WO2020082516A1 - 一种运维数据的处理方法、系统及装置 - Google Patents

一种运维数据的处理方法、系统及装置 Download PDF

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Publication number
WO2020082516A1
WO2020082516A1 PCT/CN2018/119361 CN2018119361W WO2020082516A1 WO 2020082516 A1 WO2020082516 A1 WO 2020082516A1 CN 2018119361 W CN2018119361 W CN 2018119361W WO 2020082516 A1 WO2020082516 A1 WO 2020082516A1
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data
maintenance
target server
dimension
repair
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PCT/CN2018/119361
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English (en)
French (fr)
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梁敏聪
吴炜隽
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网宿科技股份有限公司
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Priority to US16/959,941 priority Critical patent/US20210073204A1/en
Priority to EP18937657.7A priority patent/EP3869340A4/en
Publication of WO2020082516A1 publication Critical patent/WO2020082516A1/zh

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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
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    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0793—Remedial or corrective actions
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    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
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    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
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Definitions

  • the present invention relates to the field of Internet technology, and in particular, to a method, system, and device for processing operation and maintenance data.
  • the performance of software products can be judged by means of regularly checking the running status of the software, viewing the error log of the software, or analyzing the monitoring data and business data of the software itself. After getting the judgment result, you can find the problems in the software product, which are usually reflected in the server where the software product is installed. In this way, by testing the performance of the software products in the server, the server that needs to be repaired can be identified.
  • the purpose of this application is to provide a method, system and device for processing operation and maintenance data, which can improve the accuracy of product performance evaluation.
  • the present application provides a method for processing operation and maintenance data, the method includes: acquiring multiple operation and maintenance data of a target server; Data; according to the quantized data of each dimension, determine the quality assessment information of the target server.
  • another aspect of the present application also provides an operation and maintenance data processing system, which is used to implement the above method.
  • another aspect of the present application also provides an operation and maintenance data processing device.
  • the device includes a processor and a memory.
  • the memory is used to store a computer program. When the computer program is executed by the processor To implement the above method.
  • the technical solution provided by this application can automatically analyze various operation and maintenance data of the target server to determine the current operation quality of the target server.
  • the operation and maintenance data may be converted into quantitative data of various dimensions.
  • the quantitative data can make different operation and maintenance data adopt a unified measurement standard, so that the quantitative data can be directly analyzed later, thereby avoiding the problem of overly complicated data analysis methods caused by different types of operation and maintenance data, and then The process of data analysis is simplified, and the efficiency of data analysis is improved.
  • the quantitative data of different dimensions can reflect the performance of various aspects of the target server. In this way, the current operational quality of the target server can be determined according to the evaluation of the performance of the target server in various aspects. It can be seen from the above that the technical solution provided by this application can simplify the process of data analysis, and at the same time analyze data of different dimensions, and can accurately obtain the current quality assessment information of the target server.
  • FIG. 1 is a schematic structural diagram of a system architecture in an embodiment of the present invention.
  • FIG. 2 is a flowchart of a processing method of operation and maintenance data in an embodiment of the present invention
  • FIG. 3 is a schematic diagram of an operation and maintenance data processing system in an embodiment of the present invention.
  • FIG. 4 is a schematic flowchart of an automatic repair server in an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an operation and maintenance data processing device in an embodiment of the present invention.
  • the processing method for operation and maintenance data provided in this application can be applied to the system architecture shown in FIG. 1.
  • the system architecture may include users, server repair systems, and third-party databases.
  • the user can provide an operation and maintenance data acquisition interface and repair rules for the server, and the user can view the final software operation quality.
  • the third-party database may store various operation and maintenance data of the target server, and may store quantitative data generated during data analysis and repair result data generated during repair of the target server.
  • the server repair system can analyze various operation and maintenance data of the target server to determine the services to be repaired in the target server, and automatically repair these services.
  • the processing method of the operation and maintenance data provided by the present application may include the following steps.
  • the user can provide the server repair system with an acquisition interface for operation and maintenance data.
  • the server repair system can read various operation and maintenance data of the target server from a third-party database.
  • the server repair system may include a data collection layer, which may acquire data generated by the software itself or data generated by a monitoring system external to the software.
