comprises: The first module is used to collect the formula, density and modulus parameters of glass materials and build the corresponding glass molecular model; The second module is used to calculate the coordination number of the glass molecule model; The third module is used to construct a descriptor model based on a quantitative structure-performance relationship analysis method, wherein the parameters of the descriptor model include an energy parameter and the coordination number; The fourth module is used to calculate the descriptors of glasses with different ratios and fit them to their moduli; The fifth module is used to calculate the descriptor of the new multi-system glass material to be analyzed and put it into the fitting result of the fourth module to obtain the modulus of the new multi-system glass material.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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