Slow disk scheduling method, electronic device and computer readable storage medium

Abstract

The present application discloses a slow disk scheduling method, an electronic device, and a computer-readable storage medium, wherein the slow disk scheduling method comprises: obtaining a hard disk cluster, determining the slow disks in the hard disk cluster and the number of slow disks corresponding to the slow disks; wherein the slow disks correspond to data reading and writing information during the data reading and writing process; obtaining the business type corresponding to the current business in the hard disk cluster, and determining the use status of the slow disk based on the business type, the number of slow disks, and the data reading and writing information; wherein the use status of the slow disk includes one of a rejection state and a release state. The above scheme can improve the rationality of slow disk scheduling.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a slow disk scheduling method, an electronic device, and a computer-readable storage medium. Background Art

[0002] Hard disk clusters often have slow disks due to long-term reading and writing. Slow disks have characteristics such as slow disk reading and writing and slow network data transmission. Once a slow disk appears, it will seriously slow down the overall operation efficiency of the node, making the node a slow node in the cluster, and finally affecting the throughput of the entire cluster. The conventional slow disk scheduling strategy adopts a one-size-fits-all approach, which affects the availability of the cluster. Therefore, how to improve the rationality of slow disk scheduling has become an urgent problem to be solved. Summary of the invention

[0003] The main technical problem solved by the present application is to provide a slow disk scheduling method, an electronic device and a computer-readable storage medium, which can improve the rationality of slow disk scheduling.

[0004] To solve the above technical problems, the first aspect of the present application provides a slow disk scheduling method, including: obtaining a hard disk cluster, determining the slow disks in the hard disk cluster and the number of slow disks corresponding to the slow disks; wherein the slow disks have corresponding data reading and writing information during the data reading and writing process; obtaining the business type corresponding to the current business in the hard disk cluster, and determining the usage status of the slow disk based on the business type, the number of slow disks and the data reading and writing information; wherein the usage status of the slow disk includes one of a rejection status and a release status.

[0005] To solve the above technical problem, the second aspect of the present application provides an electronic device, comprising a memory and a processor coupled to each other, wherein the memory stores program instructions, and the processor is used to execute the program instructions to implement the method described in the first aspect.

[0006] In order to solve the above technical problem, the third aspect of the present application provides a computer-readable storage medium, which stores program instructions that can be executed by a processor, and the program instructions are used to implement the method described in the first aspect above.

[0007] The above scheme obtains a hard disk cluster, determines the slow disks in the hard disk cluster, counts the number of slow disks corresponding to the slow disks, and the slow disks have data reading and writing information corresponding to the data reading and writing process, obtains the business type corresponding to the current business in the hard disk cluster, and based on the business type corresponding to the current business, the number of slow disks corresponding to the slow disks and the data reading and writing information, confirms the usage status of the slow disks in the disk cluster, and sets the usage status for each slow disk, wherein the usage status of the slow disk is one of a rejection status and a release status. By changing the processing timing of the slow disk, the processing of the slow disk is postponed to the time when the business is actually requested, so as to dynamically adjust the scheduling strategy of the slow disk, and realize the real-time optimal solution of the slow disk scheduling under the established strategy, thereby improving the rationality of the slow disk scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0009] Figure 1 It is a flowchart of an implementation method of the slow disk scheduling method of the present application;

[0010] Figure 2 It is a structural schematic diagram of an implementation method of slow disk detection of the present application;

[0011] Figure 3 yes Figure 1 A flowchart of an implementation method corresponding to part of the content in step S102;

[0012] Figure 4 It is a structural schematic diagram of an embodiment of the electronic device of the present application;

[0013] Figure 5 It is a structural schematic diagram of an implementation method of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments, and different implementation methods can be adaptively combined. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] The slow disk scheduling method provided in the present application is used to schedule the slow disk in a disk cluster, wherein the disk cluster can be set in an HDFS (Hadoop Distributed File System) cluster or other processing system, and the present application does not make specific restrictions on this. The execution subject corresponding to the slow disk scheduling method provided in the present application is a processing unit that can allocate disks in the disk cluster.

[0016] See also Figure 1 , Figure 1 This is a flow chart of an implementation method of a slow disk scheduling method of the present application. The slow disk scheduling method includes:

[0017] S101: Obtain a hard disk cluster, determine a slow disk in the hard disk cluster and the number of slow disks corresponding to the slow disk; wherein the slow disk has data reading and writing information corresponding to the data reading and writing process.

[0018] Specifically, a hard disk cluster is obtained, wherein the hard disk cluster includes normal disks and slow disks, the slow disks in the hard disk cluster are determined, the number of slow disks corresponding to the slow disks is counted, and the slow disks have corresponding data reading and writing information during the data reading and writing process.