  • the acquired various operation and maintenance data may include at least the business data generated internally by the software in the target server, the monitoring data generated by the monitoring system in the target server, the log data of the target server, and the data used to characterize the Target server performance indicator data.
  • more O & M data can be analyzed.
  • the various O & M data exemplified above are only for the convenience of explaining the technical solutions of this application, and do not mean that the technical solutions of this application are only applicable to the above Examples of various O & M data.
  • different operation and maintenance data may have different types.
  • the business data generated within the software may include relational data for storing persistent data, and may also include Key-Value (key-value pair) data for storing frequently changed data.
  • the data outside the software may include time-series data used to store real-time monitoring data, and may also include log index type data used to store logs in a uniform format.
  • the methods for obtaining different O & M data are also different.
  • a variety of different operation and maintenance data can be uniformly acquired through preset instructions having a specified data type and a specified format.
  • the specified data type may be, for example, a key-value type
  • the specified format may be, for example, a json format
  • the preset specification may be a key-value type json format instruction.
  • the preset instruction with the specified data type and specified format can be read, and then the preset instruction can be converted to the type of the operation and maintenance data to be read into A data query statement that matches the current operation and maintenance data to be obtained, so that the corresponding operation and maintenance data can be obtained through the converted data query statement.
  • the converted data query statement may include, for example, a general-purpose structured query language SQL, a log query-type data field specific query language DSL, a grammatical string for a specific database, and the like.
  • the key-value json format instruction can be expressed as:
  • the converted data query statement can be expressed as: "select from web”.
  • S5 Determine the quality evaluation information of the target server according to the quantized data of each dimension.
  • the operation and maintenance data can be processed by the data processing layer.
  • various operation and maintenance data can be converted into quantized data of multiple dimensions.
  • the converted quantitative data can have the same measurement standard, so that different operation and maintenance data can be analyzed uniformly.
  • the above multiple dimensions may correspond to different services of the target server.
  • the converted quantitative data may include quantitative data characterizing the state of the server, and may also represent quantitative data representing the success rate of software services in the server.
  • the above server status and software business success rate can be used as different dimensions. In this way, the target server can be detected comprehensively by refining various operation and maintenance data into quantitative data of different dimensions.
  • the operation and maintenance data when the operation and maintenance data is converted into quantized data, different quantization methods can be selected according to the complexity of the operation and maintenance data. Specifically, if the operation and maintenance data is relatively simple, the original operation and maintenance parameters included in the operation and maintenance data may be read, and the quality level associated with the original operation and maintenance parameters may be identified.
  • the operation and maintenance data characterizing the state of the server may be: "server state: A", where "A” may represent the original operation and maintenance parameters included in the operation and maintenance data.
  • server status you can set a default rule: A means excellent, B means good, and C means fail. Then the excellent, good, and unsuccessful logos that reflect the quality of the server status can be used as the above-mentioned quality level.
  • the relationship between the original operation and maintenance parameters and the quality level can be established.
  • the operation and maintenance data characterizing the success rate of the software business may be: "success rate: 97.2%”
  • the preset default rules may be: more than 99% is excellent, 95% 99% is good, and 95% or less is a failure. Then the quality level associated with the original operation and maintenance parameters contained in the current operation and maintenance data may be good.
  • each quality level can be defined by a preset rule Standard parameters.
  • the preset rule defines that the standard parameter corresponding to an excellent quality level is 100, the standard parameter corresponding to a good quality level is 80, and the standard parameter corresponding to a failing quality level is 50.
  • a standard parameter defined by the preset rule for characterizing the quality level can be determined, and the original O & M parameter is replaced with the standard parameter.
  • some performance of the target server is often not measurable by one kind of operation and maintenance parameter, but requires multiple operation and maintenance parameters to be comprehensively measured. In this case, when quantifying these O & M parameters, the above quantization method cannot be used.
  • two or more operation and maintenance data can be trained as training samples in advance through machine learning, so that the machine learning after training The model can comprehensively obtain the quantitative data of the target server in a certain dimension for the input various operation and maintenance data.
