Prefabricated data center system management method and system

Abstract

The embodiment of the application provides a prefabricated data center system management method and system, which compares the latest system running record image of the image node corresponding to the prefabricated data center system with the past specified system running record image of the prefabricated data center system, analyzes the different feature data in the latest system running record image based on the abnormal feature analysis result, and thus, the application only analyzes the different feature data in the system running record image corresponding to the prefabricated data center system, does not need to analyze the global database, and does not analyze the feature data that does not change in the global database of the prefabricated data center system as an abnormal feature, improves the accuracy of the abnormal feature analysis, and further improves the reliability of the management of the abnormal feature distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of system optimization, in particular to a prefabricated data center system management method and system. BACKGROUND

[0002] In the related art, a prefabricated data center system is a data center physical infrastructure system that is designed, assembled and integrated in advance and tested in advance, which can realize various data test functions through a matching corresponding software running service. In the existing scheme, the abnormal features of the data recorded by the prefabricated data center system during operation need to be distributed in order to facilitate subsequent system operation management. However, in the existing scheme, the global database needs to be analyzed, which may incorrectly analyze the feature data that has not changed in the global database of the prefabricated data center system as an abnormal feature, making it difficult to ensure the accuracy of the abnormal feature analysis, and thus affecting the reliability of the subsequent management of the abnormal feature distribution. SUMMARY

[0003] To solve the above problems, the present application provides a prefabricated data center system management method, comprising:

[0004] obtaining a first system operation record image of the prefabricated data center system and obtaining a second system operation record image; the first system operation record image is the most recent system operation record image among the system operation record images of the prefabricated data center system; the second system operation record image is a designated system operation record image among the system operation record images of the prefabricated data center system, and the image node of the second system operation record image is before the image node of the first system operation record image;

[0005] comparing the first system operation record image and the second system operation record image to obtain the different feature data in the first system operation record image;

[0006] analyzing whether the different feature data covers an abnormal feature distribution, the abnormal feature distribution including at least one of a crash feature distribution and a potential error feature distribution;

[0007] based on the analysis information of the analysis of the different feature data, managing the abnormal feature distribution.

[0008] In a possible implementation, the system operation record image includes a software operation record image of the prefabricated data center system.

[0009] The analysis of whether the different feature data covers an abnormal feature distribution includes:

[0010] obtaining a difference label of the difference characteristic data corresponding to the running characteristic, the difference label being a newly-added running characteristic or a changed running characteristic;

[0011] analyzing whether the difference characteristic data covers an abnormal characteristic distribution based on the difference label of the difference characteristic data corresponding to the running characteristic;

[0012] The analyzing whether the difference characteristic data covers an abnormal characteristic distribution based on the difference label of the difference characteristic data corresponding to the running characteristic comprises:

[0013] When the difference label is a newly-added running characteristic, obtaining a running characteristic vector of the difference characteristic data corresponding to the running characteristic, the running characteristic vector comprising at least one of an independent running characteristic vector and a cooperative running characteristic vector;

[0014] When the running characteristic vector satisfies a preset crash comparison characteristic, it is determined that the difference characteristic data contains the crash characteristic distribution;

[0015] The analyzing whether the difference characteristic data covers an abnormal characteristic distribution based on the running characteristic type of the difference characteristic data corresponding to the running characteristic further comprises:

[0016] When the running characteristic vector does not satisfy the preset crash comparison characteristic, and the difference characteristic data corresponding to the running characteristic is a non-standard label running characteristic, it is determined that the difference characteristic data contains the potential error characteristic distribution.

[0017] In a possible implementation, the analyzing whether the difference characteristic data covers an abnormal characteristic distribution based on the difference label of the difference characteristic data corresponding to the running characteristic comprises:

[0018] When the difference label is a changed running characteristic, and the difference characteristic data corresponding to the running characteristic is a non-standard label running characteristic, it is determined that the difference characteristic data contains the potential error characteristic distribution.