[0019] In one application, the slow disk in the hard disk cluster is determined by counting, that is, the number of hard disk abnormalities is detected according to the predicted detection interval within the preset detection time. If the number of abnormalities within the preset detection time exceeds the preset threshold, the hard disk is considered to be a slow disk.

[0020] In another application mode, data sampling is performed on hard disks in a hard disk cluster that are in the process of reading and writing, and data within a preset detection time is taken as detection data. When the detection data is abnormal, the hard disk is considered to be a slow disk.

[0021] In some application scenarios, a disk cluster is set up in an HDFS cluster. The HDFS cluster selects a suitable hard disk for reading and writing based on the hard disk selection strategy (such as the polling-based strategy and the available space-based strategy).

[0022] S102: Obtain the service type corresponding to the current service in the hard disk cluster, and determine the use status of the slow disk based on the service type, the number of slow disks, and data read and write information; wherein the use status of the slow disk includes one of a rejection status and a release status.

[0023] Specifically, the business type corresponding to the current business in the hard disk cluster is obtained, and based on the business type corresponding to the current business, the number of slow disks corresponding to the slow disks, and the data reading and writing information, the usage status of the slow disks in the disk cluster is confirmed, and the usage status is set for each slow disk, wherein the usage status of each slow disk is one of a rejection status and a release status.

[0024] In one application method, the business type corresponding to the current business in the hard disk cluster is obtained, and the elimination probability is calculated based on the business type corresponding to the current business, the number of slow disks corresponding to the slow disks, and data reading and writing information, and then a random number is obtained, wherein the random number is uniformly distributed between 0 and 1, that is, the probability of taking any value is equal, when the elimination probability is greater than the random number, the use status of the slow disk is determined to be the elimination status, and when the elimination probability is less than the random number, the use status of the slow disk is determined to be the release status.

[0025] In another application method, the business type corresponding to the current business in the hard disk cluster is obtained, and the elimination probability is calculated based on the business type corresponding to the current business, the number of slow disks corresponding to the slow disks, and the data reading and writing information. Then, the elimination probability is compared with a preset elimination probability threshold. When the elimination probability is greater than the elimination probability threshold, the use status of the slow disk is determined to be a elimination status. When the elimination probability is less than the elimination probability threshold, the use status of the slow disk is determined to be a release status.

[0026] In one application scenario, a program is set in the HDFS cluster processing system. The program is used to randomly generate a random number x between 0 and 1. When the calculated elimination probability is greater than the random number x, the use status of the slow disk is determined to be eliminated. When the calculated elimination probability is less than the random number x, the use status of the slow disk is determined to be released.

[0027] In another application scenario, a rejection probability threshold y is directly specified in the HDFS cluster processing system. When the calculated rejection probability is greater than the rejection probability threshold y, the use status of the slow disk is determined to be a rejection status. When the calculated rejection probability is less than the rejection probability threshold y, the use status of the slow disk is determined to be a release status.

[0028] The above scheme obtains a hard disk cluster, determines the slow disks in the hard disk cluster, counts the number of slow disks corresponding to the slow disks, and the slow disks have data reading and writing information corresponding to the data reading and writing process, obtains the business type corresponding to the current business in the hard disk cluster, and based on the business type corresponding to the current business, the number of slow disks corresponding to the slow disks and the data reading and writing information, confirms the usage status of the slow disks in the disk cluster, and sets the usage status for each slow disk, wherein the usage status of the slow disk is one of a rejection status and a release status. By changing the processing timing of the slow disk, the processing of the slow disk is postponed to the time when the business is actually requested, so as to dynamically adjust the scheduling strategy of the slow disk, and realize the real-time optimal solution of the slow disk scheduling under the established strategy, thereby improving the rationality of the slow disk scheduling.

[0029] In one implementation scenario, obtaining a hard disk cluster in step S101 and determining the slow disks in the hard disk cluster and the number of slow disks corresponding to the slow disks specifically include: obtaining a hard disk cluster, detecting the hard disks in the hard disk cluster at a preset detection interval within a preset detection time, and obtaining the number of abnormalities of the hard disks; in response to the number of abnormalities exceeding a preset threshold, determining that the hard disk is a slow disk and the number of slow disks corresponding to the slow disk.

[0030] Specifically, a hard disk cluster is obtained, and the hard disks in the hard disk cluster are detected within a preset detection time and at a preset detection interval to obtain the number of abnormalities corresponding to the hard disks. When the number of abnormalities exceeds a preset threshold, the hard disk is determined to be a slow disk and the corresponding number of slow disks is determined. By counting, it is determined whether the hard disk is a slow disk, which can reduce the randomness caused by a single detection, thereby improving the accuracy of slow disk detection.