  • algorithms such as decision trees, logistic regression, support vector machines, and Naive Bayes can be used to train pre-prepared operation and maintenance data samples to obtain a machine learning model that can convert a variety of operation and maintenance data into quantitative data.
  • the acquired operation and maintenance data of the target server may be input into the machine learning model, so that the evaluation parameters of each dimension are output through the machine learning model, and the evaluation parameters of each dimension As the quantitative data of each dimension.
  • the evaluation parameters of the various dimensions may be parameters that are uniform in value.
  • the evaluation parameter may be 100, 80, and 50 in the above-mentioned embodiments, which are used to characterize the quality level.
  • the quantized data after the quantized data in various dimensions are converted, the quantized data usually changes continuously with time, so the quantized data can be stored in a time-series database. Subsequently, these quantitative data can be read from the time-series database, and these quantitative data can be visually displayed through the operation quality score dashboard, operation quality trend chart, etc., so that the user can intuitively view the operation of the target server status.
  • the quantized data of each dimension can reflect the performance of the target server in different aspects
  • the quantized data of each dimension can be aggregated, and the aggregated information can be used as the quality assessment information of the target server.
  • the quantized data of each dimension may include parameters such as the interface usage status and task accumulation status of the target server. By summarizing these parameters, the current overall operating quality of the target server can be obtained.
  • the quantized data of each dimension can be analyzed to determine the target dimension that needs to be repaired.
  • the repair trigger condition associated with the current dimension can be obtained, and if the quantized data of the current dimension meets the repair trigger condition, the current dimension can be used as the target dimension to be repaired .
  • the repair trigger condition associated with the server state may be that the quantitative data of the server state is less than or equal to 50.
  • the actual data obtained by quantization can be compared with the threshold defined by the repair trigger condition. If the size relationship in the repair trigger condition is met, it can be considered that the current dimension is abnormal and needs to be repaired. In this way, for the quantitative data of different dimensions, different repair triggering conditions can be used to determine, so as to accurately obtain the target dimension that needs to be repaired.
  • a repair rule matching the target dimension can be obtained from the repair rules provided by the user in advance, and the target server is repaired according to the repair rule .
  • each dimension of the target server may be interface availability, task accumulation degree, etc.
  • the matching repair rule may be to restart the unavailable interface service.
  • the target dimension indicates that the number of accumulations of tasks in the target server exceeds a specified threshold
  • the matching repair rule may be to switch unprocessed accumulation tasks in the target server to a standby server cluster.
  • more repair rules can be set in advance. In this way, after the target dimension is determined, the matching repair rule can be selected to automatically repair the target server.
  • data characterizing the repair result may be generated. Since the data characterizing the repair result is not generated regularly, it will not change continuously over time, so that the characterization repair The resulting data is stored in a relational database. Subsequently, the data characterizing the repair result may be read from the relational database, and the data characterizing the repair result may be visually displayed so that the user can view the repair result.
  • the present application also provides an operation and maintenance data processing system, which can be used to implement the technical solutions described in the foregoing embodiments.
  • the system may include:
  • the data collection layer is used to obtain various operation and maintenance data of the target server
  • the data processing layer is used to convert the operation and maintenance data into quantized data of various dimensions according to preset rules, and based on the quantized data, determine the target dimension to be repaired by the target server;
  • the data repair layer is used to obtain repair rules that match the target dimension, and repair the target server according to the repair rules.
  • the data processing layer includes:
  • the machine learning model quantization module is used to input the operation and maintenance data into a preset machine learning model to output the judgment parameters of each dimension through the preset machine learning model, and use the judgment parameters of each dimension as the respective Dimensional quantitative data.
  • the data processing layer includes:
  • the original operation and maintenance parameter identification module is used to read the original operation and maintenance parameters contained in the operation and maintenance data and identify the quality level associated with the original operation and maintenance parameters;
  • a parameter replacement module configured to determine standard parameters for characterizing the quality level defined by the preset rules, and replace the original operation and maintenance parameters with the standard parameters
  • the quantization module is configured to use the operation and maintenance data after parameter replacement as the quantized data processed according to the preset rule.
  • the data processing layer includes:
  • the trigger condition judgment module is used to obtain the repair trigger condition associated with the current dimension, and if the quantized data of the current dimension meets the repair trigger condition, use the current dimension as the target dimension to be repaired.
