A hardware management method, device and equipment of a distributed storage cluster and a medium

Abstract

The application discloses a hardware management method and device of a distributed storage cluster, equipment and a medium, and relates to the technical field of information. The method comprises the following steps: obtaining target hardware data information corresponding to a target hardware in a distributed storage cluster; determining a parameter affecting the service life of the target hardware in the target hardware data information to obtain a service life reference value parameter of the target hardware; comparing the service life reference value parameter with a service life standard value parameter of the target hardware to obtain a comparison result, and determining a service life analysis frequency of the target hardware according to the comparison result; and performing service life prediction of the target hardware based on the service life analysis frequency, the target hardware data information and historical hardware data information of a historical hardware corresponding to the target hardware. Through the above scheme, the service life analysis frequency can be flexibly adjusted, the hardware management in the distributed storage cluster can be effectively performed, and the fault prediction of the hardware in use can be performed.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a hardware management method, device, equipment and medium for a distributed storage cluster. Background Art

[0002] Distributed storage, as a data storage technology, utilizes the disk space on every machine in an enterprise over a network. These distributed storage resources form virtual storage devices, allowing data to be distributed and stored throughout the enterprise. Currently, distributed storage clusters are constantly expanding, and the hardware configuration associated with storage devices is relatively complex. Large-scale clusters contain a large amount of hardware information. When collecting data from individual nodes, hardware mismatches can occur, hardware information collection and reporting are delayed, and the system load is high. Once hardware information is collected, the sheer volume of information can make it difficult for operations and maintenance personnel to analyze and process it. Consequently, effective hardware management in distributed storage clusters and fault prediction for in-use hardware remain key challenges. Summary of the Invention

[0003] In view of this, the present invention aims to provide a distributed storage cluster hardware management method, apparatus, device, and medium that can effectively manage the hardware in the distributed storage cluster and predict hardware failures in use. The specific solution is as follows:

[0004] In a first aspect, the present application discloses a hardware management method for a distributed storage cluster, comprising:

[0005] Obtain target hardware data information corresponding to the target hardware in the distributed storage cluster;

[0006] Determining parameters that affect the lifespan of the target hardware according to the target hardware data information to obtain a lifespan reference value parameter of the target hardware;

[0007] Comparing the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determining a lifetime analysis frequency for the target hardware according to the comparison result;

[0008] Based on the life analysis frequency, life prediction for the target hardware is performed according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware.

[0009] Optionally, before obtaining the target hardware data information corresponding to the target hardware in the distributed storage cluster, the method further includes:

[0010] The target hardware basic information of the target hardware is obtained through a hardware basic information obtaining command, and it is determined whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information.

[0011] Optionally, acquiring target hardware basic information of the target hardware by using a hardware basic information acquisition command, and determining whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information, includes:

[0012] Obtaining target hardware basic information of the target hardware through a hardware basic information acquisition command, and determining whether the target hardware has a fault according to the target hardware basic information;

[0013] If the target hardware does not have a fault, the target hardware basic information is processed into target format basic information, and the target format basic information is compared with data in a preset hardware compatibility database to determine whether the target hardware is compatible with other hardware in the distributed storage cluster.

[0014] Optionally, after acquiring target hardware basic information of the target hardware through the hardware basic information acquisition command and determining whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information, the method further includes:

[0015] The hardware adaptation interface view corresponding to the distributed storage cluster is adjusted to ensure that hardware data information corresponding to various types of hardware in the distributed storage cluster is normally obtained.

[0016] Optionally, obtaining target hardware data information corresponding to target hardware in the distributed storage cluster includes:

[0017] Collect the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data collection period, and save the target hardware data information into a preset data information database;

[0018] Accordingly, performing life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware includes:

[0019] The target hardware data information and historical hardware data information of the historical hardware corresponding to the target hardware are read from the preset data information database, and life prediction for the target hardware is performed based on the target hardware data information and the historical hardware data information based on the life analysis frequency.