[0031] In a specific implementation scenario, please refer to Figure 2 , Figure 2 It is a structural schematic diagram of an implementation method of slow disk detection in the present application. Figure 2 Two hard disks are shown, namely hard disk 1 and hard disk 2. The detection time is 1 hour and the detection interval is 10 minutes. Hard disk 1 and hard disk 2 are detected respectively. The abnormal number of hard disk 1 is 5 times and the abnormal number of hard disk 2 is 2 times. If the hard disk has more than 3 abnormalities within the detection time, it is considered that the hard disk is a slow disk. At this time, hard disk 1 is a slow disk and hard disk 2 is a normal disk. In other implementation scenarios, the detection interval and threshold number can be specified separately through configuration items, and set according to actual conditions. This application does not make specific restrictions here.

[0032] Optionally, before detecting the hard disks in the hard disk cluster at a preset detection interval within a preset detection time and obtaining the number of hard disk abnormalities, the method includes: sampling the hard disks in the data reading and writing process to obtain data to be detected.

[0033] Specifically, when the slow disk detection interval arrives, the HDFS cluster will sample data from the hard disk during the data reading and writing process to obtain the corresponding data to be detected.

[0034] Furthermore, the step of detecting the hard disks in the hard disk cluster at a preset detection interval within a preset detection time to obtain the number of abnormalities of the hard disks specifically includes: detecting the data to be detected at a preset detection interval within the preset detection time to determine the number of abnormalities.

[0035] Specifically, the data to be tested is sampled within a preset test time and at a preset test interval to determine the number of hard disk anomalies. Through sampling, representative samples can be extracted from a large data set, thereby significantly reducing the amount of computational effort in data processing and analysis and improving data analysis efficiency.

[0036] In one embodiment, see Figure 3 , Figure 3 yes Figure 1 A flowchart of an implementation method corresponding to part of the content in step S102, wherein determining the use status of the slow disk based on the service type, the number of slow disks, and the data read and write information in step S102 specifically includes:

[0037] S301: Obtaining a quantity threshold condition that the number of slow disks matches.

[0038] Specifically, when determining the usage status of the slow disk, the current resource situation, that is, the number of slow disks in the hard disk cluster, must be considered first. The quantity threshold condition can be pre-set according to the actual situation of the HDFS cluster.

[0039] S302: In response to the number of slow disks satisfying a quantity threshold condition, based on the service type and data read and write information, determining a usage status of the slow disk.

[0040] Specifically, when the number of slow disks meets the number threshold condition, based on the business type and data read and write information of the current business, the use state of the slow disk is determined to be one of a rejection state and a release state.

[0041] In one application mode, a weight strategy corresponds to a business type, and step S302 specifically includes: in response to the number of slow disks satisfying a quantity threshold condition, determining the use status of the slow disk based on the weight strategy of the business type and data read and write information.

[0042] Specifically, when the number of slow disks meets the quantity threshold condition, the use status of the slow disk is determined to be one of a rejection status and a release status based on the weight strategy corresponding to the business type and the data read and write information.

[0043] Optionally, the quantity threshold condition may be preset according to the actual situation of the HDFS cluster, and is used to reflect whether the number of normal disks in the disk cluster is within a normal value range.

[0044] In one application scenario, data read and write information includes data transmission time and data type. Based on the weight strategy of the business type and the data read and write information, the steps for determining the usage status of the slow disk specifically include: obtaining the probability of eliminating the slow disk based on the weight strategy of the business type, data transmission time and data type; and determining the usage status of the slow disk based on the elimination probability.

[0045] Specifically, based on the weight strategy of the business type, data transmission time and data type, the probability of slow disk being eliminated is calculated, and based on the elimination probability, the use status of the slow disk is determined to be one of the elimination status and the release status.

[0046] It is understandable that there are different weight strategies for matching different business types. When the business type is write, the write weight will be higher than the read weight, and when the business type is read, the read weight will be higher than the write weight.

[0047] Furthermore, the weight strategy includes a weight corresponding to the data type, and a coefficient corresponding to the data transmission time. Based on the weight strategy of the business type, the data transmission time and the data type, the step of obtaining the probability of removing the slow disk specifically includes: obtaining the probability of removing the slow disk based on the weight corresponding to the data type and the coefficient corresponding to the data transmission time.