  • system further includes:
  • the data storage layer is used to store the quantitative data in a time-series database and store the data characterizing the repair result in a relational database.
  • system further includes:
  • the data display layer is used to read the quantized data and the data characterizing the repair result from the time-series database and the relational database, respectively, and combine the quantized data and the data characterizing the repair result Visualize it.
  • the present application also provides an operation and maintenance data processing device.
  • the device includes a processor and a memory.
  • the memory is used to store a computer program.
  • the computer program When executed by the processor, it can be implemented.
  • the processing method of the above operation and maintenance data When the computer program is executed by the processor, it can be implemented.
  • the technical solution provided by this application can automatically analyze various operation and maintenance data of the target server to determine the current operation quality of the target server.
  • the operation and maintenance data may be converted into quantitative data of various dimensions.
  • the quantitative data can make different operation and maintenance data adopt a unified measurement standard, so that the quantitative data can be directly analyzed later, thereby avoiding the problem of overly complicated data analysis methods caused by different types of operation and maintenance data, and then The process of data analysis is simplified, and the efficiency of data analysis is improved.
  • the quantitative data of different dimensions can reflect the performance of various aspects of the target server. In this way, the current operational quality of the target server can be determined according to the evaluation of the performance of the target server in various aspects. It can be seen from the above that the technical solution provided by this application can simplify the process of data analysis, and at the same time analyze data of different dimensions, and can accurately obtain the current quality assessment information of the target server.