[0020] Optionally, also include:

[0021] Adjusting the preset data collection period according to the change frequency of the target hardware data information in the preset data information database;

[0022] If the change frequency of the target hardware data information is lower than a preset change threshold, the preset data collection period is correspondingly extended according to the change frequency;

[0023] If the change frequency of the target hardware data information is higher than a preset change threshold, the preset data collection period is shortened accordingly according to the change frequency.

[0024] Optionally, performing life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware includes:

[0025] If the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, the life span of the target hardware is predicted based on the target hardware data information and the environment information of the target hardware.

[0026] In a second aspect, the present application discloses a hardware management device for a distributed storage cluster, comprising:

[0027] The hardware information acquisition module is used to obtain target hardware data information corresponding to the target hardware in the distributed storage cluster;

[0028] A life reference value determination module, configured to determine parameters affecting the life of the target hardware based on the target hardware data information, so as to obtain a life reference value parameter of the target hardware;

[0029] a frequency determination module, configured to compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine a lifetime analysis frequency for the target hardware according to the comparison result;

[0030] The life prediction module is configured to perform life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware.

[0031] In a third aspect, the present application discloses an electronic device, comprising:

[0032] Memory, used to store computer programs;

[0033] The processor is configured to execute the computer program to implement the steps of the hardware management method of the distributed storage cluster disclosed above.

[0034] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the hardware management method of the distributed storage cluster disclosed above are implemented.

[0035] In the present application, when performing hardware management of a distributed storage cluster, the target hardware data information corresponding to the target hardware in the distributed storage cluster is first obtained, and the parameters affecting the life of the target hardware are determined from the target hardware data information to obtain the life reference value parameters of the target hardware, and then the life reference value parameters are compared with the life standard value parameters of the target hardware to obtain a comparison result, and the life analysis frequency for the target hardware is determined based on the comparison result, and finally, based on the life analysis frequency, the life prediction for the target hardware is performed based on the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware. It can be seen that when performing hardware management of a distributed storage cluster, the target hardware data information corresponding to the target hardware in the distributed cluster is first obtained, and the parameters related to the life of the target hardware are determined from the target hardware data information as the life reference value parameters of the target hardware, and then the life standard value parameters of the target hardware are compared with the life reference value parameters of the target hardware, and the life analysis frequency for the target hardware is determined based on the comparison result, and finally, based on the life analysis frequency, the life prediction for the target hardware is performed based on the target hardware data information and the historical hardware data information of the historical hardware. Therefore, when the present application performs hardware management of a distributed storage cluster, by obtaining the target hardware data information of the target hardware, and determining the life reference value parameters from the target hardware data information, and determining the life analysis frequency based on the comparison results of the life reference value parameters of the target hardware and the life standard value parameters, the life analysis frequency for the target hardware can be flexibly adjusted during hardware management, thus avoiding the consumption of a large amount of system resources caused by frequent life analysis, so that the analysis for the target hardware adopts the corresponding appropriate frequency and timely determines the failure probability and expected failure time of the target hardware, so that the user can judge the life of the target hardware and prepare for replacement in advance under the premise of fully using the hardware. In summary, the present application can effectively manage the hardware in a distributed storage cluster and predict failures of the hardware in use. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0037] Figure 1 A hardware management method flow chart of a distributed storage cluster is provided for the present application;

[0038] Figure 2 A specific hardware management method flow chart of a distributed storage cluster is provided for the present application;

[0039] Figure 3 A specific hardware management method flow chart of a distributed storage cluster is provided for the present application;

[0040] Figure 4 A hardware management device structure schematic diagram of a distributed storage cluster is provided for the present application;

[0041] Figure 5 An electronic equipment structure diagram is provided for the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] As a data storage technology, distributed storage uses the disk space on each machine in an enterprise through a network, and these scattered storage resources constitute a virtual storage device, and data is distributed and stored in every corner of the enterprise. At present, the scale of distributed storage clusters is increasing, and the hardware configuration related to storage devices is relatively complex. In a large-scale cluster, the amount of various hardware information data is large, and when each node collects data, hardware adaptation abnormalities, hardware information collection reporting not in time, and large system load may occur. After collecting hardware information, a large amount of information makes it difficult for operation and maintenance personnel to analyze and process. Therefore, the hardware management method of the distributed storage cluster provided by the present application can effectively manage the hardware in the distributed storage cluster and further solve the problem of fault prediction of the hardware in use.

