WO2020087830A1 - 数据分析方法、装置、服务器及存储介质 - Google Patents

数据分析方法、装置、服务器及存储介质 Download PDF

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Publication number
WO2020087830A1
WO2020087830A1 PCT/CN2019/077518 CN2019077518W WO2020087830A1 WO 2020087830 A1 WO2020087830 A1 WO 2020087830A1 CN 2019077518 W CN2019077518 W CN 2019077518W WO 2020087830 A1 WO2020087830 A1 WO 2020087830A1
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monitoring
category
type
time series
series data
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PCT/CN2019/077518
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English (en)
French (fr)
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王亚杰
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深圳壹账通智能科技有限公司
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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
    • G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
    • G06F11/324—Display of status information
    • G06F11/327—Alarm or error message display
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters

Definitions

  • This application relates to the field of computer technology, and in particular, to a data analysis method, device, server, and storage medium.
  • the monitoring platform in the industry only makes threshold judgments based on the monitoring data, and does not carry out deep analysis and judgment on the alarm data, and many useful data have not been tapped.
  • the monitoring platform is generally only applicable to a certain field, and there is no set of monitoring platforms that are fully applicable to all operating environments. As a result, the operation and maintenance team needs to deploy and apply multiple sets of monitoring platforms. Therefore, the monitoring data is scattered, which is inconvenient for the centralized analysis and processing of later data.
  • the monitoring platform cannot analyze and judge the monitoring trend, cannot find the problem in advance, and can notify the alarm in advance.
  • the monitoring data is not summarized, and operation and maintenance reports are regularly generated. Even if an operation and maintenance report is generated, the report has the disadvantages of not being user-friendly, only capable of realizing alarm data, and failing to summarize and analyze the monitoring data.
  • a data analysis method includes:
  • Acquiring time series data corresponding to at least one monitoring object wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator;
  • the monitoring parameters include at least one of the monitoring object, monitoring category, and type indicators;
  • One or more time series data included in the input monitoring parameters are selected from the tree model, and a corresponding analysis report is generated according to the time series data.
  • a data analysis device includes:
  • An obtaining module configured to obtain time series data corresponding to at least one monitoring object, wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator;
  • the building module is used to build a tree model according to the monitoring object, monitoring category and type index;
  • a receiving module configured to receive input monitoring parameters, wherein the monitoring parameters include at least one of the monitoring object, monitoring category, and type index;
  • the generating module is configured to select one or more time series data included in the input monitoring parameter from the tree model, and generate a corresponding analysis report according to the time series data.
  • a server includes a processor and a memory, and the processor is configured to implement the following steps when executing at least one computer-readable instruction stored in the memory:
  • Acquiring time series data corresponding to at least one monitoring object wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator;
  • the monitoring parameters include at least one of the monitoring object, monitoring category, and type indicators;
  • One or more time series data included in the input monitoring parameters are selected from the tree model, and a corresponding analysis report is generated according to the time series data.
  • a non-volatile readable storage medium stores at least one computer-readable instruction, and when the at least one computer-readable instruction is executed by a processor, the following steps are implemented:
  • Acquiring time series data corresponding to at least one monitoring object wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator;
  • the monitoring parameters include at least one of the monitoring object, monitoring category, and type indicators;
  • One or more time series data included in the input monitoring parameters are selected from the tree model, and a corresponding analysis report is generated according to the time series data.
  • this application provides a data analysis method, device, server and storage medium, which can collect the collected data, such as system resource utilization rate (CPU monitoring, memory monitoring, disk monitoring and database performance, etc.) and business data (User login volume, user registration volume and core transaction data), conduct a unified overall quantitative analysis. Perform data trend analysis according to each monitoring item, summarize the trend analysis, and then conduct an overall report analysis based on the summary analysis report.
  • system resource utilization rate CPU monitoring, memory monitoring, disk monitoring and database performance, etc.
  • business data User login volume, user registration volume and core transaction data
  • FIG. 1 is a flowchart of a first preferred embodiment of the data analysis method of the present application.
  • FIG. 2 is a functional block diagram of the first preferred embodiment of the data analysis device of the present application.
  • FIG. 3 is a schematic structural diagram of a preferred embodiment of a server in at least one example of this application.
  • FIG. 1 it is a flowchart of a first preferred embodiment of the data analysis method of the present application. According to different requirements, the execution order in the flowchart may be changed, and some steps may be omitted.
  • Step S01 Obtain time series data corresponding to at least one monitoring object, where the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator.
  • the server may obtain time series data corresponding to several monitoring objects, where the monitoring objects may include several monitoring categories, that is, each monitoring object may include one or more monitoring categories.
  • the parameter information of the corresponding type index output by the monitoring category can be obtained. Understandably, the time series data of the monitoring object may be parameter information of corresponding type indicators output by the monitoring category at different time points.
  • the monitoring objects may include system resource objects and / or business type objects.
  • the server may obtain time series data of the monitored object in real time or periodically.
  • the server when used as a monitoring target of system resources, the monitoring target may include hardware, CPU, memory, hard disk and other hardware monitoring categories, and may also include a running database and other software monitoring categories.
  • the monitoring type is CPU, it can output the utilization rate (the percentage of time that the processor executes the non-idle thread), the interrupt rate (the number of times the device interrupts the processor per second-when the task is completed or needs attention, the device will send an interrupt signal To the processor), system call rate (the overall rate at which the processor calls the operating system service routine) and other types of parameter information.
  • the monitoring category When the monitoring category is memory, it can output the parameter information such as page fault rate (Page Fault-indicating that the processor requests an error on a page from the specified location in memory).
  • the monitoring category When the monitoring category is a hard disk, it can output parameter information such as the average number of read and write requests (the hard disk is queued in the instance interval).
  • the monitoring category When the monitoring category is a database, it can output parameters such as data read and write performance. information.
  • the business type when the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data.
  • monitoring categories such as user login volume, user registration volume, and core transaction data.
  • parameter information such as the number of users online can be output.
  • the monitoring category when the monitoring category is the user registration volume, parameter information such as the number of registered accounts can be output.
  • the monitoring category is core transaction data, the output can be output. Parameter information for orders, clicks and other types of indicators.
  • the monitored object has attribute information
  • the attribute information may include but is not limited to location information.
  • the monitoring object is the location information of the server
  • the server can obtain the corresponding attribute information at the same time when acquiring the time series data of the monitoring object, or the server stores one or more attribute information, and when acquiring the time series data of the monitoring object , You can get the corresponding attribute information from the stored attribute information.
  • the CPU utilization rate of server 001 in East China is 80.02%, where East China can represent the attribute information of server 001.
  • the business type when the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data.
  • the monitoring category When the monitoring category is the user login volume, it can output parameter information such as the number of users online and other types of indicators.
  • the monitoring category When the monitoring category is the user registration volume, it can output parameter information such as the number of registered accounts.
  • the monitoring type When the monitoring type is the core transaction data, it can output orders, Click on the parameter information for indicators such as ads.
  • the time series data X may be expressed as parameter information ⁇ of the type indicator corresponding to the monitoring category at time t.
  • the CPU utilization rate of server 001 in East China is 80.02%
  • the time information is 21:24:34 on September 3, 2017, and the type indicator is The utilization rate
  • the parameter information of the type index is 80.02%.
  • the server can obtain time series data through various ways, and can store it locally.
  • the time series data can be stored in the relational database by default, that is, the time t and the type index ⁇ in the time series data are stored in the relational database as key-value pairs.
  • the relational database may be an RRD Tool database that is simply stored based on files, an opentsdb database built on the K / V database, and a mysql and postgresql database built on the relational database.
  • the time series data database may include the "search engine” Elasticsearch, Crate.io, Solr database built based on Lucene, Vertica, Actian and other databases based on the columnar storage database.
  • Step S02 Construct a tree model according to the monitoring object, monitoring category and type index.
  • the monitoring object, monitoring category, and type indicators may be monitoring item parameters, so as to complete the corresponding analysis report through the output monitoring item parameters.
  • the tree model may include a root node, one or more leaf nodes, and one or more internal nodes.
  • the monitoring objects (such as system resource objects and business type objects) can be connected to the root node;
  • the monitoring category can be connected to the corresponding monitoring objects to be the child nodes of the monitoring object (such as user login volume, user registration volume, core Transaction data are used as child nodes of business type objects;
  • CPU, memory, hard disk, and database can be used as child nodes of system resource objects respectively; type indicators can be used as child nodes of corresponding monitoring categories (such as utilization rate, interruption rate, system call)
  • the rate is used as a child node of the monitoring category CPU;
  • the page miss rate is used as a child node of the monitoring category memory;
  • the number of users online is the child node of the monitoring type user login volume), and each parameter information can be used as a leaf node of the tree model To the child node of the corresponding type index.
  • the internal nodes of the tree model may include monitoring target nodes,
  • the step of constructing the tree model according to the monitoring object, monitoring category and type index includes:
  • the monitoring object is connected to the root node of the tree model
  • the monitoring category is connected to the corresponding monitoring object as a child node of the monitoring object
  • the type indicator is connected to the corresponding monitoring category as a child node of the monitoring category
  • Each parameter information corresponding to the type index is connected to the type index as a leaf node of the tree model, thereby completing the construction of the tree model.
  • Step S03 Receive input monitoring parameters, where the monitoring parameters include at least one of the monitoring object, monitoring category, and type index.
  • step S04 one or more time series data included in the input monitoring parameters are selected from the tree model, and a corresponding analysis report is generated according to the time series data.
  • a leaf node corresponding to the monitoring parameter is searched from the tree model according to the input monitoring parameter, and the leaf node corresponds to time series data corresponding to the monitoring parameter.