[0048] Specifically, based on the weight corresponding to the data type and the coefficient corresponding to the data transmission time, the probability of slow disk removal is dynamically calculated. By combining the business type of each business, the resource situation of the HDFS cluster at the current moment, the data reading and writing information of the current slow disk, and the specific slowness of the current slow disk, the probability of slow disk removal is comprehensively determined. A real-time optimal solution can be obtained, thereby improving the rationality of slow disk scheduling.

[0049] In a specific application scenario, the calculation process of the elimination probability is demonstrated by taking the current business type of writing as an example. Please refer to Table 1 and Table 2. Table 1 records the data read and write information of the slow disk, and Table 2 records the weight corresponding to the data type and the coefficient corresponding to the data transmission time. The data type includes writing, reading and metadata. It should be noted that the probability table corresponding to writing will reduce the reading weight, increase the writing weight, and distinguish the degree of slow writing from the writing, and give different coefficients. In this writing, the elimination probability of the slow disk Disk1 is: , the probability of removing the slow disk Disk2 is: , then obtain a randomly generated random number x with uniform distribution between 0 and 1, and compare x with the rejection probability of slow disk Disk1 and slow disk Disk2 respectively, so as to determine whether the use status of slow disks Disk1 and Disk2 is the rejection status or the release status. For example, when the random number x is 0.7, the use status of slow disks Disk1 and Disk2 are both the release status. When the random number x is 0.6, the use status of slow disk Disk1 is the rejection status, and the use status of slow disk Disk2 is the release status. When the random number x is 0.5, the use status of slow disks Disk1 and Disk2 are both the rejection status.

[0050] Table 1

[0051]

[0052] Table 2

[0053]

[0054] It is understandable that when the business type is reading, the probability table corresponding to reading will reduce the write weight, increase the read weight, and give different coefficients to the degree of slow reading.

[0055] S303: In response to the number of slow disks not satisfying the number threshold condition, determining the usage status of the slow disks based on the number of slow disks.

[0056] Specifically, when the number of slow disks does not meet the quantity threshold condition, the use status of the slow disks is determined to be one of a rejection status and a release status based on the number of slow disks.

[0057] In one application scenario, step S303 specifically includes: in response to the number of slow disks not meeting the quantity threshold condition, obtaining a pre-set release quantity threshold and a rejection quantity threshold; in response to the number of slow disks being less than the release quantity threshold, determining that the use status of the slow disk is a rejection status; in response to the number of slow disks being greater than the rejection quantity threshold, determining that the use status of the slow disk is a release status.

[0058] Specifically, when the number of slow disks does not meet the quantity threshold condition, the preset release quantity threshold T is obtained. max and the rejection threshold T min , when the number of slow disks is less than the release quantity threshold T max When the number of normal disks in the disk cluster is sufficient, the slow disks will be 100% removed. Because there are many normal disks available in this case, the HDFS cluster needs to take into account the throughput and latency of the business. When the number of slow disks is greater than the removal threshold T min When the HDFS cluster is running low, it means that there are almost no normal disks in the disk cluster. In this case, 100% of the slow disks will be released because the HDFS cluster must prioritize data storage rather than latency.

[0059] In a specific application scenario, the disk cluster is set in the HDFS cluster. The HDFS cluster includes a management node, a data node, and a slow disk scheduling optimization module. The slow disk scheduling optimization module mainly includes two submodules, one is a slow disk identification optimization module, and the other is a slow disk processing optimization module. The management node is responsible for managing the file system namespace, maintaining the file system tree, and the metadata of the entire HDFS cluster. The client first interacts with the management node, and the management node selects the data node for reading and writing. The data node further selects the hard disk for reading and writing. When the slow disk detection interval arrives, the data node will also sample the hard disk during the data reading and writing process to obtain the corresponding data to be detected. The slow disk identification optimization module detects the data to be detected, thereby determining the slow disk in the disk cluster. Then, the full disk processing optimization module determines the resource situation of the current cluster and the business type of the current business, and calculates the corresponding elimination probability, so as to determine whether to use the slow disk according to the calculated elimination probability.

[0060] It can be understood that based on the various implementation scenarios given in this application, whether the slow disk is eliminated is not determined according to some existing rules after the slow disk is detected, but the probability of elimination of the slow disk is comprehensively determined based on the business type of each time, the resources of the cluster at the current moment, the specific indicators of the current slow disk, the specific degree of slowness of the current slow disk, etc., to obtain a real-time optimal solution, which improves the rationality of slow disk scheduling and takes into account the throughput and latency of the business.