[0044] The embodiment of the present application discloses a hardware management method of a distributed storage cluster, referring to Figure 1 As shown in the figure, the method comprises the following steps:

[0045] Step S11: obtaining target hardware data information corresponding to target hardware in a distributed storage cluster.

[0046] In this embodiment, target hardware data information of target hardware in a distributed storage cluster is obtained, wherein the target hardware is hardware in the distributed storage cluster. The target hardware data information of the target hardware is collected by a preset data collection module to obtain the target hardware data information of the target hardware in the distributed storage cluster. The target hardware data information is obtained through the above technical solution, so that parameters affecting the lifespan of the target hardware can be subsequently determined based on the target hardware data information to obtain a reference value parameter for the lifespan of the target hardware.

[0047] Step S12: Determine parameters that affect the life of the target hardware according to the target hardware data information to obtain life reference value parameters of the target hardware.

[0048] In this embodiment, data related to the lifespan of the target hardware is determined from the collected target hardware data information to obtain a lifespan reference value parameter for the target hardware. The lifespan reference value parameter is data corresponding to the target hardware. By using the above technical solution, a lifespan reference value for the target hardware is obtained, which facilitates subsequent comparison of the lifespan reference value parameter with the target hardware's lifespan standard value parameter to obtain a comparison result, and based on the comparison result, determines a lifespan analysis frequency for the target hardware.

[0049] Step S13: Compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine the lifetime analysis frequency for the target hardware according to the comparison result.

[0050] In this embodiment, the target hardware's lifespan standard value is an industry-recognized lifespan standard value parameter for the target hardware's corresponding parameter. Specifically, the lifespan reference value parameter is compared with the target hardware's lifespan standard value parameter to obtain a comparison result, i.e., to determine whether the target hardware is approaching its lifespan limit. If the target hardware's lifespan reference value parameter exceeds the lifespan standard value parameter, it indicates that the target hardware is approaching its lifespan limit, and a higher lifespan analysis frequency is used for the target hardware; if the target hardware's lifespan reference value parameter does not exceed the lifespan standard value parameter, it indicates that the target hardware is not approaching its lifespan limit, and a lower lifespan analysis frequency is used for the target hardware. It is understood that both the higher and lower lifespan analysis frequencies can be set based on actual usage. Through the above technical solution, the lifespan reference value parameter is compared with the target hardware's lifespan standard value parameter to obtain a comparison result, and the lifespan analysis frequency for the target hardware is determined based on the comparison result, thereby performing dynamic frequency analysis on the target hardware. This allows for flexible adjustment of the lifespan analysis frequency for the target hardware during hardware management, avoiding the consumption of large system resources caused by frequent lifespan analysis.

[0051] Step S14: performing life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware.

[0052] In this embodiment, the lifespan of the target hardware data information and the historical hardware data information corresponding to the target hardware are predicted based on the lifespan analysis frequency. The historical hardware is the historical hardware that was replaced before the target hardware was used, that is, the data curves of the current target hardware and the replaced historical hardware in the distributed storage cluster are compared and analyzed to predict the lifespan of the target hardware. Through the above technical solution, since there are many hardware information parameters and historical data, for example, the disk smart information may have multiple parameters, making it difficult for users to distinguish between valid parameters, by analyzing the historical hardware data information corresponding to the target hardware, the analysis of the target hardware adopts the corresponding appropriate frequency to timely determine the failure probability and expected failure time of the target hardware, so that users can judge the lifespan of the target hardware and prepare for replacement in advance on the premise of fully using the hardware.