  • the server may generate a summary report or an overall report or other reports according to the input of at least one input monitoring parameter in the monitoring object, monitoring category, and type indicators.
  • a corresponding first summary report is generated according to the type indicator;
  • the input monitoring parameter is a monitoring category, according to the monitoring category and the type corresponding to the monitoring category
  • the first summary report of the indicator generates a corresponding second summary report;
  • the input monitoring parameter is a monitoring object, according to the second summary report of the monitoring object and the monitoring category corresponding to the monitoring object and the monitoring category
  • the first summary report of the type indicator generates the corresponding overall report.
  • the monitoring type of the monitoring object may send the corresponding time series data to the server every preset time, or feed back the corresponding time series data when requested by the server.
  • the server may generate a trend graph corresponding to the monitored object according to the time series data to generate a summary report and / or an overall report, so as to intuitively understand the status of the monitored object.
  • the server obtains time series data of the corresponding type indicator and generates a corresponding trend analysis graph.
  • the server can use each time t n in the time series data as the horizontal axis of the trend graph Point, and the parameter information ⁇ n of the corresponding type indicator is used as the value on the vertical axis of the trend graph, and then, the parameter information of the corresponding type indicator is connected by a straight line or a smooth curve, so that the corresponding monitoring category can be generated Summary report of the first type of trend graph.
  • the input parameter is a non-type index (such as the input parameter is the monitoring object or monitoring category);
  • the server judges the number of type index sub-nodes included in the corresponding monitoring category, and when the monitoring category includes a type index sub-node, corresponding to the type index sub-node Draw a trend graph for the time series data; when the monitoring category includes multiple types of index sub-nodes, merge according to the time series data corresponding to the type index sub-nodes, and then draw a trend graph based on the combined time series data.
  • the server may use each time t n in the time series data as a trend graph The point on the horizontal axis of the graph, and the parameter information of the corresponding type index ⁇ n as the value on the vertical axis of the trend graph, and then, the parameter information of the corresponding type index is connected by a straight line or a smooth curve, so, you can Generate a summary report of the second type corresponding to the trend graph of the monitoring category;
  • the server When the number of type index child nodes included in the corresponding monitoring category is greater than 1 (when the monitoring category is CPU, it has three types of indicators: utilization, interruption rate, and system call rate), which means that the monitoring type has at least two child nodes At this time, the server will merge the type index sub-nodes connected to the monitoring type node.
  • the server can draw a trend graph according to time series data.
  • the server may acquire the indicator units of various types of indicators in the monitoring category and determine whether the indicator units are the same.
  • the time value in the time series data corresponding to the type index is the horizontal axis, and the parameter information in the time series data is used as the vertical axis to establish a coordinate system.
  • Draw the trend graph by connecting the parameter information in the time series data corresponding to the type index through lines;
  • the time value in the time series data corresponding to the type index is the horizontal axis, and in the time series data corresponding to different types of indicators with different index units
  • the parameter information of is to establish a coordinate system for the vertical axis, and in the coordinate system, the parameter information in the time series data corresponding to the same type of indicator is connected by a line to draw the trend graph.
  • each time t n, m is taken as a point on the horizontal axis of the trend graph, and
  • the parameter information of the corresponding type indicator ⁇ n, m is used as the value on the vertical axis of the trend graph, and the parameter information of the same type indicator can be connected by a straight line or a smooth curve, so, relative to the utilization rate, interruption rate and system call
  • the trend graph of the three types of indicators can be used as a summary report of the second type.
  • the The server may generate a combined graph, the combined graph may have a vertical axis for two indicator units, each time t n, m is taken as a point on the horizontal axis of the trend graph, and the corresponding type index parameter information ⁇ n, m As the value on the vertical axis of the trend chart, and the parameter information of the same type of indicator can be connected by a straight line or a smooth curve to generate a combined chart, in this way, a summary report of the second type can be generated.
  • the indicator unit of the type indicator included in the monitoring category is greater than two, multiple trend graphs can be generated.
  • the server may analyze the state corresponding to the monitoring category connected to the monitoring object node, and generate the state corresponding to the monitoring type in the overall report.
  • the status of the monitoring category can be analyzed by reference factors such as the mean value, whether it is within a preset range, or not greater than a preset value.
  • the server can determine the sequence of time-series data ( ⁇ n, t n) of the n-v preset range, the preset value comparison, and ⁇ n is not within the preset range, or greater than the preset value, the output corresponding to t n in the report, or to ⁇ n is not within the preset range, or greater than the preset value corresponding to t n by Trend graph.
  • the first aspect of the present application provides a data analysis method.
  • the method includes: acquiring time series data corresponding to at least one monitoring object, where the monitoring object includes at least one monitoring category, and the monitoring category includes At least one type indicator; construct a tree model based on the monitoring object, monitoring category, and type indicator; receive input monitoring parameters, wherein the monitoring parameter includes at least one of the monitoring object, monitoring category, and type indicator; and from all One or more time series data included in the input monitoring parameter is selected in the tree model, and a corresponding analysis report is generated according to the time series data.
  • the collected data such as system resource utilization rate (CPU monitoring, memory monitoring, disk monitoring and database performance, etc.) and business data (user login volume, user registration volume, and core transaction data) can be unified and quantitatively analyzed.
  • Perform data trend analysis according to each monitoring item summarize the trend analysis, and then conduct an overall report analysis based on the summary analysis report.
  • FIG. 2 is a functional block diagram of the first preferred embodiment of the data analysis device of the present application.
  • the data analysis device 20 runs on a server.
  • the data analysis device 20 may include a plurality of functional modules composed of program code segments.
  • the program codes of each program segment in the data analysis device 20 may be stored in a memory and executed by at least one processor to perform the data analysis function.
  • the data analysis device 20 can be divided into multiple functional modules according to the functions it performs.
  • the functional module may include: an obtaining module 201, a building module 202, a receiving module 203, and a generating module 204.
  • the module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can perform fixed functions, and are stored in a memory. In some embodiments, the function of each module will be detailed in subsequent embodiments.
  • the obtaining module 201 is configured to obtain time series data corresponding to at least one monitoring object, wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator.
  • the server may obtain time series data corresponding to several monitoring objects, where the monitoring objects may include several monitoring categories, that is, each monitoring object may include one or more monitoring categories.
  • the parameter information of the corresponding type index output by the monitoring category can be obtained. Understandably, the time series data of the monitoring object may be parameter information of corresponding type indicators output by the monitoring category at different time points.
  • the monitoring objects may include system resource objects and / or business type objects.
  • the server may obtain time series data of the monitored object in real time or periodically.
  • the server when used as a monitoring target of system resources, the monitoring target may include hardware, CPU, memory, hard disk and other hardware monitoring categories, and may also include a running database and other software monitoring categories.
  • the monitoring type is CPU, it can output the utilization rate (the percentage of time that the processor executes the non-idle thread), the interrupt rate (the number of times the device interrupts the processor per second-when the task is completed or needs attention, the device will send an interrupt signal To the processor), system call rate (the overall rate at which the processor calls the operating system service routine) and other types of parameter information.
  • the monitoring category When the monitoring category is memory, it can output the parameter information such as page fault rate (Page Fault-indicating that the processor requests an error on a page from the specified location in memory).
  • the monitoring category When the monitoring category is a hard disk, it can output parameter information such as the average number of read and write requests (the hard disk is queued in the instance interval).
  • the monitoring category When the monitoring category is a database, it can output parameters such as data read and write performance. information.
  • the business type when the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data.
  • monitoring categories such as user login volume, user registration volume, and core transaction data.
  • parameter information such as the number of users online can be output.
  • the monitoring category when the monitoring category is the user registration volume, parameter information such as the number of registered accounts can be output.
  • the monitoring category is core transaction data, the output can be output. Parameter information for orders, clicks and other types of indicators.
  • the monitored object has attribute information
  • the attribute information may include but is not limited to location information.
  • the monitoring object is the location information of the server
  • the server can obtain the corresponding attribute information at the same time when acquiring the time series data of the monitoring object, or the server stores one or more attribute information, and when acquiring the time series data of the monitoring object , You can get the corresponding attribute information from the stored attribute information.
  • the CPU utilization rate of server 001 in East China is 80.02%, where East China can represent the attribute information of server 001.
  • the business type when the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data.
  • the monitoring category When the monitoring category is the user login volume, it can output parameter information such as the number of users online and other types of indicators.
  • the monitoring category When the monitoring category is the user registration volume, it can output parameter information such as the number of registered accounts.
  • the monitoring type When the monitoring type is the core transaction data, it can output orders, Click on the parameter information for indicators such as ads.
  • the time series data X may be expressed as parameter information ⁇ of the type indicator corresponding to the monitoring category at time t.
  • the CPU utilization rate of server 001 in East China is 80.02%
  • the time information is 21:24:34 on September 3, 2017, and the type indicator is The utilization rate
  • the parameter information of the type index is 80.02%.
  • the server can obtain time series data through various ways, and can store it locally.
  • the time series data can be stored in the relational database by default, that is, the time t and the type index ⁇ in the time series data are stored in the relational database as key-value pairs.
  • the relational database may be an RRD Tool database that is simply stored based on files, an opentsdb database built on the K / V database, and a mysql and postgresql database built on the relational database.
  • the time series data database may include the "search engine” Elasticsearch, Crate.io, Solr database built based on Lucene, Vertica, Actian and other databases based on the columnar storage database.
  • the building module 202 is used to build a tree model according to monitoring objects, monitoring categories and type indicators.
