A detection method and system for the assembly process of a crank connecting rod mechanism

Abstract

The present invention discloses a detection method and system for the assembly process of a crank connecting rod mechanism, including: obtaining the tolerance values of the historical assembly of each component in the crank connecting rod mechanism, thereby constructing an assembly process detection library; using a hybrid clustering algorithm to cluster the assembly process detection library to determine the normal process assembly threshold and the abnormal process assembly threshold; the hybrid clustering algorithm includes optimizing each particle in the particle swarm algorithm within the decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k-th iteration step and the k + 1-th iteration step to the fitness value of the k-th iteration step; obtaining the tolerance values of the actual assembly of each component in the crank connecting rod mechanism, and comparing them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank connecting rod mechanism, effectively improving the engine assembly accuracy.

Description

Technical Field

[0001] The present invention relates to the field of assembly technology in intelligent manufacturing of engines, and particularly to a method and system for detecting the assembly process of a crank - connecting rod mechanism. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] The assembly quality of an automotive engine has a very important impact on performance indicators such as the fuel consumption rate, torque, and power of the vehicle. In actual production and manufacturing, the assembly process of each component of the crank - connecting rod mechanism directly affects the position of the engine piston in the cylinder, thereby affecting the change in the compression ratio. The compression ratio directly determines the power performance, economy, and emissions of the engine. Especially nowadays, engines are required to meet emission requirements. The fluctuation of the tolerances of each component of the crank - connecting rod mechanism will directly affect the change in the compression ratio, resulting in inconsistencies in engine performance, especially in emissions. Furthermore, currently, the tolerance detection of each component of the engine crank - connecting rod mechanism is carried out individually, and there is no analysis system for simultaneously detecting multiple parameters. Therefore, it is very necessary to develop a big - data analysis system for the assembly process of the engine crank - connecting rod mechanism. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a method and system for detecting the assembly process of a crank - connecting rod mechanism. The firefly algorithm is used to improve the particle swarm algorithm, and the improved firefly and particle swarm fusion algorithm is applied to the clustering algorithm to cluster the tolerance value data of the historical assembly of each component, so as to obtain the normal process assembly threshold and the abnormal process assembly threshold, thereby determining the normal or abnormal state of the assembly tolerance of each component of the crank - connecting rod mechanism, realizing the detection of the assembly process of the crank - connecting rod mechanism, and effectively improving the engine assembly accuracy.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present invention provides a method for detecting the assembly process of a crank - connecting rod mechanism, including:

[0007] Obtain the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism, and thus construct an assembly process detection library;

[0008] Use the hybrid clustering algorithm to cluster the assembly process detection library to determine the normal process assembly threshold and the abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement to the particle swarm algorithm, where each particle searches for the optimal solution within the decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k - th iteration step and the (k + 1) - th iteration step to the fitness value of the k - th iteration step;

[0009] Obtain the tolerance values of the actual assembly of each component in the crank - connecting rod mechanism, and compare them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank - connecting rod mechanism.

[0010] As an alternative implementation, the optimization of the particle swarm algorithm is improved by using the firefly algorithm. Each particle searches for the position of the firefly with the highest brightness within the decision domain range, and updates the velocity and position of the particle accordingly.

[0011] As an alternative implementation, the process of correcting the decision domain range includes: if the ratio is less than the set threshold, correct the decision domain radius to [0.5 + 0.5|cos(YLF i k )|]·r i ; where YLF i k is the fitness value of particle i at the k - th iteration step, and r i is the decision domain radius.

[0012] As an alternative implementation, when the ratio is always less than the set threshold after times of optimization, correct the decision domain radius to [2 + 2|cos(YLF i k )|]·r i , if the fitness value at this time is greater than the original fitness value, update the particle swarm position, otherwise, do not update the particle swarm position.

[0013] As an alternative implementation, the process of correcting the decision domain range includes: if the ratio is not less than the set threshold, correct the decision domain radius to [1 + |cos(YLF i k )|]·r i ; where YLF i k is the fitness value of particle i at the k - th iteration step, and r i is the decision domain radius.