[0053] It can be seen that in this embodiment, when performing hardware management of a distributed storage cluster, the target hardware data information of the target hardware in the distributed cluster is first obtained, and parameters related to the life of the target hardware are determined from the target hardware data information as the life reference value parameters of the target hardware. The life standard value parameters of the target hardware are then compared with the life reference value parameters of the target hardware. The life analysis frequency for the target hardware is determined based on the comparison results. Finally, based on the life analysis frequency, the life of the target hardware is predicted according to the target hardware data information and the historical hardware data information of the historical hardware. Therefore, when the present application performs hardware management of a distributed storage cluster, by obtaining the target hardware data information of the target hardware, and determining the life reference value parameters from the target hardware data information, and determining the life analysis frequency based on the comparison results of the life reference value parameters of the target hardware and the life standard value parameters, the life analysis frequency for the target hardware can be flexibly adjusted during hardware management, thus avoiding the consumption of a large amount of system resources caused by frequent life analysis, so that the analysis for the target hardware adopts the corresponding appropriate frequency and timely determines the failure probability and expected failure time of the target hardware, so that the user can judge the life of the target hardware and prepare for replacement in advance under the premise of fully using the hardware. In summary, the present application can effectively manage the hardware in a distributed storage cluster and predict failures of the hardware in use.

[0054] See also Figure 2 As shown, the embodiment of the present invention discloses a specific hardware management method for a distributed storage cluster. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0055] Step S21: acquiring target hardware basic information of the target hardware through a hardware basic information acquisition command, and determining whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information.

[0056] In this embodiment, the target hardware basic information of the target hardware is obtained through the hardware basic information acquisition command, and whether the target hardware is compatible with other hardware in the distributed storage cluster is determined based on the target hardware basic information, including: obtaining the target hardware basic information of the target hardware through the hardware basic information acquisition command, and determining whether the target hardware has a fault based on the target hardware basic information; if the target hardware does not have a fault, processing the target hardware basic information into target format basic information, and comparing the target format basic information with the data in a preset hardware compatibility database to determine whether the target hardware is compatible with other hardware in the distributed storage cluster.

[0057] It is understandable that before collecting data on the hardware in the distributed storage cluster, the hardware needs to be adapted first. In a distributed storage system, the hardware situation is relatively complicated, and there may be problems such as different manufacturers, different supported firmware versions, the maximum number of devices supported by the server, and different SAS cards and RAID cards, so it is necessary to adapt the hardware in the distributed storage cluster. Specifically, first check whether the target hardware is available, and if there is a fault, issue an alarm prompt for the target hardware fault. Then obtain the basic information of the target hardware, check whether the target hardware is available, obtain the target hardware basic information of the target hardware through the hardware basic information acquisition command, and determine whether the target hardware has a fault based on the target hardware basic information. If the target hardware does not have a fault, the hardware basic information of different formats from different manufacturers is processed into target format basic information, and the target format basic information is compared with the data in the preset hardware compatibility database to determine whether the target hardware is compatible with other hardware in the distributed storage cluster. Among them, the preset hardware compatibility database is a database in which the compatibility relationship between various types of hardware is written in advance.

[0058] In this embodiment, after determining whether the target hardware is compatible with other hardware in the distributed storage cluster, the method further includes adjusting the hardware adaptation interface view corresponding to the distributed storage cluster to ensure that hardware data information corresponding to various types of hardware in the distributed storage cluster is normally obtained. Through the above technical solution, determining whether the target hardware is compatible before obtaining the target hardware information can effectively avoid system anomalies caused by insufficient hardware adaptation checks and ensure hardware adaptation in advance.

[0059] Step S22: Acquire target hardware data information corresponding to the target hardware in the distributed storage cluster.

[0060] Step S23: Determine the parameters that affect the life of the target hardware according to the target hardware data information to obtain a life reference value parameter of the target hardware.

[0061] Step S24: Compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine the lifetime analysis frequency for the target hardware according to the comparison result.

[0062] Step S25: performing a life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware.

[0063] It can be seen that, in the embodiment, by judging whether the target hardware is suitable before obtaining the data information of the target hardware, system abnormity caused by insufficient hardware adaptation check can be effectively avoided, and hardware adaptation is prepared in advance.

[0064] Referring to Figure 3 As shown in the figure, the embodiment of the application discloses a specific hardware management method of a distributed storage cluster, and the technical solution is further described and optimized compared with the previous embodiment.