  • the monitoring object, monitoring category, and type indicators may be monitoring item parameters, so as to complete the corresponding analysis report through the output monitoring item parameters.
  • the tree model may include a root node, one or more leaf nodes, and one or more internal nodes.
  • the monitoring objects (such as system resource objects and business type objects) can be connected to the root node;
  • the monitoring category can be connected to the corresponding monitoring objects to be the child nodes of the monitoring object (such as user login volume, user registration volume, core Transaction data are used as child nodes of business type objects;
  • CPU, memory, hard disk, and database can be used as child nodes of system resource objects respectively; type indicators can be used as child nodes of corresponding monitoring categories (such as utilization rate, interruption rate, system call)
  • the rate is used as a child node of the monitoring category CPU;
  • the page missing rate is used as a child node of the monitoring category memory;
  • the number of users online is a child node of the monitoring type user login volume), and each parameter information can be used as a leaf node of the tree model and connected To the child node of the corresponding type index.
  • the internal nodes of the tree model may include monitoring target
  • the step of constructing the tree model according to the monitoring object, monitoring category and type index includes:
  • the monitoring object is connected to the root node of the tree model
  • the monitoring category is connected to the corresponding monitoring object as a child node of the monitoring object
  • the type indicator is connected to the corresponding monitoring category as a child node of the monitoring category
  • Each parameter information corresponding to the type index is connected to the type index as a leaf node of the tree model, thereby completing the construction of the tree model.
  • the receiving module 203 is configured to receive input monitoring parameters, where the monitoring parameters include at least one of the monitoring object, monitoring category, and type index.
  • the generating module 204 is configured to select one or more time series data included in the input monitoring parameter from the tree model, and generate a corresponding analysis report according to the time series data.
  • a leaf node corresponding to the monitoring parameter is searched from the tree model according to the input monitoring parameter, and the leaf node corresponds to time series data corresponding to the monitoring parameter.
  • the server may generate a summary report or an overall report or other reports according to the input of at least one input monitoring parameter in the monitoring object, monitoring category, and type indicators.
  • a corresponding first summary report is generated according to the type indicator;
  • the input monitoring parameter is a monitoring category, according to the monitoring category and the type corresponding to the monitoring category
  • the first summary report of the indicator generates a corresponding second summary report;
  • the input monitoring parameter is a monitoring object, according to the second summary report of the monitoring object and the monitoring category corresponding to the monitoring object and the monitoring category
  • the first summary report of the type indicator generates the corresponding overall report.
  • the monitoring type of the monitoring object may send the corresponding time series data to the server every preset time, or feed back the corresponding time series data when requested by the server.
  • the server may generate a trend graph corresponding to the monitored object according to the time series data to generate a summary report and / or an overall report, so as to intuitively understand the status of the monitored object.
  • the server obtains time series data of the corresponding type indicator and generates a corresponding trend analysis graph.
  • the server can use each time t n in the time series data as the horizontal axis of the trend graph Point, and the parameter information ⁇ n of the corresponding type indicator is used as the value on the vertical axis of the trend graph, and then, the parameter information of the corresponding type indicator is connected by a straight line or a smooth curve, so that the corresponding monitoring category can be generated Summary report of the first type of trend graph.
  • the input parameter is a non-type index (such as the input parameter is the monitoring object or monitoring category);
  • the server judges the number of type index sub-nodes included in the corresponding monitoring category.
  • the monitoring category includes a type index sub-node
  • the corresponding Draw a trend graph for the time series data; when the monitoring category includes multiple types of index sub-nodes, merge according to the time series data corresponding to the type index sub-nodes, and then draw a trend graph based on the combined time series data.
  • the server may use each time t n in the time series data as a trend graph The point on the horizontal axis of the graph, and the parameter information of the corresponding type index ⁇ n as the value on the vertical axis of the trend graph, and then, the parameter information of the corresponding type index is connected by a straight line or a smooth curve, so, you can Generate a summary report of the second type corresponding to the trend graph of the monitoring category;
  • the server When the number of type index child nodes included in the corresponding monitoring category is greater than 1 (when the monitoring category is CPU, it has three types of indicators: utilization, interruption rate, and system call rate), which means that the monitoring type has at least two child nodes At this time, the server will merge the type index sub-nodes connected to the monitoring type node.
  • the server can draw a trend graph according to time series data.
  • the server may acquire the indicator units of various types of indicators in the monitoring category and determine whether the indicator units are the same.
  • the time value in the time series data corresponding to the type index is the horizontal axis, and the parameter information in the time series data is used as the vertical axis to establish a coordinate system.
  • Draw the trend graph by connecting the parameter information in the time series data corresponding to the type index through lines;
  • the time value in the time series data corresponding to the type index is the horizontal axis, and in the time series data corresponding to different types of indicators with different index units
  • the parameter information of is to establish a coordinate system for the vertical axis, and in the coordinate system, the parameter information in the time series data corresponding to the same type of indicator is connected by a line to draw the trend graph.
  • each time t n, m is taken as a point on the horizontal axis of the trend graph, and
  • the parameter information of the corresponding type indicator ⁇ n, m is used as the value on the vertical axis of the trend graph, and the parameter information of the same type indicator can be connected by a straight line or a smooth curve, so, relative to the utilization rate, interruption rate and system call
  • the trend graph of the three types of indicators can be used as a summary report of the second type.
  • the The server may generate a combined graph, the combined graph may have a vertical axis for two indicator units, each time t n, m is taken as a point on the horizontal axis of the trend graph, and the corresponding type index parameter information ⁇ n, m As the value on the vertical axis of the trend chart, and the parameter information of the same type of indicator can be connected by a straight line or a smooth curve to generate a combined chart, in this way, a summary report of the second type can be generated.
  • the indicator unit of the type indicator included in the monitoring category is greater than two, multiple trend graphs can be generated.
  • the server may analyze the state corresponding to the monitoring category connected to the monitoring object node, and generate an overall report of the state corresponding to the monitoring type.
  • the status of the monitoring category can be analyzed by reference factors such as the mean value, whether it is within a preset range, or not greater than a preset value.
  • the status of the monitoring category can be analyzed by reference factors such as the mean value, whether it is within a preset range, or not greater than a preset value.
  • the server can determine the sequence of time-series data ( ⁇ n, t n) of the n-v preset range, the preset value comparison, and ⁇ n is not within the preset range, or greater than the preset value, the output corresponding to t n in the report, or to ⁇ n is not within the preset range, or greater than the preset value corresponding to t n by Trend graph.
  • the data analysis device 20 includes an acquisition module 201, a construction module 202, a reception module 203, and a generation module 204.
  • the acquiring module 201 is used to acquire time series data corresponding to at least one monitoring object, wherein the monitoring object includes at least one monitoring category, and the monitoring category includes at least one type indicator;
  • the construction module 202 is configured to monitor the object , Monitoring category and type indicators to build a tree model;
  • the receiving module 203 is used to receive input monitoring parameters, wherein the monitoring parameters include at least one of the monitoring object, monitoring category and type indicators;
  • the generation The module 204 is configured to select one or more time series data included in the input monitoring parameter from the tree model, and generate a corresponding analysis report according to the time series data.
  • the collected data such as system resource utilization rate (CPU monitoring, memory monitoring, disk monitoring and database performance, etc.), business data (user logins, user registrations, and core transaction data) can be unified and quantitatively analyzed based on For each monitoring item, perform data trend analysis, summarize the trend analysis, template the summary, and generate an analysis report. Based on the summary analysis report, the overall report analysis is carried out.
  • system resource utilization rate CPU monitoring, memory monitoring, disk monitoring and database performance, etc.
  • business data user logins, user registrations, and core transaction data
  • the above integrated unit implemented in the form of a software function module may be stored in a non-volatile readable storage medium.
  • the above software function module is stored in a storage medium, and includes several instructions to enable a computer device (which may be a personal computer, a dual-screen device, or a network device, etc.) or a processor (processor) to execute the various embodiments of the application Part of the method.
  • FIG. 3 is a schematic structural diagram of a preferred embodiment of a server in at least one example of this application.
  • the server 3 includes a memory 31, at least one processor 32, computer-readable instructions 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.
  • the computer-readable instructions 33 may be divided into one or more modules / units, the one or more modules / units are stored in the memory 31, and are controlled by the at least one processor 32 Execute to complete this application.
  • the one or more modules / units may be a series of computer-readable instruction instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions 33 in the server 3.
  • the server 3 is a device that can automatically perform numerical calculation and / or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (application license, Specific Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.
  • a microprocessor an application specific integrated circuit (application license, Specific Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.
  • ASIC Application license, Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Signal Processor
  • embedded devices etc.
  • the schematic diagram 3 is only an example of the server 3, and does not constitute a limitation on the server 3, and may include more or less components than the illustration, or a combination of certain components, or different components.
  • the server 3 may further include input and output devices
  • the at least one processor 32 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC ), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the processor 32 may be a microprocessor or the processor 32 may also be any conventional processor, etc.
  • the processor 32 is the control center of the server 3, and uses various interfaces and lines to connect the entire server 3 The various parts.
  • the memory 31 may be used to store the computer-readable instructions 33 and / or modules / units, and the processor 32 executes or executes the computer-readable instructions and / or modules / units stored in the memory 31, and Recall the data stored in the memory 31 to realize various functions of the server 3.
  • the memory 31 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required by at least one function (such as a sound playback function, an image playback function, etc.), etc .; the storage data area may Data (such as audio data, phone book, etc.) created according to the use of the server 3 is stored.
  • the memory 31 may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart) Media, Card (SMC), and a secure digital (SD) Card, flash memory card (Flash), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart) Media, Card (SMC), and a secure digital (SD) Card, flash memory card (Flash), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • Program code is stored in the memory 31, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions.