[0061] See also Figure 4 , Figure 4 4 is a schematic diagram of the structure of an embodiment of an electronic device of the present application, wherein the electronic device 40 comprises a memory 400 and a processor 402 coupled to each other, wherein the memory 400 stores program data (not shown), and the processor 402 calls the program data to implement the method in any of the above embodiments, and the description of the relevant content can be found in the detailed description of the above method embodiment, which will not be repeated here. Specifically, the electronic device 40 includes but is not limited to: a desktop computer, a laptop computer, a tablet computer, a server, etc., which are not limited here. In addition, the processor 402 can also be referred to as a CPU (Center Processing Unit). The processor 402 may be an integrated circuit chip with signal processing capabilities. The processor 402 may also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. In addition, the processor 402 may be implemented by an integrated circuit chip.

[0062] See also Figure 5 , Figure 5 It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. The computer-readable storage medium 50 stores program data 500. When the program data 500 is executed by a processor, the method in any of the above embodiments is implemented. For descriptions of related contents, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0063] It should be noted that the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present implementation scheme.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0066] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A slow disk scheduling method, characterized in that: include: Obtain a hard disk cluster, determine a slow disk in the hard disk cluster and the number of slow disks corresponding to the slow disk; wherein the slow disk has data reading and writing information corresponding to the data reading and writing process; Obtaining a service type corresponding to a current service in the hard disk cluster, and determining a usage status of the slow disk based on the service type, the number of the slow disks, and the data read and write information; wherein the usage status of the slow disk includes one of a discarded status and a released status; Wherein, determining the usage status of the slow disk based on the business type, the number of the slow disks and the data read and write information includes: obtaining a quantity threshold condition matched by the number of slow disks; in response to the number of slow disks satisfying the quantity threshold condition, determining the usage status of the slow disk based on the business type and the data read and write information; in response to the number of slow disks not satisfying the quantity threshold condition, determining the usage status of the slow disk based on the number of slow disks; Wherein, the business type corresponds to a weight strategy; in response to the number of slow disks satisfying the quantity threshold condition, the usage status of the slow disk is determined based on the business type and the data read and write information, including: in response to the number of slow disks satisfying the quantity threshold condition, the usage status of the slow disk is determined based on the weight strategy of the business type and the data read and write information; wherein different business types are matched with different weight strategies, when the business type is writing, the write weight is higher than the read weight, and when the business type is reading, the read weight is higher than the write weight, and the data read and write information includes data transmission time and data type.

2. The method according to claim 1, characterized in that The obtaining of the hard disk cluster and determining the slow disk in the hard disk cluster and the number of slow disks corresponding to the slow disk include: Acquire the hard disk cluster, detect the hard disks in the hard disk cluster at a preset detection interval within a preset detection time, and obtain the number of abnormalities of the hard disks; In response to the number of abnormalities exceeding a preset threshold, it is determined that the hard disk is a slow disk and the number of slow disks corresponding to the slow disk is determined.

3. The method according to claim 2, characterized in that Before the hard disks in the hard disk cluster are detected at preset detection intervals within a preset detection time and the number of abnormalities of the hard disks is obtained, the method includes: Sampling the hard disk during the data reading and writing process to obtain data to be detected; The detecting the hard disks in the hard disk cluster at a preset detection interval within a preset detection time to obtain the number of abnormalities of the hard disks includes: The data to be detected is detected at a preset detection interval within a preset detection time to determine the number of abnormalities.

4. The method according to claim 1, characterized in that The determining the usage status of the slow disk based on the weight strategy of the service type and the data read / write information includes: Based on the weight strategy of the service type, the data transmission time and the data type, obtaining the probability of removing the slow disk; Based on the elimination probability, the usage status of the slow disk is determined.

5. The method according to claim 4, characterized in that The weight strategy includes a weight corresponding to the data type and a coefficient corresponding to the data transmission time; The weight strategy based on the service type, the data transmission time and the data type, obtaining the probability of removing the slow disk includes: Based on the weight corresponding to the data type and the coefficient corresponding to the data transmission time, the probability of eliminating the slow disk is obtained.

6. The method according to claim 1, characterized in that In response to the number of the slow disks not satisfying the number threshold condition, determining the state of the slow disk based on the number of the slow disks includes: In response to the number of slow disks not meeting the number threshold condition, obtaining a preset release number threshold and rejection number threshold; In response to the number of slow disks being less than the release number threshold, determining that the use state of the slow disk is the rejection state; In response to the number of slow disks being greater than the rejection number threshold, determining that the use status of the slow disk is the release status.

7. An electronic device, characterized in that: The method comprises a memory and a processor coupled to each other, wherein the memory stores program instructions, and the processor is used to execute the program instructions to implement the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: Program instructions that can be executed by a processor are stored, and the program instructions are used to implement the method described in any one of claims 1 to 6.

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