[0065] Step S31: collecting target hardware data information corresponding to target hardware in the distributed storage cluster according to a preset data collection period, and saving the target hardware data information into a preset data information database.

[0066] In this embodiment, after collecting the target hardware data information corresponding to the target hardware in the distributed storage cluster according to the preset data collection cycle and saving the target hardware data information to the preset data information database, it also includes: adjusting the preset data collection cycle according to the change frequency of the target hardware data information in the preset data information database; if the change frequency of the target hardware data information is lower than the preset change threshold, then the preset data collection cycle is correspondingly extended according to the change frequency; if the change frequency of the target hardware data information is higher than the preset change threshold, then the preset data collection cycle is correspondingly shortened according to the change frequency. It can be understood that the resource consumption caused by frequent queries can be reduced by collecting and caching, but a collection cycle that is too long will lead to untimely information updates, and a collection cycle that is too short will still result in unnecessary consumption. Specifically, the preset data collection cycle is analyzed. If the target hardware data information of the target hardware has a low change frequency, the preset data collection cycle is extended; if the target hardware data information of the target hardware has a high change frequency, the preset data collection cycle is shortened. Furthermore, the target hardware data information collected will be stored in the preset data information database of the node where the target hardware of the distributed storage cluster is located. When the stored data exceeds a certain time, the representative data in the historical data in the preset data information database and the data that can reflect the changes in the target hardware situation will be retained, and the other data will be cleaned. Through the above technical solution, the target hardware data information corresponding to the target hardware in the distributed storage cluster is collected according to the preset data collection cycle, and the target hardware data information is saved in the preset data information database, so that the preset data collection cycle is dynamically adjusted according to the feedback of the target hardware data information, avoiding unnecessary resource consumption in the data collection process; at the same time, data cleaning is performed according to the type of historical data to avoid the problem of excessive storage space occupied by historical data, resulting in waste of resources.

[0067] Step S32: Determine the parameters that affect the life of the target hardware according to the target hardware data information to obtain a life reference value parameter of the target hardware.

[0068] Step S33: Compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine the lifetime analysis frequency for the target hardware according to the comparison result.

[0069] Step S34: reading the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware from the preset data information database, and performing life prediction for the target hardware according to the target hardware data information and the historical hardware data information based on the life analysis frequency.

[0070] In this embodiment, if the historical hardware data information corresponding to the target hardware cannot be found in the preset data information database, a lifespan prediction for the target hardware is performed based on the target hardware data information and the environment information of the target hardware. The environment information of the target hardware includes, but is not limited to, temperature information, so as to facilitate a comprehensive analysis and prediction of the target hardware lifespan based on the target hardware data information and information such as the temperature of the target hardware.

[0071] It can be seen that in this embodiment, by collecting the target hardware data information corresponding to the target hardware in the distributed storage cluster according to the preset data collection cycle, and saving the target hardware data information to the preset data information database, the preset data collection cycle is dynamically adjusted according to the feedback of the target hardware data information to avoid unnecessary resource consumption in the data collection process; at the same time, data cleaning is performed according to the type of historical data to avoid the problem of excessive storage space occupied by historical data, resulting in waste of resources.

[0072] See also Figure 4 The embodiment of the present application discloses a hardware management device for a distributed storage cluster, including:

[0073] The hardware information acquisition module 11 is used to obtain target hardware data information corresponding to the target hardware in the distributed storage cluster;

[0074] A life reference value determination module 12 is configured to determine parameters that affect the life of the target hardware based on the target hardware data information to obtain a life reference value parameter of the target hardware;

[0075] a frequency determination module 13, configured to compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine a lifetime analysis frequency for the target hardware according to the comparison result;

[0076] The life prediction module 14 is configured to perform life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware.