  • the various modules (acquisition module 201, construction module 202, reception module 203, and generation module 204) described in FIG. 2 are program codes stored in the memory 31 and executed by the at least one processor 32 In order to achieve the function of each module to achieve the purpose of data analysis.
  • the module / unit integrated in the server 3 may be stored in a non-volatile readable storage medium.
  • the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through computer-readable instructions.
  • the computer-readable instructions can be stored in a non-volatile In reading the storage medium, when the computer-readable instructions are executed by the processor, the steps of the foregoing method embodiments may be implemented.
  • the computer readable instructions include computer readable instruction codes, and the computer readable instruction codes may be in source code form, object code form, executable file, or some intermediate form, etc.
  • the non-volatile readable medium may include: any entity or device capable of carrying the computer-readable instruction code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals
  • telecommunications signals and software distribution media.
  • the content contained in the non-volatile readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions. For example, in some jurisdictions, according to legislation and patent practice, non- Volatile readable media does not include electrical carrier signals and telecommunication signals.
  • the server 3 may further include a power supply (such as a battery) that supplies power to various components.
  • the power supply may be logically connected to the at least one processor 32 through a power management system to implement management through the power management system Charging, discharging, and power management functions.
  • the power supply may also include any component such as one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, and a power status indicator.
  • the server 3 may also include a Bluetooth module, a Wi-Fi module, etc., which will not be repeated here.
  • the functional units in the embodiments of the present application may be integrated in the same processing unit, or each unit may exist alone physically, or two or more units may be integrated in the same unit.
  • the above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software function modules.

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Abstract

一种数据分析方法，所述方法包括获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标（S01）；根据监控对象、监控类别及类型指标构建树形模型（S02）；接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种（S03）；从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告（S04）。还提供一种数据分析装置、服务器及存储介质。通过上述方法能够有效地分析监控数据并将所述监控数据通过趋势图进行数据展示，方便用户查看。

Description

数据分析方法、装置、服务器及存储介质
本申请要求于2018年11月02日提交中国专利局，申请号为201811302678.X申请名称为“数据分析方法、装置、服务器及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及计算机技术领域，具体涉及一种数据分析方法、装置、服务器及存储介质。
背景技术
目前，业内的监控平台，只是根据监控数据进行阀值判断，没有对告警数据进行跟深层次的分析判断，很多有用的数据没有被挖掘到。并且所述监控平台一般只适用于某个领域，没有一套完整适用于所有运行环境的监控平台。造成运维团队需要部署和适用多套监控平台，因此，造成监控数据的分散，对后期数据集中分析处理造成不便。
所述监控平台无法对监控趋势进行分析判断，无法做到问题提前发现，提前告警通知。没有对监控数据进行总结，定期生成运维报告。即使生成了运维报告，所述报告也存在不够人性化，仅能够实现告警数据，无法实现对监控数据进行总结分析的缺点。
申请内容
鉴于以上内容，有必要提出一种数据分析方法、装置、服务器及存储介质，能够有效地分析监控数据。
一种数据分析方法，所述方法包括：
获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
根据所述监控对象、监控类别及类型指标构建树形模型；
接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
一种数据分析装置，所述装置包括：
获取模块，用于获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
构建模块，用于根据所述监控对象、监控类别及类型指标构建树形模型；
接收模块，用于接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
生成模块，用于从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
一种服务器，所述服务器包括处理器和存储器，所述处理器用于执行存储器中存储的至少一个计算机可读指令时实现以下步骤：
获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
根据所述监控对象、监控类别及类型指标构建树形模型；
接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
一种非易失性可读存储介质，所述非易失性可读存储介质存储有至少一个计算机可读指令，所述至少一个计算机可读指令被处理器执行时实现以下步骤：
获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
根据所述监控对象、监控类别及类型指标构建树形模型；
接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
由以上技术方案可知，本申请提供一种数据分析方法、装置、服务器及存储介质，可以将采集的数据，如系统资源使用率(CPU监控、内存监控、磁盘监控和数据库性能等)和业务数据(用户登陆量、用户注册量和核心交易数据)，进行统一整体量化分析。根据各监控项分别进行数据趋势分析，将趋势分析进行小结，再基于小结分析报告的基础上进行总体的报告分析。
附图说明
图1是本申请数据分析方法的第一较佳实施例的流程图。
图2是本申请数据分析装置的第一较佳实施例的功能模块图。
图3是本申请至少一个实例中服务器的较佳实施例的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”和“第三”等是用于区别不同对象，而非用于描述特定顺序。此外，术语“包括”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元 的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。
如图1所示，是本申请数据分析方法的第一较佳实施例的流程图。根据不同的需求，所述流程图中的执行顺序可以改变，某些步骤可以省略。
步骤S01，获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标。
所述服务器可获取对应若干监控对象的时间序列数据，其中，监控对象可包括若干监控类别，即每一监控对象可包括一个或多个监控类别。当对监控对象进行监控时，可获取监控类别输出的对应类型指标的参数信息。可以理解地，监控对象的时间序列数据可为不同时间点上监控类别输出的对应类型指标的参数信息。
较佳地，所述监控对象可包括系统资源对象及/或业务类型对象。所述服务器可实时或周期性获取监控对象的时间序列数据。
例如，当服务器作为系统资源的监控对象时，所述的监控对象可包括CPU、内存、硬盘等硬件类的监控类别，还可包括运行的数据库等软件类的监控类别。监控类别为CPU时，可输出利用率(处理器执行非闲置线程时间的百分比)、中断率(每秒钟设备中断处理器的次数--在完成一个任务或需要注意时，装置会发出中断讯号给处理器)、系统调用率(处理器调用操作系统服务例行程序的综合速率)等类型指标的参数信息。
监控类别为内存时可输出页缺失率(Page Fault-表示处理器向内存指定的位置请求一页出现错误)等类型指标的参数信息。监控类别为硬盘时，可输出读取和写入请求的平均数(为硬盘在实例间隔中列队)等类型指标的参数信息，监控类别为数据库时，可输出数据读写性能等类型指标的参数信息。
另外，当业务类型作为业务类的监控对象时，其可包括用户登陆量、用户注册量、核心交易数据等监控类别。例如，监控类别为用户登陆量时可输出用户在线数量等类型指标的参数信息，监控类别为用户注册量时可输出注册账号数量等类型指标的参数信息，监控类别为核心交易数据时可输出下订单、点击广告等类型指标的参数信息。
本实施方式中，所述监控对象具有属性信息，所述属性信息可包括但不限于位置信息。例如，监控对象为服务器的具有位置信息，所述服务器获取监控对象的时间序列数据时可同时获取对应的属性信息，或是服务器存储有一个或多个属性信息，当获取监控对象的时间序列数据时，可从其存储的属性信息中获取对应的属性信息。比如，在2017年9月3日21点24分34秒，华东区的服务器001的CPU利用率是80.02％，其中华东区即可表示服务器001的属性信息。