[0019] In a possible implementation, the system running record image comprises a storage operation record image of the prefabricated data center system.

[0020] The analyzing whether the difference characteristic data covers an abnormal characteristic distribution comprises:

[0021] searching for a specified running instruction in the difference characteristic data, the specified running instruction comprising instruction data related to a crash program;

[0022] When the specified running instruction is searched in the difference characteristic data, it is determined that the difference characteristic data contains the potential error characteristic distribution.

[0023] In a possible implementation, the system operation record image comprises a storage operation record image of the prefabricated data center system; and the analysis of whether the abnormal feature distribution is included in the difference feature data comprises:

[0024] searching a task category pointing task in the difference feature data, the task category pointing task being a category of a pointing task;

[0025] when the task category pointing task is searched, a first task scheduling area of a running task pointed by the task category pointing task is acquired;

[0026] when the first task scheduling area does not match a second task scheduling area of the prefabricated data center system, it is determined that the potential error feature distribution is included in the difference feature data.

[0027] In a possible implementation, the second system operation record image is acquired by:

[0028] in the system operation record images of the prefabricated data center system, a system operation record image in which a mapping node is before a mapping node of the first system operation record image and is closest to the mapping node of the first system operation record image is acquired as the second system operation record image.

[0029] In a possible implementation, the second system operation record image is acquired by:

[0030] in the system operation record images of the prefabricated data center system, a system operation record image in which a mapping node is a specified mapping node and is before a mapping node of the first system operation record image is acquired as the second system operation record image.

[0031] In a possible implementation, the management of the abnormal feature distribution based on the analysis information of the analysis of the difference feature data comprises:

[0032] when the abnormal feature distribution in the difference feature data comprises the crash feature distribution, a first operation and maintenance indication is sent to an operation and maintenance service system, the first operation and maintenance indication being used to instruct to handle the crash feature distribution;

[0033] when the abnormal feature distribution in the difference feature data comprises the potential error feature distribution, a second operation and maintenance indication is sent to the operation and maintenance service system, the second operation and maintenance indication being used to instruct to verify the potential error feature distribution.

[0034] In a possible implementation, the management of the abnormal feature distribution based on the analysis information of the analysis on the distinguished feature data comprises:

[0035] When the abnormal feature distribution in the distinguished feature data comprises the crash feature distribution, the running feature corresponding to the crash feature distribution is deleted.

[0036] When the abnormal feature distribution in the distinguished feature data comprises the potential error feature distribution, the running feature corresponding to the potential error feature distribution is isolated.

[0037] The application further provides a prefabricated data center system management system comprising a processor and a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program, when executed by the processor, implements the prefabricated data center system management method in any one of the above methods.

[0038] In summary, the prefabricated data center system management method and system provided by the application compare the latest system running record image of the image node corresponding to the prefabricated data center system with the past specified system running record image of the prefabricated data center system, analyze the distinguished feature data in the latest system running record image, and manage based on the analysis result, so that the application only analyzes the distinguished feature data in the system running record image of the prefabricated data center system, does not need to analyze the global database, and does not erroneously analyze the feature data that has not changed in the global database of the prefabricated data center system as abnormal features, thereby improving the accuracy of abnormal feature analysis and the reliability of management of abnormal feature distribution. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the application, the following will briefly introduce the drawings needed in the embodiments. It should be understood that the following drawings are only some embodiments of the application, and therefore should not be considered as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained based on these drawings without creative labor.

[0040] Figure 1 is a flowchart of the prefabricated data center system management method provided by the application. DETAILED DESCRIPTION

[0041] Figure 1 is a flowchart of the prefabricated data center system management method provided by the application, which can be executed by a prefabricated data center system management system, and will be described in detail below.

[0042] Step S11, obtaining a first system running record image of the prefabricated data center system and obtaining a second system running record image. The first system running record image is the most recent system running record image of the image node in each system running record image of the prefabricated data center system. The second system running record image is a designated system running record image in each system running record image of the prefabricated data center system, and the image node of the second system running record image is before the image node of the first system running record image.