[0014] As an alternative implementation, the process of clustering the assembly process detection library includes: clustering based on the improved particle swarm algorithm. The optimal position obtained by the particle swarm through optimization is the clustering center that minimizes the fitness value, and the fitness value is the Euclidean distance from the tolerance value of the actual assembly of each component to the clustering center.

[0015] As an alternative embodiment, the tolerance values for the assembly of each component include: the dimensional tolerance of the piston pin hole diameter, the dimensional tolerance of the piston pin diameter, the dimensional tolerance of the small end diameter of the connecting rod, the dimensional tolerance of the center distance between the hole and the shaft from the small end to the big end of the connecting rod, the dimensional tolerance of the big end diameter of the connecting rod, the dimensional tolerance of the crankpin diameter, and the dimensional tolerance of the connecting rod cap diameter.

[0016] As an alternative embodiment, after clustering the assembly process detection library, clusters are obtained. An abnormal process assembly threshold or a normal process assembly threshold is delimited by setting a tolerance threshold around each cluster. The union of the assembly thresholds obtained for each cluster is taken to obtain an assembly process decision library for comparison.

[0017] In a second aspect, the present invention provides a crank connecting rod mechanism assembly process detection system, including:

[0018] A detection library construction module, configured to obtain the tolerance values of the historical assembly of each component in the crank connecting rod mechanism, thereby constructing an assembly process detection library;

[0019] A decision library construction module, configured to cluster the assembly process detection library by using a hybrid clustering algorithm to determine a normal process assembly threshold and an abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement to the particle swarm algorithm, where each particle searches for an optimum within a decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k-th iteration step and the (k + 1)-th iteration step to the fitness value of the k-th iteration step;

[0020] A state detection module, configured to obtain the tolerance values of the actual assembly of each component in the crank connecting rod mechanism, and compare them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the crank connecting rod mechanism assembly process.

[0021] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in the first aspect is completed.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first aspect is completed.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] The present invention provides a detection method and system for the assembly process of a crank - connecting rod mechanism. The hybrid clustering algorithm is used to cluster the tolerance value data of the historical assembly of each component. The hybrid clustering algorithm is an improved particle swarm algorithm using the firefly algorithm. The improved firefly and particle swarm fusion algorithm is applied to the clustering analysis algorithm to improve the performance of the clustering analysis algorithm, so as to obtain more accurate and reasonable detection results, realize the detection of the assembly process of the crank - connecting rod mechanism, and effectively improve the engine assembly accuracy.

[0025] The present invention provides a detection method and system for the assembly process of a crank - connecting rod mechanism. The firefly algorithm is used to improve the iterative update strategy of the particle swarm algorithm. Each particle in the particle swarm algorithm has the optimization characteristics of the firefly algorithm at the same time, and each particle searches for the optimal solution within the decision domain range of the firefly. The decision domain range of the firefly is corrected according to the ratio of the difference between the fitness values of the k - th iteration step and the (k + 1) - th iteration step to the fitness value of the k - th iteration step, so as to enhance the local and global optimization capabilities of the particles.

[0026] Advantages of additional aspects of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0028] Figure 1 It is a flowchart of the detection method for the assembly process of the crank - connecting rod mechanism provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0031] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0032] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0033] Embodiment 1

[0034] This embodiment provides a detection method for the assembly process of a crank - connecting rod mechanism, including:

[0035] Obtaining the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism, thereby constructing an assembly process detection library;

[0036] Using a hybrid clustering algorithm to cluster the assembly process detection library to determine the normal process assembly threshold and the abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement on the particle swarm algorithm, where each particle optimizes within the decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k - th iteration step and the (k + 1)-th iteration step to the fitness value of the k - th iteration step;

[0037] Obtaining the tolerance values of the actual assembly of each component in the crank - connecting rod mechanism, and comparing them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank - connecting rod mechanism.

[0038] The following combines Figure 1 to elaborate on the method of this embodiment in detail.