另外，当业务类型作为业务类的监控对象时，其可包括用户登陆量、用户注册量、核心交易数据等监控类别。监控类别为用户登陆量时可输出用户在线数量等类型指标的参数信息，监控类别为用户注册量时可输出注册账号 数量等类型指标的参数信息，监控类别为核心交易数据时可输出下订单、点击广告等类型指标的参数信息。
可以理解地，所述时间序列数据X可表示为监控类别在t时刻所对应的类型指标的参数信息ν。比如，在2017年9月3日21点24分34秒，华东区的服务器001的CPU利用率是80.02％，其中，时刻信息为2017年9月3日21点24分34秒，类型指标为利用率，类型指标的参数信息为80.02％。
可以理解地，所述时间序列数据X可表示为监控类别在t时刻所对应的类型指标的参数信息ν。因而，对于监控类别包含一个类型指标时，其对应的监控类别的时间序列数据X可表示为{X＝(ν 1,t 1),(ν 2,t 2),…,(ν n,t n)}，其中为自然数，(ν n,t n)表示序列对，t n＞t n-1，即序列对(ν n,t n)为最新的序列对；对于监控类别包含两个或两个以上的类型指标时，其监控类别的时间序列数据X可表示为{X＝X 1,X 2,…,X m}，其中，X m可表示为{X m＝(ν 1m,t 1),(ν 2m,t 2),…,(ν nm,t n)}，其中m表示类型指标的数量，n为自然数。
本实施方式中，所述服务器可通过多种途径获取时间序列数据，并可将其进行本地化存储。
在一实施方式中，时间序列数据可默认存在关系型数据库中，即将时间序列数据中时刻t时刻及类型指标ν作为键值对存储于关系型数据库。其中，关系型数据库可以是直接基于文件的简单存储的RRD Tool数据库，基于K/V数据库构建的opentsdb数据库，基于关系型数据库构建mysql、postgresql数据库。
在其他实施方式中，当对数据存储要较高或数据量比较大(如需要的图表有变更，需要从上报的源头重新来一遍。而且要等新数据过来之后，才能查看这些新数据)时，可以使用时间序列数据存储于时间序列数据类数据库中，以提升数据读写效率和减少数据占用存储空间。其中，时间序列数据类数据库可包括基于Lucene构建的“搜索引擎”Elasticsearch、Crate.io、Solr数据库，基于列式存储数据库的Vertica、Actian等数据库。
步骤S02，根据监控对象、监控类别及类型指标构建树形模型。
本实施方式中，监控对象、监控类别及类型指标可为监控项参数，以通过输出的监控项参数完成对应的分析报告。
可以理解地，树形模型可包括根节点、一个或多个叶节点、及一个或多个内部节点。较佳地，监控对象(如系统资源对象、业务类型对象)可连接于根节点；监控类别可连接于对应的监控对象，以作为监控对象的子节点(如用户登陆量、用户注册量、核心交易数据分别作为业务类型对象的子节点；CPU、内存、硬盘、数据库可分别作为系统资源对象的子节点)；类型指标则可作为对应监控类别的子节点(如利用率、中断率、系统调用率分别作为监控类别CPU的子节点；页缺失率作为监控类别内存的子节点；用户在线数量作为监控类型用户登陆量的子节点)，每一参数信息可作为树形模型的叶节点，并连接至对应类型指标的子节点。可以理解地，树形模型的内部节点 可包括监控对象节点、监控类别节点及类型指标节点。
具体地，所述根据监控对象、监控类别及类型指标构建树形模型的步骤包括：
a)所述监控对象连接于所述树形模型的根节点；
b)所述监控类别连接于对应的监控对象，以作为所述监控对象的子节点；
c)所述类型指标连接于对应的监控类别，以作为所述监控类别的子节点；
d)所述类型指标对应的每一参数信息连接于所述类型指标，以作为所述树形模型的叶节点，由此完成所述树形模型的构建。
步骤S03，接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种。
步骤S04，从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
本实施方式中，根据输入的监控参数从所述树形模型中查找对应于所述监控参数的叶节点，所述叶节点对应的就是所述监控参数对应的时间序列数据。
优选地，所述服务器可根据输入的接收所述监控对象、监控类别及类型指标中至少一输入监控参数生成小结报告或总体报告或其他报告。具体而言，当输入的监控参数是类型指标时，根据所述类型指标生成对应的第一小结报告；当输入的监控参数是监控类别时，根据所述监控类别和所述监控类别对应的类型指标的第一小结报告生成对应的第二小结报告；当输入的监控参数是监控对象时，根据所述监控对象和所述监控对象对应的监控类别的第二小结报告和所述监控类别对应的类型指标的第一小结报告生成对应的总体报告。
本实施方式中，监控对象的监控类型可每隔预设时间发送对应的时间序列数据至所述服务器，或是基于所述服务器的请求时反馈对应的时间序列数据。
所述服务器可根据时间序列数据生成对应监控对象的趋势图，以生成小结报告及/或总体报告，进而达到直观地对了解监控对象的状态的目的。
1)当输入的参数为类型指标时，所述服务器获取对应类型指标的时间序列数据，并生成对应的趋势分析图。
较佳地，当输入参数是类型指标时，如对应监控类别为内存的页缺失率的类型指标；或对应监控类别为用户登陆量的用户在线数量的类型指标，其对应的时间序列数据可表示为{X＝(ν 1,t 1),(ν 2,t2),…,(ν n,t n)}，所述服务器可将时间序列数据中各时刻t n作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n作为趋势图中纵轴上的值，之后，再将对应的类型指标的参数信息通过直线或平滑的曲线连接，如此，即可生成对应监控类别趋势图的第一类型的小结报告。
2)当输入的参数为非类型指标(如输入的参数为监控对象或监控类别)时；
2.1)当输入的参数为监控类别时，所述服务器判断对应的监控类别所包含的类型指标子节点的数量，当所述监控类别包含一个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据绘制趋势图；当所述监控类别包含多个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据进行合并，再根据合并后的时间序列数据绘制趋势图。
具体地，当对应的监控类别所包含的类型指标子节点的数量为1时，即表示监控类别具有唯一的子节点，此时，所述服务器可将时间序列数据中各时刻t n作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n作为趋势图中纵轴上的值，之后，再将对应的类型指标的参数信息通过直线或平滑的曲线连接，如此，即可生成对应监控类别趋势图的第二类型的小结报告；
当对应的监控类别所包含的类型指标子节点的数量大于1时(监控类别为CPU时，其具有利用率、中断率及系统调用率三个类型指标)，即表示监控类型具有至少两个子节点，此时，所述服务器将连接监控类型节点的类型指标子节点进行合并操作。
可以理解地，当监控类别具有两个或多个类型指标时，对应的时间序列数据可表示为{X＝X 1,X 2,…,X m}，其中，X m可表示为{X m＝(ν 1m,t 1),(ν 2m,t2),…,(ν nm,t n)}。
例如，监控类别为CPU时，其具有利用率、中断率及系统调用率三个类型指标，此时，所述服务器合并操作后的时间序列数据可表示为可X＝{X 1,X 2,X 3}，其中X 1对应利用率的类型指标，其序列对可表示为{X 1＝(ν 1,1,t 1),(ν 2,1,t 2),…,(ν n,1,t n)},X 2对应中断率的类型指标，其序列对可表示为{X 2＝(ν 1,2,t 1),(ν 2,2,t 2),…,(ν n,2,t n)}，X 3对应系统调用率的类型指标，其序列对可表示为{X 3＝(ν 1,3,t 1),(ν 2,3,t 2),…,(ν n,3,t n)}。
较佳地，所述服务器可根据时间序列数据绘制趋势图。在绘制趋势图之前，所述服务器可获取监控类别的各类型指标的指标单位，并判断各指标单位是否相同。
当所述类型指标的指标单位相同时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以所述时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将所述类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图；
当所述类型指标的指标单位中存在不相同的指标单位时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以具有不同指标单位的不同类型指标对应的时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将相同类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图。
具体地，当监控类别包含的类型指标的指标单位相同(如利用率、中断率及系统调用率的指标单位为百分比)时，各时刻t n,m作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n,m作为趋势图中纵轴上的值，且可将相同类型指标的参数信息通过直线或平滑的曲线连接，如此，相对于利用率、中 断率及系统调用率三类型指标的趋势图可作为第二类型的小结报告。
当监控类别包含的类型指标的指标单位中存在不相同的指标单位(如利用率、中断率及系统调用率的指标单位为百分比，而其他类型指标的指标单位可能为次/秒)时，所述服务器可生成组合图，所述组合图可具有针对两种指标单位的纵轴，各时刻t n,m作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n,m作为趋势图中纵轴上的值，且可将相同类型指标的参数信息通过直线或平滑的曲线连接，以生成组合图，如此，即可生成第二类型的小结报告。当监控类别包含的类型指标的指标单位大于两种时，可生成多个趋势图。
2.2)当输入的参数为监控对象时，所述服务器可对连接监控对象节点的监控类别所对应的状态进行分析，并将监控类型所对应的状态生成在总体报告。
在一实施方式中，监控类别的状态可通过均值、是否位于预设范围内、或不大于预设值等参考因素进行分析。
可以理解地，当参考因素是均值时，所述服务器可将监控类别所对应的类型指标的时间序列数据进行算术平均，如对于{X＝(ν 1,t 1),(ν 2,t 2),…,(ν n,t n)}，所对应的均值为：
Figure PCTCN2019077518-appb-000001
当参考因素为是否位于预设范围内、或不大于预设值时，所述服务器可判断时间序列数据中序列对(ν n,t n)中ν n与预设范围内、预设值进行比较，并在ν n不在预设范围内、或大于预设值时，在报告中输出对应的t n，或是将ν n不在预设范围内、或大于预设值所对应的t n通过趋势图进行表示。
综上所述，本申请第一方面提供一种数据分析方法，所述方法包括：获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；根据监控对象、监控类别及类型指标构建树形模型；接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。从而可以将采集的数据，如系统资源使用率(CPU监控、内存监控、磁盘监控和数据库性能等)和业务数据(用户登陆量、用户注册量和核心交易数据)，进行统一整体量化分析。根据各监控项分别进行数据趋势分析，将趋势分析进行小结，再基于小结分析报告的基础上进行总体的报告分析。
以上所述，仅是本申请的具体实施方式，但本申请的保护范围并不局限于此，对于本领域的普通技术人员来说，在不脱离本申请创造构思的前提下，还可以做出改进，但这些均属于本申请的保护范围。
下面结合图2至图3，分别对实现上述数据分析方法的服务器功能模块及硬件结构进行介绍。
图2为本申请数据分析装置第一较佳实施例的功能模块图。
在一些实施例中，所述数据分析装置20运行于服务器中。所述数据分析装置20可以包括多个由程序代码段所组成的功能模块。所述数据分析装置20中的各个程序段的程序代码可以存储于存储器中，并由至少一个处理器所执行，以执行所述的数据分析功能。
本实施例中，所述数据分析装置20根据其所执行的功能，可以被划分为多个功能模块。所述功能模块可以包括：获取模块201、构建模块202、接收模块203及生成模块204。本申请所称的模块是指一种能够被至少一个处理器所执行并且能够完成固定功能的一系列计算机可读指令段，其存储在存储器中。在一些实施例中，关于各模块的功能将在后续的实施例中详述。