[0043] Step S12, performing feature comparison between the first system running record image and the second system running record image to obtain the difference feature data in the first system running record image.

[0044] Step S13, analyzing whether the difference feature data covers an abnormal feature distribution, the abnormal feature distribution including at least one of a crash feature distribution and a potential error feature distribution.

[0045] Step S14, managing the abnormal feature distribution based on the analysis information of analyzing the difference feature data.

[0046] Based on the above steps, the embodiment compares the most recent system running record image of the corresponding image node of the prefabricated data center system with the designated system running record image of the past of the prefabricated data center system, analyzes the difference feature data in the most recent system running record image, and manages the abnormal feature distribution based on the abnormal feature analysis result. Therefore, the application only analyzes the difference feature data in the system running record image corresponding to the prefabricated data center system, does not need to analyze the global database, and does not incorrectly analyze the feature data that has no change in the global database of the prefabricated data center system as an abnormal feature, thereby improving the accuracy of abnormal feature analysis and further improving the reliability of managing the abnormal feature distribution

[0047] In a possible implementation, the system running record image includes a software running record image of the prefabricated data center system, and analyzing whether the difference feature data covers an abnormal feature distribution includes: obtaining a difference label of a running feature corresponding to the difference feature data, the difference label being a newly added running feature or a changed running feature; and analyzing whether the difference feature data covers an abnormal feature distribution based on the difference label of the running feature corresponding to the difference feature data.

[0048] In a possible implementation, the analysis of whether the abnormal feature distribution is covered in the difference feature data based on the difference label of the corresponding running feature of the difference feature data comprises: when the difference label is a newly-added running feature, obtaining a running feature vector of the corresponding running feature of the difference feature data, the running feature vector comprising at least one of an independent running feature vector and a cooperative running feature vector; and when the running feature vector satisfies a preset collapse comparison feature, determining that the collapse feature distribution is covered in the difference feature data.

[0049] In a possible implementation, the analysis of whether the abnormal feature distribution is covered in the difference feature data based on the running feature type of the corresponding running feature of the difference feature data further comprises: when the running feature vector does not satisfy the preset collapse comparison feature, and the corresponding running feature of the difference feature data is a non-standard label running feature, determining that the potential error feature distribution is covered in the difference feature data.

[0050] In a possible implementation, the analysis of whether the abnormal feature distribution is covered in the difference feature data based on the difference label of the corresponding running feature of the difference feature data comprises: when the difference label is a changed running feature, and the corresponding running feature of the difference feature data is a non-standard label running feature, determining that the potential error feature distribution is covered in the difference feature data.

[0051] In a possible implementation, the system running record image comprises a storage operation record image of the prefabricated data center system; and the analysis of whether the abnormal feature distribution is covered in the difference feature data comprises: searching for a specified running instruction in the difference feature data, the specified running instruction comprising instruction data related to a collapse program; and when the specified running instruction is searched in the difference feature data, determining that the potential error feature distribution is covered in the difference feature data.

[0052] In a possible implementation, the system running record image comprises a storage operation record image of the prefabricated data center system; and the analysis of whether the abnormal feature distribution is covered in the difference feature data comprises: searching for a task category pointing instruction in the difference feature data, the task category pointing instruction being a category of a pointing task; when the task category pointing instruction is searched, obtaining a first task scheduling area of a running task pointed by the task category pointing instruction; and when the first task scheduling area does not match a second task scheduling area of the prefabricated data center system, determining that the potential error feature distribution is covered in the difference feature data.

[0053] In a possible implementation, the second system running record image is acquired by: from the system running record images of the prefabricated data center system, a system running record image whose image node is before the image node of the first system running record image is acquired as the second system running record image.

[0054] In a possible implementation, the second system running record image is acquired by: from the system running record images of the prefabricated data center system, a system running record image whose image node is before the image node of the first system running record image is acquired as the second system running record image.