  • each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware.
  • the above technical solutions can be embodied in the form of software products in essence or part that contributes to the existing technology, and the computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic Discs, optical discs, etc., include several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to perform the methods described in the various embodiments or some parts of the embodiments.

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Abstract

本发明公开了一种运维数据的处理方法、系统及装置，其中，所述方法包括：获取目标服务器的多种运维数据；按照预设规则将所述运维数据转换为各个维度的量化数据；根据所述各个维度的量化数据，确定所述目标服务器的质量评估信息。本申请提供的技术方案，能够提高对产品性能的评判精度。

Description

一种运维数据的处理方法、系统及装置 技术领域
本发明涉及互联网技术领域，特别涉及一种运维数据的处理方法、系统及装置。
背景技术
目前，可以通过定时检查软件的运行状态、查看软件的错误日志或者分析软件本身的监控数据和业务数据等手段对软件产品的性能进行评判。在得到评判结果之后，可以发现软件产品中存在的问题，这些问题通常会在安装软件产品的服务器中体现出来。这样，通过对服务器中软件产品的性能进行检测，从而可以识别需要修复的服务器。
然而，上述的对软件产品的性能进行评判的方法，往往需要运维人员对大量的数据进行分析。一方面，只有软件发生故障时，运维人员才能发现异常数据，这样无法有效地防止软件出错；另一方面，软件的日常告警数据量相当庞大，运维人员很容易遗漏其中的高风险警报，从而无法有效地对服务器进行修复。因此，当前亟需一种高效并且准确的产品性能评判手段。
发明内容
本申请的目的在于提供一种运维数据的处理方法、系统及装置，能够提高对产品性能的评判精度。
为实现上述目的，本申请一方面提供一种运维数据的处理方法，所述方法包括：获取目标服务器的多种运维数据；按照预设规则将所述运维数据转换为各个维度的量化数据；根据所述各个维度的量化数据，确定所述目标服务器的质量评估信息。
为实现上述目的，本申请另一方面还提供一种运维数据的处理系统，所述系统用于实现上述的方法。
为实现上述目的，本申请另一方面还提供一种运维数据的处理装置，所述 装置包括处理器和存储器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现上述的方法。
由上可见，本申请提供的技术方案，可以对目标服务器的多种运维数据进行自动分析，从而确定出目标服务器当前的运营质量。具体地，在获取到目标服务器的多种运维数据之后，可以将这些运维数据转换为各个维度的量化数据。该量化数据可以使得不同的运维数据采用统一的衡量标准，这样，后续可以直接对该量化数据进行分析，从而避免了由于运维数据的类型不同而导致的数据分析手段过于繁杂的问题，进而简化了数据分析的过程，提高了数据分析的效率。不同维度的量化数据可以体现目标服务器各个方面的性能。这样，根据对目标服务器各个方面的性能进行评估，从而可以确定出目标服务器当前的运营质量。由上可见，本申请提供的技术方案，能够简化数据分析的过程，同时针对不同维度的数据进行分析，可以精确地得出目标服务器当前的质量评估信息。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施方式中系统架构的结构示意图；
图2是本发明实施方式中运维数据的处理方法的流程图；
图3是本发明实施方式中运维数据的处理系统的示意图；
图4是本发明实施方式中自动修复服务器的流程示意图；
图5是本发明实施方式中运维数据的处理装置的结构示意图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明实施方式作进一步地详细描述。
本申请提供的运维数据的处理方法，可以应用于如图1所示的系统架构中。具体地，所述系统架构中可以包括用户、服务器修复系统以及第三方数据库。其中，所述用户可以提供运维数据的获取接口以及针对服务器的修复规则，并 且所述用户可以查看最终的软件运营质量。所述第三方数据库则可以存储目标服务器的多种运维数据，并且可以存储在数据分析过程中产生的量化数据以及在目标服务器修复过程中产生的修复结果数据等。所述服务器修复系统可以针对目标服务器的多种运维数据进行分析，从而确定出目标服务器中待修复的业务，并对这些业务进行自动修复。