[0077] It can be seen that in this embodiment, when performing hardware management of a distributed storage cluster, the target hardware data information of the target hardware in the distributed cluster is first obtained, and parameters related to the life of the target hardware are determined from the target hardware data information as the life reference value parameters of the target hardware. The life standard value parameters of the target hardware are then compared with the life reference value parameters of the target hardware. The life analysis frequency for the target hardware is determined based on the comparison results. Finally, based on the life analysis frequency, the life of the target hardware is predicted according to the target hardware data information and the historical hardware data information of the historical hardware. Therefore, when the present application performs hardware management of a distributed storage cluster, by obtaining the target hardware data information of the target hardware, and determining the life reference value parameters from the target hardware data information, and determining the life analysis frequency based on the comparison results of the life reference value parameters of the target hardware and the life standard value parameters, the life analysis frequency for the target hardware can be flexibly adjusted during hardware management, thus avoiding the consumption of a large amount of system resources caused by frequent life analysis, so that the analysis for the target hardware adopts the corresponding appropriate frequency and timely determines the failure probability and expected failure time of the target hardware, so that the user can judge the life of the target hardware and prepare for replacement in advance under the premise of fully using the hardware. In summary, the present application can effectively manage the hardware in a distributed storage cluster and predict failures of the hardware in use.

[0078] In some specific embodiments, the hardware management device of the distributed storage cluster further includes:

[0079] The hardware adaptation module is used to obtain target hardware basic information of the target hardware through a hardware basic information acquisition command, and determine whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information.

[0080] In some specific embodiments, the hardware adaptation module specifically includes:

[0081] a fault checking unit, configured to obtain target hardware basic information of the target hardware through a hardware basic information obtaining command, and determine whether the target hardware has a fault according to the target hardware basic information;

[0082] A compatibility judgment unit is used to process the basic information of the target hardware into basic information of a target format if there is no fault in the target hardware, and compare the basic information of the target format with the data in a preset hardware compatibility database to determine whether the target hardware is compatible with other hardware in the distributed storage cluster.

[0083] In some specific embodiments, the hardware management device of the distributed storage cluster further includes:

[0084] The view adjustment module is used to adjust the hardware adaptation interface view corresponding to the distributed storage cluster to ensure that hardware data information corresponding to various types of hardware in the distributed storage cluster is normally obtained.

[0085] In some specific embodiments, the hardware information acquisition module 11 is specifically configured to: collect the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data collection period, and save the target hardware data information into a preset data information database;

[0086] Correspondingly, the life prediction module 14 is specifically used to: read the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware from the preset data information database, and perform life prediction for the target hardware based on the target hardware data information and the historical hardware data information based on the life analysis frequency.

[0087] In some specific embodiments, the hardware management device of the distributed storage cluster further includes:

[0088] a period adjustment module, configured to adjust the preset data collection period according to the frequency of change of the target hardware data information in the preset data information database;

[0089] A cycle extension module, configured to extend the preset data collection cycle accordingly according to the change frequency if the change frequency of the target hardware data information is lower than a preset change threshold;

[0090] The cycle shortening module is configured to shorten the preset data collection cycle accordingly according to the change frequency if the change frequency of the target hardware data information is higher than a preset change threshold.

[0091] In some specific embodiments, the life prediction module 14 is specifically used to: if the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, then perform life prediction for the target hardware based on the target hardware data information and the environmental information of the target hardware.

[0092] Figure 5An electronic device 20 provided in an embodiment of the present application is shown. The electronic device 20 may further include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the hardware management method for a distributed storage cluster disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0093] In this embodiment, the power supply 23 is used to provide voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0094] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0095] The operating system 221 is used to manage and control the hardware devices on the electronic device 20, and the computer program 222 can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to implement the hardware management method of the distributed storage cluster executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to perform other specific tasks.

[0096] Furthermore, this application discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned hardware management method for a distributed storage cluster. The specific steps of this method can be found in the corresponding contents disclosed in the aforementioned embodiments and will not be further described here.