[0055] In a possible implementation, the abnormal feature distribution is managed based on analysis information of analyzing the distinguished feature data, including: when the abnormal feature distribution in the distinguished feature data includes the crash feature distribution, a first operation and maintenance instruction is sent to an operation and maintenance service system, the first operation and maintenance instruction being used for instructing to handle the crash feature distribution; when the abnormal feature distribution in the distinguished feature data includes the potential error feature distribution, a second operation and maintenance instruction is sent to the operation and maintenance service system, the second operation and maintenance instruction being used for instructing to verify the potential error feature distribution.

[0056] In a possible implementation, the abnormal feature distribution is managed based on analysis information of analyzing the distinguished feature data, including: when the abnormal feature distribution in the distinguished feature data includes the crash feature distribution, a first operation and maintenance instruction is sent to an operation and maintenance service system, the first operation and maintenance instruction being used for instructing to handle the crash feature distribution; when the abnormal feature distribution in the distinguished feature data includes the potential error feature distribution, a second operation and maintenance instruction is sent to the operation and maintenance service system, the second operation and maintenance instruction being used for instructing to verify the potential error feature distribution.

[0057] In some alternative implementations, the prefabricated data center system management system described above can include one or more processors and memories. The memories can store data and instructions for the prefabricated data center system management system to execute or use, and the prefabricated data center system management system can implement the methods described above by executing or using the data and instructions.

[0058] In various embodiments, the prefabricated data center system management system can be, but is not limited to, a terminal device such as a server, a desktop computing device, or a mobile computing device (e.g., a laptop computer, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the prefabricated data center system management system can have more or less components and / or different architecture. For example, in some embodiments, the prefabricated data center system management system includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including touch screen displays), a nonvolatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0059] Each of the embodiments in the present specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be mutually referred to.

[0060] The prefabricated data center system management method and system provided in the present application are described in detail above. The principles and implementation manners of the present application are described by using specific examples. The above embodiment descriptions are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the present specification should not be understood as a limitation of the present application.

[0061] Based on one aspect of the present application, a computer readable storage medium is provided for storing program codes for executing the prefabricated data center system management method described in the above embodiments.

[0062] Based on one aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the method provided in the various optional implementation manners of the above embodiments.

[0063] The terms "first", "second", "third", "fourth", and the like in the description of the application and in the claims of the foregoing, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of these terms herein is to be construed to cover a generalised use of such terms to refer to similar elements independently of each other occurrence in the description and claims of this application. It is to be understood that the data shown herein is presented by way of example only and that the application can be embodied in many different forms without departing from the spirit or scope of the application. It is to be understood that the application is to be construed as covering all reasonable modifications and alterations of this application, such as can be given to any of the elements described by way of examples, without departing from the spirit of the application.

[0064] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0065] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected based on actual needs to achieve the purpose of the embodiment.

[0066] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0067] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0068] The above-described and above-embodied examples are only used to illustrate the technical solutions of the present application, but not to limit the same. Although the above-mentioned embodiments have been described in detail, those skilled in the art should understand that they can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for part of the technical features. The modification or replacement does not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0069] The above-described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the drawings is not intended to limit the protection scope of the present application, but only represents selected embodiments of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. In addition, based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

Claims

1. A management method for a prefabricated data center system, characterized in that, The method comprises: acquiring a first system running record image of the prefabricated data center system and acquiring a second system running record image; the first system running record image is the most recent system running record image of the image nodes in each system running record image of the prefabricated data center system; the second system running record image is a designated system running record image in each system running record image of the prefabricated data center system, and the image node of the second system running record image is before the image node of the first system running record image; characteristic comparison is performed between the first system running record image and the second system running record image to obtain differential characteristic data in the first system running record image; analysis is performed on whether the differential characteristic data covers an abnormal characteristic distribution, the abnormal characteristic distribution comprising at least one of a crash characteristic distribution and a potential error characteristic distribution; management is performed on the abnormal characteristic distribution based on analysis information obtained by analyzing the differential characteristic data; the system running record image comprises a storage operation record image of the prefabricated data center system; and the analysis on whether the differential characteristic data covers an abnormal characteristic distribution comprises: searching for a task category pointing to a task in the differential characteristic data, the task category pointing to a task; when the task category pointing to a task is searched, a first task scheduling area of a running task pointed to by the task category pointing to a task is acquired; when the first task scheduling area does not match a second task scheduling area of the prefabricated data center system, it is determined that the differential characteristic data contains the potential error characteristic distribution.