具体地，请参阅图1和图2，本申请提供的运维数据的处理方法可以包括以下步骤。
S1：获取目标服务器的多种运维数据。
在本实施方式中，用户可以向服务器修复系统提供运维数据的获取接口，通过该获取接口，服务器修复系统可以从第三方数据库中读取目标服务器的多种运维数据。请参阅图3，所述服务器修复系统中可以包括数据汇集层，该数据汇集层可以获取软件本身产生的数据或者软件外部的监控系统产生的数据。这样，获取的多种运维数据则可以至少包括所述目标服务器中软件内部产生的业务数据、所述目标服务器中监控系统产生的监控数据、所述目标服务器的日志数据以及用于表征所述目标服务器性能的指标数据。当然，在实际应用中，还可以对更多的运维数据进行分析，上述例举的多种运维数据只是为了便于阐述本申请的技术方案，并不表示本申请的技术方案仅适用于以上例举出的多种运维数据。
在本实施方式中，不同的运维数据可以具备不同的类型。例如，软件内部产生的业务数据可以包括用于存储持久性数据的关系型数据，还可以包括用于存储频繁变动的数据的Key-Value(键值对)型数据。软件外部的数据可以包括用于存储实时监控数据的时序型数据，还可以包括用于存储格式统一的日志的日志索引型数据。
由于各种运维数据的来源不同，因此获取不同运维数据的方法也不同。为了简化数据获取的过程，在本实施方式中可以通过具备指定数据类型和指定格式的预设指令来统一地获取多种不同的运维数据。具体地，该指定数据类型例如可以是key-value类型，该指定格式例如可以是json格式，那么该预设指定便可以是key-value型json格式的指令。在实际应用中，当需要获取运维数据时，可以读取该具备指定数据类型和指定格式的预设指令，然后可以按照待读取的运维数据的类型，将所述预设指令转换为与当前待获取的运维数据相匹配 的数据查询语句，从而可以通过转换得到的所述数据查询语句获取对应的运维数据。具体地，转换后的数据查询语句例如可以包括通用型结构化查询语言SQL、日志索引型数据领域特定查询语言DSL、针对特定数据库的语法字符串等。举例来说，假设该key-value型json格式的指令可以表示为：
{“table”:“port”，
“field”：“web”}
那么针对通用型结构化查询语言SQL而言，转换后的数据查询语句可以表示为：“select web from port”。
这样，通过将统一的预设指令转换为不同的数据查询语句，从而可以通过简洁的方式获取多种不用类型的运维数据。
S3：按照预设规则将所述运维数据转换为各个维度的量化数据。
S5：根据所述各个维度的量化数据，确定所述目标服务器的质量评估信息。
请参阅图3，在本实施方式中，当数据汇集层获取到多种运维数据之后，可以由数据处理层对这些运维数据进行处理。具体地，由于原始的运维数据差别较大，因此无法利用统一的衡量标准来判断运维数据的质量。为了解决该问题，在本实施方式中可以将多种运维数据转换为多个维度的量化数据。转换后的量化数据可以具备相同的衡量标准，从而能够统一对不同的运维数据进行分析。
在本实施方式中，上述的多个维度可以对应目标服务器的不同业务。例如，转换后的量化数据中，可以包括表征服务器状态的量化数据，还可以表征服务器中软件业务成功率的量化数据。上述的服务器状态和软件业务成功率便可以作为不同的维度。这样，通过将多种运维数据细化为不同维度的量化数据，从而可以全面地对目标服务器进行检测。
在本实施方式中，在将运维数据转换为量化数据时，可以根据运维数据的复杂程度，选用不同的量化方法。具体地，若运维数据比较简单，则可以读取所述运维数据中包含的原始运维参数，并识别所述原始运维参数关联的品质层级。举例来说，表征服务器状态的运维数据可以是：“服务器状态：A”，其中，“A”便可以表示该运维数据中包含的原始运维参数。针对服务器状态而言，可以设定一个默认的规则：A表示优秀，B表示良好，C表示不及格。那么优秀、良好、不及格这些反映服务器状态品质的标识便可以作为上述的品质层级。通过默认的规则，可以建立原始运维参数与品质层级之间的关联。又例如，表征 软件业务成功率的运维数据可以是：“成功率：97.2％”，而针对软件业务成功率而言，预先设定的默认规则可以是：99％以上为优秀，95％至99％为良好，95％以下为不及格。那么当前的运维数据中包含的原始运维参数关联的品质层级则可以是良好。
由上可见，针对不同的运维数据，与同一品质层级关联的原始运维参数可以具备不同的形式，为了将这些不同的形式进行统一，在本实施方式中可以由预设规则限定各个品质层级的标准参数。例如，该预设规则限定：优秀的品质层级对应的标准参数为100，良好的品质层级对应的标准参数为80，不及格的品质层级对应的标准参数为50。这样，在识别所述原始运维参数关联的品质层级之后，可以确定由所述预设规则限定的用于表征所述品质层级的标准参数，并将所述原始运维参数替换为所述标准参数。通过这样的处理方式，上述关于服务器状态和软件业务成功率的运维数据中，“A”可以被替换为100，“97.2％”可以被替换为80。这样，在进行参数替换之后，关联同一品质层级的参数可以具备相同的数值，从而将不同的运维数据进行了统一量化。参数替换后的运维数据便可以作为按照所述预设规则处理后的量化数据。