[0097] Finally, it needs to be pointed out that in this article, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0098] The above describes in detail the hardware management method, device, equipment and medium of the distributed storage cluster provided by the present application. The principles and implementation manners of the present application are described by applying specific examples in this article. The above example is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A hardware management method for a distributed storage cluster, characterized in that: include: Obtaining target hardware data information corresponding to target hardware in a distributed storage cluster, including: collecting the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data collection period, and saving the target hardware data information to a preset data information database; adjusting the preset data collection period according to the change frequency of the target hardware data information in the preset data information database; if the change frequency of the target hardware data information is lower than a preset change threshold, extending the preset data collection period accordingly according to the change frequency; if the change frequency of the target hardware data information is higher than the preset change threshold, shortening the preset data collection period accordingly according to the change frequency; Determining parameters that affect the lifespan of the target hardware according to the target hardware data information to obtain a lifespan reference value parameter of the target hardware; Comparing the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determining a lifetime analysis frequency for the target hardware according to the comparison result; Based on the life analysis frequency, a life prediction is performed for the target hardware according to the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware, including: when the length of time for storing data in the preset data information database reaches a preset length, the target hardware data information is cleaned according to the historical hardware data information; the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware are read from the preset data information database, and based on the life analysis frequency, a life prediction is performed for the target hardware according to the target hardware data information and the historical hardware data information.

2. The hardware management method of a distributed storage cluster according to claim 1, characterized in that: Before acquiring the target hardware data information corresponding to the target hardware in the distributed storage cluster, the method further includes: The target hardware basic information of the target hardware is obtained through a hardware basic information obtaining command, and it is determined whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information.

3. The hardware management method of a distributed storage cluster according to claim 2, characterized in that: The acquiring target hardware basic information of the target hardware through the hardware basic information acquisition command, and determining whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information, includes: Obtaining target hardware basic information of the target hardware through a hardware basic information acquisition command, and determining whether the target hardware has a fault according to the target hardware basic information; If the target hardware does not have a fault, the target hardware basic information is processed into target format basic information, and the target format basic information is compared with data in a preset hardware compatibility database to determine whether the target hardware is compatible with other hardware in the distributed storage cluster.

4. The hardware management method of a distributed storage cluster according to claim 2, characterized in that: After acquiring the target hardware basic information of the target hardware through the hardware basic information acquisition command and determining whether the target hardware is compatible with other hardware in the distributed storage cluster according to the target hardware basic information, the method further includes: The hardware adaptation interface view corresponding to the distributed storage cluster is adjusted to ensure that hardware data information corresponding to various types of hardware in the distributed storage cluster is normally obtained.

5. The hardware management method of a distributed storage cluster according to claim 1, characterized in that: The performing life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware includes: If the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, the life span of the target hardware is predicted based on the target hardware data information and the environment information of the target hardware.

6. A hardware management device for a distributed storage cluster, characterized in that: include: A hardware information acquisition module, configured to acquire target hardware data information corresponding to target hardware in a distributed storage cluster, comprising: collecting the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data acquisition period, and saving the target hardware data information into a preset data information database; adjusting the preset data acquisition period according to the change frequency of the target hardware data information in the preset data information database; if the change frequency of the target hardware data information is lower than a preset change threshold, extending the preset data acquisition period accordingly according to the change frequency; if the change frequency of the target hardware data information is higher than the preset change threshold, shortening the preset data acquisition period accordingly according to the change frequency; A life reference value determination module, configured to determine parameters affecting the life of the target hardware based on the target hardware data information, so as to obtain a life reference value parameter of the target hardware; a frequency determination module, configured to compare the lifetime reference value parameter with the lifetime standard value parameter of the target hardware to obtain a comparison result, and determine a lifetime analysis frequency for the target hardware according to the comparison result; A life prediction module is used to perform life prediction for the target hardware based on the life analysis frequency according to the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware, including: when the length of time for storing data in the preset data information database reaches a preset length, cleaning the target hardware data information according to the historical hardware data information; reading the target hardware data information and the historical hardware data information of the historical hardware corresponding to the target hardware from the preset data information database, and performing life prediction for the target hardware based on the target hardware data information and the historical hardware data information based on the life analysis frequency.

7. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the hardware management method for a distributed storage cluster according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the hardware management method of the distributed storage cluster according to any one of claims 1 to 5 are implemented.

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