[0039] In this embodiment, each component in the crank - connecting rod mechanism includes: a piston, a piston pin, a connecting rod, a crankshaft, a connecting rod bearing shell, and a connecting rod cap;

[0040] The assembly tolerance values of the above - mentioned each component collected include: the diameter size tolerance of the piston pin hole, the diameter size tolerance of the piston pin, the diameter size tolerance of the small end of the connecting rod in the connecting rod, the hole - shaft center distance size tolerance from the small end to the big end of the connecting rod in the connecting rod, the diameter size tolerance of the big end of the connecting rod in the connecting rod, the diameter size tolerance of the crank pin, and the diameter size tolerance of the connecting rod cap;

[0041] Constructing an assembly process detection library for the crank - connecting rod mechanism by collecting the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism.

[0042] In this embodiment, the hybrid clustering algorithm is specifically as follows: The firefly algorithm is used to improve the iterative update strategy of the particle swarm algorithm, so that each particle in the particle swarm algorithm has the optimization characteristics of the firefly algorithm, and each particle optimizes within the decision domain of the firefly. The decision domain of the firefly is corrected according to the ratio of the difference between the fitness values of the k-th iteration step and the (k + 1)-th iteration step to the fitness value of the k-th iteration step. Finally, the firefly and particle swarm fusion algorithm is applied to the K-means clustering analysis algorithm to improve the performance of the K-means clustering analysis algorithm, thereby obtaining the hybrid clustering algorithm.

[0043] The optimization process of each particle for the particle swarm includes:

[0044] In a population composed of m particles, the position of the i-th particle is represented as x i =(x i1 ,x i2 ,...x in ), where i = 1, 2,..., m; n is the dimension of the particle, and its velocity is v i =(v i1 ,v i2 ,...v in ); its personal best is yp i =(yp i1 ,yp i2 ,...,yp in ), which is the optimal position searched by particle i; the global best of the population is yp g =(yp g1 ,yp g2 ,...,yp gn ), which is the optimal position of the fitness value searched by all particles.

[0045] Each particle x i has a firefly optimization process within its decision domain to find the position X p of the firefly with the highest brightness:

[0046]

[0047] where r i is the decision domain radius of the firefly; γ is the attraction coefficient, generally taking any constant between [0.01, 100]; β is the attractiveness, usually β = 1; a pi is the Cartesian distance between the optimal position of the fitness value searched by particle i within the decision domain radius and the current position of the particle, that is

[0048] Update the velocity and position of the particle:

[0049]

[0050]

[0051] where ω is the weight; k is the current iteration number; c 1 and c 2 are learning factors, also known as acceleration constants. Usually, c 1 = c 2 = 2; rand is a random number between [0, 1]; is the velocity of the i-th particle at the k-th iteration step and the (k + 1)-th iteration step, is the position of the i-th particle at the k-th iteration step and the (k + 1)-th iteration step.

[0052] If the fitness value of the fitness function is smaller than that of the previous iteration step, the above update method is maintained; if the fitness value is larger than that of the previous iteration step, the update methods for the particle velocity and position are:

[0053]

[0054]

[0055] The decision domain range of the firefly is corrected according to the ratio of the difference in fitness values between the k-th iteration step and the (k + 1)-th iteration step to the fitness value of the k-th iteration step. Specifically:

[0056] The fitness value obtained by the improved firefly and particle swarm fusion algorithm at the k-th iteration step is YLF i k , and the fitness value obtained by the improved firefly and particle swarm hybrid algorithm at the (k + 1)-th iteration step is YLF i k+1 , then the ratio is:

[0057]

[0058] If YL(k) is less than the set threshold, taking 1% as an example in this embodiment, the radius of the decision domain of the firefly with attractive behavior is reduced to [0.5 + 0.5|cos(YLF i k )|]·r i , to enhance the local optimization ability;

[0059] When the above optimization process is continuously carried out times, and the value of YL(k) is always less than the set threshold, at this time, the algorithm is randomly perturbed, and the radius of the decision domain of the firefly with attractive behavior is increased to [2 + 2|cos(YLF i k )|]·r i;Recalculate the fitness value. If the current fitness value is better than the original one, update the particle swarm position; otherwise, do not update the particle swarm position.