在一个实施方式中，目标服务器的有些性能往往不是一种运维参数能够衡量的，而是需要多种运维参数进行综合衡量。在这种情况下，对这些运维参数进行量化时，则无法采用以上的量化方法。为了得到多种不同的运维数据所反映的目标服务器的性能，可以通过机器学习的方法，预先将两种或者多于两种的运维数据作为训练样本进行训练，从而使得训练后的机器学习模型能够针对输入的多种运维数据，综合得到目标服务器在某个维度的量化数据。
具体地，可以采用决策树、逻辑回归、支持向量机、朴素贝叶斯等算法，对预先准备的运维数据样本进行训练，从而得到能够将多种运维数据转换为量化数据的机器学习模型。在训练得到机器训练模型后，可以将获取的所述目标服务器的运维数据输入该机器学习模型中，从而通过所述机器学习模型输出各个维度的评判参数，并将所述各个维度的评判参数作为所述各个维度的量化数据。其中，所述各个维度的评判参数便可以是经过数值统一的参数。例如，该评判参数可以是上述实施方式中的100、80以及50这些用于表征品质层级的数值。
在一个实施方式中，在转换得到各个维度的量化数据后，由于该量化数据 通常会随着时间的推移而不断变化，因此，可以将该量化数据存储于时序型数据库中。后续，可以从所述时序型数据库中读取这些量化数据，并将这些量化数据通过运营质量分数仪表盘、运营质量走势图等可视化的方式进行展示，从而使得用户能够直观地查看目标服务器的运行状态。
在本实施方式中，由于各个维度的量化数据能够体现目标服务器在不同方面的性能，因此可以将这些各个维度的量化数据进行汇总，汇总后的信息便可以作为目标服务器的质量评估信息。例如，所述各个维度的量化数据中可以包括目标服务器的接口使用情况、任务堆积情况等方面的参数，通过将这些参数进行汇总，便可以得到目标服务器当前的整体运行质量。
在本实施方式中，在得到各个维度的量化数据之后，可以对每个维度的量化数据进行分析，从而确定出需要修复的目标维度。具体地，针对当前维度的量化数据，可以获取与当前维度相关联的修复触发条件，若所述当前维度的量化数据符合所述修复触发条件，则可以将所述当前维度作为待修复的目标维度。例如，当前维度为服务器状态，那么与服务器状态相关联的修复触发条件可以是服务器状态的量化数据小于或者等于50。此时，可以将量化得到的实际数据与该修复触发条件限定的阈值进行比较，如果符合该修复触发条件中的大小关系，则可以认为当前维度存在异常，需要被修复。这样，针对不同维度的量化数据，可以采用不同的修复触发条件进行判定，从而准确得到需要修复的目标维度。
在一个实施方式中，在确定出需要修复的目标维度之后，可以从用户预先提供的修复规则中，获取与该目标维度相匹配的修复规则，并按照所述修复规则对所述目标服务器进行修复。具体地，请参阅图4，目标服务器的各个维度可以是接口可用性、任务堆积程度等。那么当目标维度表征所述目标服务器的接口不可用时，相匹配的修复规则可以是重启不可用的接口服务。当所述目标维度表征所述目标服务器中任务的堆积数量超过指定阈值时，相匹配的修复规则可以是将所述目标服务器中未处理的堆积任务切换至备用服务器集群。当然，在实际应用中，可以预先设置更多的修复规则。这样，在确定出目标维度之后，便可以选用相匹配的修复规则，对目标服务器进行自动修复。
在一个实施方式中，在对目标服务器修复之后，可以产生表征修复结果的数据，由于表征修复结果的数据并非是定时产生的，因此不会随着时间的推移 而不断变化，从而可以将表征修复结果的数据存储于关系型数据库中。后续，也可以从所述关系型数据库中读取所述表征修复结果的数据，并将所述表征修复结果的数据通过可视化的方式进行展示，以便用户查看修复结果。
请参阅图3，本申请还提供一种运维数据的处理系统，所述系统可以用于实现上述实施方式中描述的技术方案。具体地，在实际应用中，所述系统可以包括：
数据汇集层，用于获取目标服务器的多种运维数据；
数据处理层，用于按照预设规则将所述运维数据转换为各个维度的量化数据，并基于所述量化数据，确定所述目标服务器待修复的目标维度；
数据修复层，用于获取与所述目标维度相匹配的修复规则，并按照所述修复规则对所述目标服务器进行修复。
在一个实施方式中，所述数据处理层包括：
机器学习模型量化模块，用于将所述运维数据输入预设机器学习模型，以通过所述预设机器学习模型输出各个维度的评判参数，并将所述各个维度的评判参数作为所述各个维度的量化数据。
在一个实施方式中，所述数据处理层包括：
原始运维参数识别模块，用于读取所述运维数据中包含的原始运维参数，并识别所述原始运维参数关联的品质层级；
参数替换模块，用于确定由所述预设规则限定的用于表征所述品质层级的标准参数，并将所述原始运维参数替换为所述标准参数；
量化模块，用于将参数替换后的运维数据作为按照所述预设规则处理后的量化数据。
在一个实施方式中，所述数据处理层包括：
触发条件判断模块，用于获取与当前维度相关联的修复触发条件，若所述当前维度的量化数据符合所述修复触发条件，将所述当前维度作为待修复的目标维度。
在一个实施方式中，所述系统还包括：