获取模块201，用于获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标。
所述服务器可获取对应若干监控对象的时间序列数据，其中，监控对象可包括若干监控类别，即每一监控对象可包括一个或多个监控类别。当对监控对象进行监控时，可获取监控类别输出的对应类型指标的参数信息。可以理解地，监控对象的时间序列数据可为不同时间点上监控类别输出的对应类型指标的参数信息。
较佳地，所述监控对象可包括系统资源对象及/或业务类型对象。所述服务器可实时或周期性获取监控对象的时间序列数据。
例如，当服务器作为系统资源的监控对象时，所述的监控对象可包括CPU、内存、硬盘等硬件类的监控类别，还可包括运行的数据库等软件类的监控类别。监控类别为CPU时，可输出利用率(处理器执行非闲置线程时间的百分比)、中断率(每秒钟设备中断处理器的次数--在完成一个任务或需要注意时，装置会发出中断讯号给处理器)、系统调用率(处理器调用操作系统服务例行程序的综合速率)等类型指标的参数信息。
监控类别为内存时可输出页缺失率(Page Fault-表示处理器向内存指定的位置请求一页出现错误)等类型指标的参数信息。监控类别为硬盘时，可输出读取和写入请求的平均数(为硬盘在实例间隔中列队)等类型指标的参数信息，监控类别为数据库时，可输出数据读写性能等类型指标的参数信息。
另外，当业务类型作为业务类的监控对象时，其可包括用户登陆量、用户注册量、核心交易数据等监控类别。例如，监控类别为用户登陆量时可输出用户在线数量等类型指标的参数信息，监控类别为用户注册量时可输出注册账号数量等类型指标的参数信息，监控类别为核心交易数据时可输出下订单、点击广告等类型指标的参数信息。
本实施方式中，所述监控对象具有属性信息，所述属性信息可包括但不限于位置信息。例如，监控对象为服务器的具有位置信息，所述服务器获取监控对象的时间序列数据时可同时获取对应的属性信息，或是服务器存储有一个或多个属性信息，当获取监控对象的时间序列数据时，可从其存储的属性信息中获取对应的属性信息。比如，在2017年9月3日21点24分34秒，华东区的服务器001的CPU利用率是80.02％，其中华东区即可表示服务器 001的属性信息。
另外，当业务类型作为业务类的监控对象时，其可包括用户登陆量、用户注册量、核心交易数据等监控类别。监控类别为用户登陆量时可输出用户在线数量等类型指标的参数信息，监控类别为用户注册量时可输出注册账号数量等类型指标的参数信息，监控类别为核心交易数据时可输出下订单、点击广告等类型指标的参数信息。
可以理解地，所述时间序列数据X可表示为监控类别在t时刻所对应的类型指标的参数信息ν。比如，在2017年9月3日21点24分34秒，华东区的服务器001的CPU利用率是80.02％，其中，时刻信息为2017年9月3日21点24分34秒，类型指标为利用率，类型指标的参数信息为80.02％。
可以理解地，所述时间序列数据X可表示为监控类别在t时刻所对应的类型指标的参数信息ν。因而，对于监控类别包含一个类型指标时，其对应的监控类别的时间序列数据X可表示为{X＝(ν 1,t 1),(ν 2,t 2),…,(ν n,t n)}，其中为自然数，(ν n,t n)表示序列对，t n＞t n-1，即序列对(ν n,t n)为最新的序列对；对于监控类别包含两个或两个以上的类型指标时，其监控类别的时间序列数据X可表示为{X＝X 1,X 2,…,X m}，其中，X m可表示为{X m＝(ν 1m,t 1),(ν 2m,t 2),…,(ν nm,t n)}，其中m表示类型指标的数量，n为自然数。
本实施方式中，所述服务器可通过多种途径获取时间序列数据，并可将其进行本地化存储。
在一实施方式中，时间序列数据可默认存在关系型数据库中，即将时间序列数据中时刻t时刻及类型指标ν作为键值对存储于关系型数据库。其中，关系型数据库可以是直接基于文件的简单存储的RRD Tool数据库，基于K/V数据库构建的opentsdb数据库，基于关系型数据库构建mysql、postgresql数据库。
在其他实施方式中，当对数据存储要较高或数据量比较大(如需要的图表有变更，需要从上报的源头重新来一遍。而且要等新数据过来之后，才能查看这些新数据)时，可以使用时间序列数据存储于时间序列数据类数据库中，以提升数据读写效率和减少数据占用存储空间。其中，时间序列数据类数据库可包括基于Lucene构建的“搜索引擎”Elasticsearch、Crate.io、Solr数据库，基于列式存储数据库的Vertica、Actian等数据库。
构建模块202用于根据监控对象、监控类别及类型指标构建树形模型。
本实施方式中，监控对象、监控类别及类型指标可为监控项参数，以通过输出的监控项参数完成对应的分析报告。
可以理解地，树形模型可包括根节点、一个或多个叶节点、及一个或多个内部节点。较佳地，监控对象(如系统资源对象、业务类型对象)可连接于根节点；监控类别可连接于对应的监控对象，以作为监控对象的子节点(如用户登陆量、用户注册量、核心交易数据分别作为业务类型对象的子节点；CPU、内存、硬盘、数据库可分别作为系统资源对象的子节点)；类型指标 则可作为对应监控类别的子节点(如利用率、中断率、系统调用率分别作为监控类别CPU的子节点；页缺失率作为监控类别内存的子节点；用户在线数量作为监控类型用户登陆量的子节点)，每一参数信息可作为树形模型的叶节点，并连接至对应类型指标的子节点。可以理解地，树形模型的内部节点可包括监控对象节点、监控类别节点及类型指标节点。
具体地，所述根据监控对象、监控类别及类型指标构建树形模型的步骤包括：
a)所述监控对象连接于所述树形模型的根节点；
b)所述监控类别连接于对应的监控对象，以作为所述监控对象的子节点；
c)所述类型指标连接于对应的监控类别，以作为所述监控类别的子节点；
d)所述类型指标对应的每一参数信息连接于所述类型指标，以作为所述树形模型的叶节点，由此完成所述树形模型的构建。
所述接收模块203用于接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种。
所述生成模块204用于从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
本实施方式中，根据输入的监控参数从所述树形模型中查找对应于所述监控参数的叶节点，所述叶节点对应的就是所述监控参数对应的时间序列数据。
优选地，所述服务器可根据输入的接收所述监控对象、监控类别及类型指标中至少一输入监控参数生成小结报告或总体报告或其他报告。具体而言，当输入的监控参数是类型指标时，根据所述类型指标生成对应的第一小结报告；当输入的监控参数是监控类别时，根据所述监控类别和所述监控类别对应的类型指标的第一小结报告生成对应的第二小结报告；当输入的监控参数是监控对象时，根据所述监控对象和所述监控对象对应的监控类别的第二小结报告和所述监控类别对应的类型指标的第一小结报告生成对应的总体报告。
本实施方式中，监控对象的监控类型可每隔预设时间发送对应的时间序列数据至所述服务器，或是基于所述服务器的请求时反馈对应的时间序列数据。
所述服务器可根据时间序列数据生成对应监控对象的趋势图，以生成小结报告及/或总体报告，进而达到直观地对了解监控对象的状态的目的。
1)当输入的参数为类型指标时，所述服务器获取对应类型指标的时间序列数据，并生成对应的趋势分析图。
较佳地，当输入参数是类型指标时，如对应监控类别为内存的页缺失率的类型指标；或对应监控类别为用户登陆量的用户在线数量的类型指标，其对应的时间序列数据可表示为{X＝(ν 1,t 1),(ν 2,t2),…,(ν n,t n)}，所述服务器可将时间序列数据中各时刻t n作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n作为趋势图中纵轴上的值，之后，再将对应的类型指标的参数信息通过直 线或平滑的曲线连接，如此，即可生成对应监控类别趋势图的第一类型的小结报告。
2)当输入的参数为非类型指标(如输入的参数为监控对象或监控类别)时；
2.1)当输入的参数为监控类别时，所述服务器判断对应的监控类别所包含的类型指标子节点的数量，当所述监控类别包含一个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据绘制趋势图；当所述监控类别包含多个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据进行合并，再根据合并后的时间序列数据绘制趋势图。
具体地，当对应的监控类别所包含的类型指标子节点的数量为1时，即表示监控类别具有唯一的子节点，此时，所述服务器可将时间序列数据中各时刻t n作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n作为趋势图中纵轴上的值，之后，再将对应的类型指标的参数信息通过直线或平滑的曲线连接，如此，即可生成对应监控类别趋势图的第二类型的小结报告；
当对应的监控类别所包含的类型指标子节点的数量大于1时(监控类别为CPU时，其具有利用率、中断率及系统调用率三个类型指标)，即表示监控类型具有至少两个子节点，此时，所述服务器将连接监控类型节点的类型指标子节点进行合并操作。
可以理解地，当监控类别具有两个或多个类型指标时，对应的时间序列数据可表示为{X＝X 1,X 2,…,X m}，其中，X m可表示为{X m＝(ν 1m,t 1),(ν 2m,t2),…,(ν nm,t n)}。
例如，监控类别为CPU时，其具有利用率、中断率及系统调用率三个类型指标，此时，所述服务器合并操作后的时间序列数据可表示为可X＝{X 1,X 2,X 3}，其中X 1对应利用率的类型指标，其序列对可表示为{X 1＝(ν 1,1,t 1),(ν 2,1,t 2),…,(ν n,1,t n)},X 2对应中断率的类型指标，其序列对可表示为{X 2＝(ν 1,2,t 1),(ν 2,2,t 2),…,(ν n,2,t n)}，X 3对应系统调用率的类型指标，其序列对可表示为{X 3＝(ν 1,3,t 1),(ν 2,3,t 2),…,(ν n,3,t n)}。
较佳地，所述服务器可根据时间序列数据绘制趋势图。在绘制趋势图之前，所述服务器可获取监控类别的各类型指标的指标单位，并判断各指标单位是否相同。
当所述类型指标的指标单位相同时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以所述时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将所述类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图；
当所述类型指标的指标单位中存在不相同的指标单位时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以具有不同指标单位的不同类型指标对应的时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将相同类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图。
具体地，当监控类别包含的类型指标的指标单位相同(如利用率、中断率及系统调用率的指标单位为百分比)时，各时刻t n,m作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n,m作为趋势图中纵轴上的值，且可将相同类型指标的参数信息通过直线或平滑的曲线连接，如此，相对于利用率、中断率及系统调用率三类型指标的趋势图可作为第二类型的小结报告。
当监控类别包含的类型指标的指标单位中存在不相同的指标单位(如利用率、中断率及系统调用率的指标单位为百分比，而其他类型指标的指标单位可能为次/秒)时，所述服务器可生成组合图，所述组合图可具有针对两种指标单位的纵轴，各时刻t n,m作为趋势图的横轴上的点，而对应的类型指标的参数信息ν n,m作为趋势图中纵轴上的值，且可将相同类型指标的参数信息通过直线或平滑的曲线连接，以生成组合图，如此，即可生成第二类型的小结报告。当监控类别包含的类型指标的指标单位大于两种时，可生成多个趋势图。
2.2)当输入的参数为监控对象时，所述服务器可对连接监控对象节点的监控类别所对应的状态进行分析，并将监控类型所对应的状态生成总体报告。
在一实施方式中，监控类别的状态可通过均值、是否位于预设范围内、或不大于预设值等参考因素进行分析。
在一实施方式中，监控类别的状态可通过均值、是否位于预设范围内、或不大于预设值等参考因素进行分析。