[0060] If YL(k) is not less than the set threshold, increase the radius of the decision domain of the firefly's attraction behavior to [1 + |cos(YLF i k )|]·r i , so as to enhance the global optimization ability.

[0061] In this embodiment, the firefly and particle swarm fusion algorithm is used in the K-means clustering algorithm. The parameters of the particles are calculated using the firefly and particle swarm fusion algorithm. Each particle represents a clustering method, and the action target of each particle represents the position of this clustering center. The fitness function is the Euclidean distance from all data to the nearest clustering center; the Euclidean distance is:

[0062]

[0063] where YLF i is the sum of the Euclidean distances from the i-th particle (tolerance value) to all clustering centers. N is the total number of clusters in the K-means clustering algorithm, m is the number of tolerance value data, n is the dimension of the tolerance value data, y ij is the actual assembly tolerance value of each component, and c t is the clustering center, that is, the optimal position of the particle swarm optimization.

[0064] The particle finds the clustering center that minimizes the fitness function by finding the shortest path from the initial position to the final position, thereby clustering and dividing the assembly process inspection library.

[0065] In this embodiment, the above hybrid clustering algorithm is used to cluster the assembly process inspection library to obtain the corresponding clusters, determine the normal process assembly threshold and the abnormal process assembly threshold, and construct the assembly process decision library;

[0066] Among them, in this embodiment, the number of clusters is set to 3, thus forming 3 clusters with the tolerances of each component of the crank connecting rod mechanism being too large, medium, and too small. The tolerance values of the outermost 3% of the components in the two clusters with too large and too small tolerances of each component of the crank connecting rod mechanism are used as the abnormal process assembly threshold, and the tolerance values of the outermost 1% of the components in the cluster with moderate tolerances of each component of the crank connecting rod mechanism are used as the normal process assembly threshold. Compare the union of the thresholds obtained by the two methods to construct the assembly process decision library.

[0067] In this embodiment, the tolerance values of the actual assembly of each component in the crank - connecting rod mechanism are collected in real - time, and compared with the normal process assembly threshold and the abnormal process assembly threshold in the assembly process decision - making library to determine the normal or abnormal state of the assembly tolerance of each component in the crank - connecting rod mechanism, thereby realizing the detection of the assembly process of the crank - connecting rod mechanism and effectively improving the engine assembly accuracy.

[0068] Embodiment 2

[0069] This embodiment provides a detection system for the assembly process of a crank - connecting rod mechanism, including:

[0070] A detection library construction module, configured to obtain the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism, thereby constructing an assembly process detection library;

[0071] A decision - making library construction module, configured to cluster the assembly process detection library by using a hybrid clustering algorithm to determine the normal process assembly threshold and the abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement to the particle swarm algorithm, where each particle optimizes within the decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k - th iteration step and the (k + 1) - th iteration step to the fitness value of the k - th iteration step;

[0072] A state detection module, configured to obtain the tolerance values of the actual assembly of each component in the crank - connecting rod mechanism, and compare them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank - connecting rod mechanism.

[0073] It should be noted here that the above - mentioned modules correspond to the steps described in Embodiment 1. The examples and application scenarios implemented by the above - mentioned modules and the corresponding steps are the same, but are not limited to the content disclosed in the above - mentioned Embodiment 1. It should be noted that the above - mentioned modules, as part of the system, can be executed in a computer system such as a set of computer - executable instructions.

[0074] In more embodiments, there is also provided:

[0075] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0076] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general - purpose processors, digital signal processors DSP, application - specific integrated circuits ASIC, field - programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0077] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0078] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the method described in Embodiment 1.