数据存储层，用于将所述量化数据存储于时序型数据库中，并将表征修复结果的数据存储于关系型数据库中。
在一个实施方式中，所述系统还包括：
数据展示层，用于分别从所述时序型数据库中和所述关系型数据库中读取所述量化数据和所述表征修复结果的数据，并将所述量化数据和所述表征修复结果的数据通过可视化的方式进行展示。
请参阅图5，本申请还提供一种运维数据的处理装置，所述装置包括处理器和存储器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，可以实现上述的运维数据的处理方法。
由上可见，本申请提供的技术方案，可以对目标服务器的多种运维数据进行自动分析，从而确定出目标服务器当前的运营质量。具体地，在获取到目标服务器的多种运维数据之后，可以将这些运维数据转换为各个维度的量化数据。该量化数据可以使得不同的运维数据采用统一的衡量标准，这样，后续可以直接对该量化数据进行分析，从而避免了由于运维数据的类型不同而导致的数据分析手段过于繁杂的问题，进而简化了数据分析的过程，提高了数据分析的效率。不同维度的量化数据可以体现目标服务器各个方面的性能。这样，根据对目标服务器各个方面的性能进行评估，从而可以确定出目标服务器当前的运营质量。由上可见，本申请提供的技术方案，能够简化数据分析的过程，同时针对不同维度的数据进行分析，可以精确地得出目标服务器当前的质量评估信息。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件来实现。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (12)

  1. 一种运维数据的处理方法，其特征在于，所述方法包括：
    获取目标服务器的多种运维数据；
    按照预设规则将所述运维数据转换为各个维度的量化数据；
    根据所述各个维度的量化数据，确定所述目标服务器的质量评估信息。
  2. 根据权利要求1所述的方法，其特征在于，获取目标服务器的多种运维数据包括：
    读取具备指定数据类型和指定格式的预设指令，并将所述预设指令转换为与当前待获取的运维数据相匹配的数据查询语句，并通过转换得到的所述数据查询语句获取对应的运维数据。
  3. 根据权利要求1所述的方法，其特征在于，按照预设规则将所述运维数据转换为各个维度的量化数据包括：
    将所述运维数据输入预设机器学习模型，以通过所述预设机器学习模型输出各个维度的评判参数，并将所述各个维度的评判参数作为所述各个维度的量化数据。
  4. 根据权利要求1所述的方法，其特征在于，按照预设规则将所述运维数据转换为各个维度的量化数据包括：
    读取所述运维数据中包含的原始运维参数，并识别所述原始运维参数关联的品质层级；
    确定由所述预设规则限定的用于表征所述品质层级的标准参数，并将所述原始运维参数替换为所述标准参数；
    将参数替换后的运维数据作为按照所述预设规则处理后的量化数据。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    获取与当前维度相关联的修复触发条件，若所述当前维度的量化数据符合所述修复触发条件，将所述当前维度作为待修复的目标维度。
  6. 根据权利要求5所述的方法，其特征在于，在将所述当前维度作为待修复的目标维度之后，所述方法还包括：
    获取与所述目标维度相匹配的修复规则，并按照所述修复规则对所述目标服务器进行修复。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    将所述量化数据存储于时序型数据库中，并将表征修复结果的数据存储于关系型数据库中。
  8. 根据权利要求7所述的方法，其特征在于，在按照所述修复规则对所述目标服务器进行修复之后，所述方法还包括：
    分别从所述时序型数据库中和所述关系型数据库中读取所述量化数据和所述表征修复结果的数据，并将所述量化数据和所述表征修复结果的数据通过可视化的方式进行展示。
  9. 根据权利要求6所述的方法，其特征在于，按照所述修复规则对所述目标服务器进行修复至少包括以下一种：
    若所述目标维度表征所述目标服务器的接口不可用，重启不可用的接口服务；
    若所述目标维度表征所述目标服务器中任务的堆积数量超过指定阈值，将所述目标服务器中未处理的堆积任务切换至备用服务器集群。
  10. 根据权利要求1所述的方法，其特征在于，所述多种运维数据至少包括所述目标服务器中软件内部产生的业务数据、所述目标服务器中监控系统产生的监控数据、所述目标服务器的日志数据以及用于表征所述目标服务器性能的指标数据。
  11. 一种运维数据的处理系统，其特征在于，所述系统用于实现如权利要求1至10中任一所述的方法。
  12. 一种运维数据的处理装置，其特征在于，所述装置包括存储器和处理器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现如权利要求1至10中任一所述的方法。
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