可以理解地，当参考因素是均值时，所述服务器可将监控类别所对应的类型指标的时间序列数据进行算术平均，如对于{X＝(ν 1,t 1),(ν 2,t 2),…,(ν n,t n)}，所对应的均值为：
Figure PCTCN2019077518-appb-000002
当参考因素为是否位于预设范围内、或不大于预设值时，所述服务器可判断时间序列数据中序列对(ν n,t n)中ν n与预设范围内、预设值进行比较，并在ν n不在预设范围内、或大于预设值时，在报告中输出对应的t n，或是将ν n不在预设范围内、或大于预设值所对应的t n通过趋势图进行表示。
综上所述，本申请提供的数据分析装置20，包括获取模块201、构建模块202、接收模块203和生成模块204。所述获取模块201用于获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；所述构建模块202用于根据监控对象、监控类别及类型指标构建树形模型；所述接收模块203用于接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及所述生成模块204用于从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。从而可以将采集的数据，如系统资源使用率(CPU监控、内存监控、磁盘监控和数据库性能等)，业务数据(用户登陆量、用户注册量和核心交易数据)，进行统一整体量化分析，根据各监控项，分别进行数据趋势分析，将趋势分析进行小结，对小结进行模板化，生成分析报告。再基于小结分析 报告的基础上，进行总体的报告分析。
上述以软件功能模块的形式实现的集成的单元，可以存储在一个非易失性可读取存储介质中。上述软件功能模块存储在一个存储介质中，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，双屏设备，或者网络设备等)或处理器(processor)执行本申请各个实施例所述方法的部分。
图3为本申请至少一个实例中服务器的较佳实施例的结构示意图。
所述服务器3包括：存储器31、至少一个处理器32、存储在所述存储器31中并可在所述至少一个处理器32上运行的计算机可读指令33及至少一条通讯总线34。
所述至少一个处理器32执行所述计算机可读指令33时实现上述数据分析方法实施例中的步骤。
示例性的，所述计算机可读指令33可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器31中，并由所述至少一个处理器32执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机可读指令指令段，所述指令段用于描述所述计算机可读指令33在所述服务器3中的执行过程。
所述服务器3是一种能够按照事先设定或存储的指令，自动进行数值计算和/或信息处理的设备，其硬件包括但不限于微处理器、专用集成电路(应用程序lication Specific Integrated Circuit，ASIC)、可编程门阵列(Field－Programmable Gate Array，FPGA)、数字处理器(Digital Signal Processor，DSP)、嵌入式设备等。本领域技术人员可以理解，所述示意图3仅仅是服务器3的示例，并不构成对服务器3的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述服务器3还可以包括输入输出设备、网络接入设备、总线等。
所述至少一个处理器32可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。所述处理器32可以是微处理器或者所述处理器32也可以是任何常规的处理器等，所述处理器32是所述服务器3的控制中心，利用各种接口和线路连接整个服务器3的各个部分。
所述存储器31可用于存储所述计算机可读指令33和/或模块/单元，所述处理器32通过运行或执行存储在所述存储器31内的计算机可读指令和/或模块/单元，以及调用存储在存储器31内的数据，实现所述服务器3的各种功能。所述存储器31可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据服务器3的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器31可以包括高速随机存取存 储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
所述存储器31中存储有程序代码，且所述至少一个处理器32可调用所述存储器31中存储的程序代码以执行相关的功能。例如，图2中所述的各个模块(获取模块201、构建模块202、接收模块203及生成模块204)是存储在所述存储器31中的程序代码，并由所述至少一个处理器32所执行，从而实现所述各个模块的功能以达到数据分析的目的。
所述服务器3集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个非易失性可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一非易失性可读存储介质中，所述计算机可读指令在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机可读指令包括计算机可读指令代码，所述计算机可读指令代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述非易失性可读介质可以包括：能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述非易失性可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，非易失性可读介质不包括电载波信号和电信信号。
尽管未示出，所述服务器3还可以包括给各个部件供电的电源(比如电池)，优选的，电源可以通过电源管理系统与所述至少一个处理器32逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。电源还可以包括一个或一个以上的直流或交流电源、再充电系统、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。所述服务器3还可以包括蓝牙模块、Wi-Fi模块等，在此不再赘述。
应所述了解，所述实施例仅为说明之用，在专利申请范围上并不受此结构的限制。
在本申请所提供的几个实施例中，应所述理解到，所揭露的电子设备和方法，可以通过其它的方式实现。例如，以上所描述的电子设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
另外，在本申请各个实施例中的各功能单元可以集成在相同处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在相同单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件 功能模块的形式实现。
对于本领域技术人员而言，显然本申请不限于上述示范性实施例的细节，而且在不背离本申请的精神或基本特征的情况下，能够以其他的具体形式实现本申请。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本申请的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。此外，显然“包括”一词不排除其他单元或，单数不排除复数。系统权利要求中陈述的多个单元或装置也可以由一个单元或装置通过软件或者硬件来实现。第一，第二等词语用来表示名称，而并不表示任何特定的顺序。
最后应说明的是，以上实施例仅用以说明本申请的技术方案而非限制，尽管参照较佳实施例对本申请进行了详细说明，本领域的普通技术人员应当理解，可以对本申请的技术方案进行修改或等同替换，而不脱离本申请技术方案的精神范围。

Claims (20)

  1. 一种数据分析方法，其特征在于，所述方法包括：
    获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
    根据所述监控对象、监控类别及类型指标构建树形模型；
    接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
    从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
  2. 如权利要求1所述的数据分析方法，其特征在于，所述监控对象的时间序列数据为不同时间点上监控类别输出的对应类型指标的参数信息。
  3. 如权利要求2所述的数据分析方法，其特征在于，所述根据所述监控对象、监控类别及类型指标构建树形模型包括：
    所述监控对象连接于所述树形模型的根节点；
    所述监控类别连接于对应的监控对象，以作为所述监控对象的子节点；
    所述类型指标连接于对应的监控类别，以作为所述监控类别的子节点；
    所述类型指标对应的每一参数信息连接于所述类型指标，以作为所述树形模型的叶节点，由此完成所述树形模型的构建。
  4. 如权利要求2所述的数据分析方法，其特征在于：
    当输入的监控参数是类型指标时，根据所述类型指标生成对应的第一小结报告；
    当输入的监控参数是监控类别时，根据所述监控类别和所述监控类别对应的类型指标的第一小结报告生成对应的第二小结报告；
    当输入的监控参数是监控对象时，根据所述监控对象和所述监控对象对应的监控类别的第二小结报告和所述监控类别对应的类型指标的第一小结报告生成对应的总体报告。
  5. 如权利要求2所述的数据分析方法，其特征在于，
    当所述输入的监控参数为监控对象时，根据所述树形模型中与所述监控对象节点连接的监控类别所对应的状态进行分析，并根据监控类型所对应的状态生成总体报告。
  6. 如权利要求4所述的数据分析方法，其特征在于：
    当所述输入的监控参数为监控类别时，判断所述监控类别所包含的类型指标子节点的数量；
    当所述监控类别包含一个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据绘制趋势图；
    当所述监控类别包含多个类型指标子节点时，根据所述类型指标子节点对 应的时间序列数据进行合并，再根据合并后的时间序列数据绘制趋势图。
  7. 如权利要求6所述的数据分析方法，其特征在于，所述方法还包括：
    在绘制所述趋势图之前，获取所述监控类别的类型指标的指标单位，并判断各指标单位是否相同；
    当所述类型指标的指标单位相同时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以所述时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将所述类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图；
    当所述类型指标的指标单位中存在不相同的指标单位时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以具有不同指标单位的不同类型指标对应的时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将相同类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图。
  8. 一种数据分析装置，其特征在于，所述装置包括：
    获取模块，用于获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
    构建模块，用于根据所述监控对象、监控类别及类型指标构建树形模型；
    接收模块，用于接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
    生成模块，用于从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
  9. 一种服务器，其特征在于，所述服务器包括处理器和存储器，所述处理器用于执行存储器中存储的至少一个计算机可读指令时实现以下步骤：
    获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至少一个监控类别，所述监控类别包括至少一个类型指标；
    根据所述监控对象、监控类别及类型指标构建树形模型；