[0079] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0080] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0081] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A detection method for the assembly process of a crank - connecting rod mechanism, characterized in that, it includes: Obtaining the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism, and thus constructing an assembly process detection library; Using a hybrid clustering algorithm to cluster the assembly process detection library to determine the normal process assembly threshold and the abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement to the particle swarm algorithm, where each particle optimizes within the decision domain range, and the decision domain range is corrected according to the ratio of the difference between the fitness values of the k - th iteration step and the (k + 1)-th iteration step to the fitness value of the k - th iteration step; the optimization of the particle swarm algorithm is improved by using the firefly algorithm, and each particle searches for the position of the firefly with the highest brightness within the decision domain range to update the velocity and position of the particle; Obtaining the tolerance values of the actual assembly of each component in the crank - connecting rod mechanism, and comparing them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank - connecting rod mechanism.

2. The detection method for the assembly process of a crank - connecting rod mechanism according to claim 1, characterized in that, The process of correcting the decision domain range includes: if the ratio is less than the set threshold, correct the decision domain radius to ; where is the fitness value of particle at the th iteration step, and is the decision domain radius.

3. The detection method for the assembly process of a crank - connecting rod mechanism according to claim 2, characterized in that, After times of continuous optimization, if the ratio is always less than the set threshold, then correct the decision domain radius to . If the fitness value at this time is greater than the original fitness value, update the particle swarm position; otherwise, do not update the particle swarm position. Among them, ; is the particle The fitness value of the i-th iteration step.

4. The detection method for the assembly process of a crank - connecting rod mechanism according to claim 1, characterized in that, The process of correcting the decision domain range includes: if the ratio is not less than the set threshold, correct the decision domain radius to ; where is the fitness value of particle at the th iteration step, and is the decision domain radius.

5. The detection method for the assembly process of a crank - connecting rod mechanism according to claim 1, characterized in that, The process of clustering the assembly process detection library includes: clustering based on the improved particle swarm algorithm, and the optimal position obtained by the particle swarm through optimization is the clustering center that minimizes the fitness value, and the fitness value is the Euclidean distance from the tolerance value of the actual assembly of each component to the clustering center.

6. The detection method for the assembly process of a crank - connecting rod mechanism according to claim 1, characterized in that, The tolerance values of the assembly of each component include: the diameter size tolerance of the piston pin hole, the diameter size tolerance of the piston pin, the diameter size tolerance of the small end of the connecting rod, the hole - shaft center distance size tolerance from the small end to the big end of the connecting rod, the diameter size tolerance of the big end of the connecting rod, the diameter size tolerance of the crank pin, and the diameter size tolerance of the connecting rod cap; After clustering the assembly process detection library, clusters are obtained. An abnormal process assembly threshold or a normal process assembly threshold is delimited by setting a tolerance threshold around the periphery of each cluster, and the union of the assembly thresholds obtained under each cluster is taken to obtain an assembly process decision library for comparison.

7. A detection system for the assembly process of a crank - connecting rod mechanism, characterized in that, it includes: A detection library construction module configured to obtain the tolerance values of the historical assembly of each component in the crank - connecting rod mechanism and thus construct an assembly process detection library; A decision library construction module, configured to cluster an assembly process detection library by using a hybrid clustering algorithm to determine a normal process assembly threshold and an abnormal process assembly threshold; the hybrid clustering algorithm includes an improvement to a particle swarm algorithm, wherein each particle performs optimization within a decision domain range, and the decision domain range is corrected according to the ratio of the fitness value difference between the k-th iteration step and the (k + 1)-th iteration step to the fitness value of the k-th iteration step; the optimization of the particle swarm algorithm is improved by using a firefly algorithm, and each particle searches for the position of the firefly with the highest brightness within the decision domain range, and updates the speed and position of the particle accordingly; A state detection module, configured to obtain the tolerance values of the actual assemblies of the components in the crank connecting rod mechanism, and compare them with the normal process assembly threshold and the abnormal process assembly threshold to determine the normal or abnormal state of the assembly process of the crank connecting rod mechanism.

8. An electronic device, characterized in that, it includes a memory and a processor, and computer instructions stored on the memory and running on the processor, and when the computer instructions are run by the processor, the method according to any one of claims 1-6 is completed.

9. A computer-readable storage medium, characterized in that, it is used to store computer instructions, and when the computer instructions are executed by the processor, the method according to any one of claims 1-6 is completed.

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