2. The prefabricated data center system management method of claim 1, wherein, the system running record image comprises a software running record image of the prefabricated data center system; the analysis on whether the differential characteristic data covers an abnormal characteristic distribution comprises: acquiring a differential label of a running characteristic corresponding to the differential characteristic data, the differential label being a newly added running characteristic or a changed running characteristic; based on the differential label of the running characteristic corresponding to the differential characteristic data, analyzing whether the differential characteristic data covers an abnormal characteristic distribution; the analysis on whether the differential characteristic data covers an abnormal characteristic distribution based on the differential label of the running characteristic corresponding to the differential characteristic data comprises: when the differential label is a newly added running characteristic, a running characteristic vector of the running characteristic corresponding to the differential characteristic data is acquired, the running characteristic vector comprising at least one of an independent running characteristic vector and a cooperative running characteristic vector; when the running characteristic vector satisfies a preset crash comparison characteristic, it is determined that the differential characteristic data contains the crash characteristic distribution; the analysis on whether the differential characteristic data covers an abnormal characteristic distribution based on the running characteristic type of the running characteristic corresponding to the differential characteristic data further comprises: when the running characteristic vector does not satisfy the preset crash comparison characteristic, and the running characteristic corresponding to the differential characteristic data is a non-standard label running characteristic, it is determined that the differential characteristic data contains the potential error characteristic distribution.

3. The method of claim 2, wherein, The distinguishing label based on the distinguishing feature data corresponding to the running feature is used to analyze whether the abnormal feature distribution is covered in the distinguishing feature data, including: When the distinguishing label is a changed running feature, and the distinguishing feature data corresponding to the running feature is a non-standard label running feature, it is determined that the potential error feature distribution is contained in the distinguishing feature data.

4. The method of claim 1-3, wherein, The second system running record image is obtained, including: Among the system running record images of the prefabricated data center system, the system running record image in which the image node is before the image node of the first system running record image and is closest to the image node of the first system running record image is obtained as the second system running record image.

5. The method of claim 1-3, wherein, The second system running record image is obtained, including: Among the system running record images of the prefabricated data center system, the system running record image in which the image node is the specified image node and is before the image node of the first system running record image is obtained as the second system running record image.

6. The method of claim 1-3, wherein, The analysis information based on the analysis of the distinguishing feature data is used to manage the abnormal feature distribution, including: When the abnormal feature distribution in the distinguishing feature data includes the crash feature distribution, a first operation and maintenance instruction is sent to an operation and maintenance service system, and the first operation and maintenance instruction is used to instruct to handle the crash feature distribution; When the abnormal feature distribution in the distinguishing feature data includes the potential error feature distribution, a second operation and maintenance instruction is sent to the operation and maintenance service system, and the second operation and maintenance instruction is used to instruct to verify the potential error feature distribution.

7. The method of claim 1-3, wherein, The analysis information based on the analysis of the distinguishing feature data is used to manage the abnormal feature distribution, including: When the abnormal feature distribution in the distinguishing feature data includes the crash feature distribution, the running feature corresponding to the crash feature distribution is deleted; When the abnormal feature distribution in the distinguishing feature data includes the potential error feature distribution, the running feature corresponding to the potential error feature distribution is isolated.

8. A prefabricated data center system management system, characterized by The computer program is stored in the readable storage medium, and when the computer program is executed by the processor, the prefabricated data center system management method in any one of claims 1-7 is realized.

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