    接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
    从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
  10. 如权利要求9所述的服务器，其特征在于，所述监控对象的时间序列数据为不同时间点上监控类别输出的对应类型指标的参数信息。
  11. 如权利要求10所述的服务器，其特征在于，在根据所述监控对象、监控类别及类型指标构建树形模型时，所述处理器执行所述至少一个计算机可读指令以实现以下步骤：
    所述根据所述监控对象、监控类别及类型指标构建树形模型包括：
    所述监控对象连接于所述树形模型的根节点；
    所述监控类别连接于对应的监控对象，以作为所述监控对象的子节点；
    所述类型指标连接于对应的监控类别，以作为所述监控类别的子节点；
    所述类型指标对应的每一参数信息连接于所述类型指标，以作为所述树形 模型的叶节点，由此完成所述树形模型的构建。
  12. 如权利要求10所述的服务器，其特征在于，所述处理器执行所述至少一个计算机可读指令还实现以下步骤：
    当输入的监控参数是类型指标时，根据所述类型指标生成对应的第一小结报告；
    当输入的监控参数是监控类别时，根据所述监控类别和所述监控类别对应的类型指标的第一小结报告生成对应的第二小结报告；
    当输入的监控参数是监控对象时，根据所述监控对象和所述监控对象对应的监控类别的第二小结报告和所述监控类别对应的类型指标的第一小结报告生成对应的总体报告。
  13. 如权利要求10所述的服务器，其特征在于，所述处理器执行所述至少一个计算机可读指令还实现以下步骤：
    当所述输入的监控参数为监控对象时，根据所述树形模型中与所述监控对象节点连接的监控类别所对应的状态进行分析，并根据监控类型所对应的状态生成总体报告。
  14. 如权利要求12所述的服务器，其特征在于，所述处理器执行所述至少一个计算机可读指令还实现以下步骤：
    当所述输入的监控参数为监控类别时，判断所述监控类别所包含的类型指标子节点的数量；
    当所述监控类别包含一个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据绘制趋势图；
    当所述监控类别包含多个类型指标子节点时，根据所述类型指标子节点对应的时间序列数据进行合并，再根据合并后的时间序列数据绘制趋势图。。
  15. 如权利要求14所述的服务器，其特征在于，所述处理器执行所述至少一个计算机可读指令还实现以下步骤：
    在绘制所述趋势图之前，获取所述监控类别的类型指标的指标单位，并判断各指标单位是否相同；
    当所述类型指标的指标单位相同时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以所述时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将所述类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图；
    当所述类型指标的指标单位中存在不相同的指标单位时，以所述类型指标对应的时间序列数据中的时刻值为横轴，以具有不同指标单位的不同类型指标对应的时间序列数据中的参数信息为纵轴建立坐标系，在所述坐标系中将相同类型指标对应的时间序列数据中的参数信息通过线条连接绘制所述趋势图。
  16. 一种非易失性可读存储介质，其特征在于，所述非易失性可读存储介质存储有至少一个计算机可读指令，所述至少一个计算机可读指令被处理器执行时实现以下步骤：
    获取对应至少一个监控对象的时间序列数据，其中，所述监控对象包括至 少一个监控类别，所述监控类别包括至少一个类型指标；
    根据所述监控对象、监控类别及类型指标构建树形模型；
    接收输入的监控参数，其中，所述监控参数包括所述监控对象、监控类别及类型指标中至少一种；及
    从所述树形模型中选择所述输入的监控参数包含的一个或多个时间序列数据，并根据所述时间序列数据生成对应的分析报告。
  17. 如权利要求16所述的存储介质，其特征在于，所述监控对象的时间序列数据为不同时间点上监控类别输出的对应类型指标的参数信息。
  18. 如权利要求17所述的存储介质，其特征在于，在根据所述监控对象、监控类别及类型指标构建树形模型时，所述至少一个计算机可读指令被处理器执行时以实现以下步骤：
    所述根据所述监控对象、监控类别及类型指标构建树形模型包括：
    所述监控对象连接于所述树形模型的根节点；
    所述监控类别连接于对应的监控对象，以作为所述监控对象的子节点；
    所述类型指标连接于对应的监控类别，以作为所述监控类别的子节点；
    所述类型指标对应的每一参数信息连接于所述类型指标，以作为所述树形模型的叶节点，由此完成所述树形模型的构建。
  19. 如权利要求17所述的存储介质，其特征在于，所述至少一个计算机可读指令被处理器执行时还实现以下步骤：
    当输入的监控参数是类型指标时，根据所述类型指标生成对应的第一小结报告；
    当输入的监控参数是监控类别时，根据所述监控类别和所述监控类别对应的类型指标的第一小结报告生成对应的第二小结报告；
    当输入的监控参数是监控对象时，根据所述监控对象和所述监控对象对应的监控类别的第二小结报告和所述监控类别对应的类型指标的第一小结报告生成对应的总体报告。
  20. 如权利要求17所述的存储介质，其特征在于，所述至少一个计算机可读指令被处理器执行时还实现以下步骤：
    当所述输入的监控参数为监控对象时，根据所述树形模型中与所述监控对象节点连接的监控类别所对应的状态进行分析，并根据监控类型所对应的状态生成总体报告。
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112700328A (zh) * 2021-01-11 2021-04-23 河南中原消费金融股份有限公司 一种指标自动分析方法、装置、设备及存储介质
CN112819313A (zh) * 2021-01-27 2021-05-18 上海哔哩哔哩科技有限公司 目标图像生成方法及装置
CN112862220A (zh) * 2021-03-23 2021-05-28 山东中创软件工程股份有限公司 一种设备生命周期预测方法、装置及相关设备
CN113407647A (zh) * 2021-06-18 2021-09-17 珠海美佳音科技有限公司 温湿度数据的采集方法、装置、设备及存储介质
CN113489954A (zh) * 2021-07-06 2021-10-08 北京市商汤科技开发有限公司 生产作业报告的生成方法、装置、电子设备及存储介质
CN113762717A (zh) * 2021-08-03 2021-12-07 国能国华(北京)电力研究院有限公司 设备运行状态监控方法、装置、电子设备、存储介质
CN116743180A (zh) * 2023-08-08 2023-09-12 东莞市欧派奇电子科技有限公司 一种储能电源数据智能存储方法
CN116755978A (zh) * 2023-08-21 2023-09-15 深圳惠科存储科技有限公司 内存运行状态评估方法、电子设备及可读存储介质
CN117389854A (zh) * 2023-11-13 2024-01-12 北京驭数华创科技有限公司 性能分析方法、装置、电子设备及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110246407A1 (en) * 2008-12-15 2011-10-06 Korea Instititute of Science & Technology Informat System and method for hybrid rete reasoning based on in-memory and dbms
CN102740319A (zh) * 2011-04-15 2012-10-17 中兴通讯股份有限公司 一种关键性能指标的监控方法及装置
CN107038512A (zh) * 2016-02-03 2017-08-11 中国电力科学研究院 一种指标体系建立方法
CN107741955A (zh) * 2017-09-15 2018-02-27 平安科技（深圳）有限公司 业务数据监控方法、装置、终端设备及存储介质

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9197511B2 (en) * 2012-10-12 2015-11-24 Adobe Systems Incorporated Anomaly detection in network-site metrics using predictive modeling
US9021447B2 (en) * 2013-02-12 2015-04-28 Concurix Corporation Application tracing by distributed objectives
CN107015872B (zh) * 2016-12-09 2018-03-16 上海壹账通金融科技有限公司 监控数据的处理方法及装置
CN107943668B (zh) * 2017-12-15 2019-02-26 江苏神威云数据科技有限公司 计算机服务器集群日志监控方法及监控平台

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110246407A1 (en) * 2008-12-15 2011-10-06 Korea Instititute of Science & Technology Informat System and method for hybrid rete reasoning based on in-memory and dbms
CN102740319A (zh) * 2011-04-15 2012-10-17 中兴通讯股份有限公司 一种关键性能指标的监控方法及装置
CN107038512A (zh) * 2016-02-03 2017-08-11 中国电力科学研究院 一种指标体系建立方法
CN107741955A (zh) * 2017-09-15 2018-02-27 平安科技（深圳）有限公司 业务数据监控方法、装置、终端设备及存储介质

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112700328A (zh) * 2021-01-11 2021-04-23 河南中原消费金融股份有限公司 一种指标自动分析方法、装置、设备及存储介质
CN112700328B (zh) * 2021-01-11 2024-04-16 河南中原消费金融股份有限公司 一种指标自动分析方法、装置、设备及存储介质
CN112819313A (zh) * 2021-01-27 2021-05-18 上海哔哩哔哩科技有限公司 目标图像生成方法及装置
CN112862220A (zh) * 2021-03-23 2021-05-28 山东中创软件工程股份有限公司 一种设备生命周期预测方法、装置及相关设备
CN113407647A (zh) * 2021-06-18 2021-09-17 珠海美佳音科技有限公司 温湿度数据的采集方法、装置、设备及存储介质
CN113489954A (zh) * 2021-07-06 2021-10-08 北京市商汤科技开发有限公司 生产作业报告的生成方法、装置、电子设备及存储介质
CN113762717A (zh) * 2021-08-03 2021-12-07 国能国华(北京)电力研究院有限公司 设备运行状态监控方法、装置、电子设备、存储介质
CN116743180A (zh) * 2023-08-08 2023-09-12 东莞市欧派奇电子科技有限公司 一种储能电源数据智能存储方法
CN116743180B (zh) * 2023-08-08 2024-02-13 东莞市欧派奇电子科技有限公司 一种储能电源数据智能存储方法
CN116755978A (zh) * 2023-08-21 2023-09-15 深圳惠科存储科技有限公司 内存运行状态评估方法、电子设备及可读存储介质
CN117389854A (zh) * 2023-11-13 2024-01-12 北京驭数华创科技有限公司 性能分析方法、装置、电子设备及介质

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