WO2012142821A1

WO2012142821A1 - Device and method for acquiring multi-dimensional statistical performance data in network management

Info

Publication number: WO2012142821A1
Application number: PCT/CN2011/080947
Authority: WO
Inventors: 杜贤俊; 李进; 文秀林; 周艳; 熊纪涛; 张国彩
Original assignee: 中兴通讯股份有限公司
Priority date: 2011-04-19
Filing date: 2011-10-18
Publication date: 2012-10-26
Also published as: CN102752122A; CN102752122B

Abstract

The present invention provides a device and method for acquiring multi-dimensional statistical performance data in network management. The device comprises: a dividing module, configured to perform two-dimensional division on a statistic dimension according to a multi-dimensional statistics requirement on performance data; a modeling module, configured to establish a two-dimensional coordinate statistical model of the performance data; a path setting module, configured to set a statistics execution path of the performance data on the two-dimensional coordinate statistical model; and an acquiring module, configured to trigger running of the statistics execution path according to a preset rule, so as to acquire a summary result corresponding to the multi-dimensional statistics requirement. By using the technical solution provided in the present invention, the complexity of multi-dimensional statistics on the performance data is reduced, and various user requirements are satisfied.

Description

TECHNICAL FIELD The present invention relates to the field of communications, and in particular to an apparatus and method for acquiring multi-dimensional statistical performance data in network management. BACKGROUND Performance management is one of several management functions in telecommunication network management. The purpose of performance management is to monitor and collect relevant performance statistics on networks, network elements or devices, and evaluate the effectiveness of networks and network elements. The status of the device, supporting network planning and network analysis. Statistical analysis of performance data is at the heart of performance management and is also difficult. For example, in the field of integrated network management performance management, there are various dimensions of statistical analysis of performance data in the industry, including time dimension, measurement object dimension, network element dimension, network dimension, area dimension, business dimension, user dimension, and so on. Data statistics requirements for different dimensions essentially reflect different user needs. However, multi-dimensional performance data statistics make the implementation of the integrated network management performance management system more complicated and cumbersome. In general, the industry develops a specific software system for user-specific data statistics requirements, and the system is difficult to meet multiple user needs at the same time, and its scalability is also poor. In view of the above problems in the related art, an effective solution has not yet been proposed. SUMMARY OF THE INVENTION A primary object of the present invention is to provide an apparatus and method for acquiring multi-dimensional statistical performance data in network management to solve at least the above problems. According to an aspect of the present invention, an apparatus for acquiring multi-dimensional statistical performance data in network management is provided, including: a dividing module, configured to perform two-dimensional division of a statistical dimension according to a multi-dimensional statistical requirement for performance data; , a two-dimensional coordinate statistical model set to establish performance data; a path setting module, configured to set a statistical execution path of performance data on the two-dimensional coordinate statistical model; and an acquisition module configured to trigger a statistical execution path operation according to a predetermined rule, Get summary results corresponding to multi-dimensional statistical requirements. The dividing module is configured to divide the statistical dimension into a time statistical dimension and a location statistical dimension, wherein the location statistical dimension includes at least one of the following: a measurement object dimension, a network element dimension, a network dimension, an area dimension, a service type dimension, and a user dimension. . The path setting module is configured to set a data summary manner for two adjacent statistical state nodes in each statistical execution path; and set different statistical execution paths for the two adjacent state nodes according to different statistical requirements. The acquiring module includes: a triggering unit, configured to trigger a running of the statistical execution path in real time according to the newly generated performance data, or trigger a running of the statistical execution path when the preset time period is reached; and the acquiring unit is set to run the statistical execution path, Get summary results corresponding to multi-dimensional statistical requirements. The path setting module is further configured to set a statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model, and/or to set a statistical execution path for the statistical execution path on the same dimension to cross the intermediate state node. According to another aspect of the present invention, a method for obtaining multi-dimensional statistical performance data in network management is provided, including: a dividing module performs two-dimensional division on a statistical dimension according to a multi-dimensional statistical requirement of performance data; The dimension of module division establishes a two-dimensional coordinate statistical model of performance data; on the two-dimensional coordinate statistical model, the path setting module sets a statistical execution path of performance data; the acquisition module triggers the execution of the statistical execution path according to a predetermined rule, and obtains multi-dimensional statistics. The summary result corresponding to the demand. The dividing module performs two-dimensional division of the statistical dimension according to the multi-dimensional statistical requirement of the performance data, including: the dividing module divides the statistical dimension into a time statistical dimension and a position statistical dimension, wherein the location statistical dimension includes at least one of the following: Object Dimensions, Network Dimensions, Network Dimensions, Regional Dimensions, Business Type Dimensions, User Dimensions. The path setting module sets the statistical execution path of the performance data, including: the path setting module sets a data summary manner for two adjacent statistical state nodes in each statistical execution path; and two non-adjacent two according to different statistical requirements The status node sets a different statistical execution path. The obtaining module triggers the running of the statistical execution path according to one of the following predetermined rules: triggering the running of the statistical execution path in real time according to the newly generated performance data; triggering the running of the statistical execution path when the preset time point is reached. The path setting module sets the statistical execution path of the performance data, and further includes at least one of the following steps: setting a statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model; and performing a statistical execution path on the same dimension across the middle The status node sets the statistical execution path. According to the present invention, according to the multi-dimensional statistical requirement of the performance data, the statistical dimension is divided into two dimensions, and the multi-dimensional performance data statistics in the related technology are solved, which makes the performance management implementation more complicated and cumbersome and difficult. Meeting the needs of multiple users and poor scalability, and thus achieving the statistical complexity of reducing multiple dimensions of performance data and meeting the needs of multiple users. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are set to illustrate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, In the drawings: FIG. 1 is a structural block diagram of an apparatus for acquiring multi-dimensional statistical performance data in network management according to an embodiment of the present invention; FIG. 2 is a structure of an apparatus for acquiring multi-dimensional statistical performance data in network management according to a preferred embodiment of the present invention; schematic diagram. 3 is a flowchart of a method for acquiring multi-dimensional statistical performance data in network management according to an embodiment of the present invention; FIG. 4 is a schematic flowchart of a method for acquiring multi-dimensional statistical performance data in network management according to an example 1 of the present invention; 2 is a schematic diagram of a two-dimensional coordinate statistical model of a performance counter of Example 1; FIG. 6 is a schematic diagram of a statistical execution path of a performance counter according to Example 1 of the present invention; FIG. 7 is a two-dimensional coordinate statistic of a performance counter "number of successful calls" according to Example 2 of the present invention. FIG. 8 is a schematic diagram of a statistical execution path of a performance counter "call success count" according to Example 2 of the present invention. BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. FIG. 1 is a structural block diagram of an apparatus for acquiring multi-dimensional statistical performance data in network management according to an embodiment of the present invention. As shown in FIG. 1 , the apparatus includes: a dividing module 10 configured to perform two-dimensional division of a statistical dimension according to a multi-dimensional statistical requirement for performance data; and in actual application, divide a statistical dimension of performance data into two dimensions , for example: time statistics dimension and location statistics dimension, where the second dimension is all of the statistical dimensions of the performance data except the first dimension The collective name for the dimension. Thus, since there are only two dimensions, the difficulty of multi-dimensional statistics for the performance data dimension is greatly simplified. Moreover, the same performance data may have one or more independent two-dimensional coordinate statistical models according to the statistical business requirements of the performance data. The modeling module 12 is connected to the dividing module 10 and configured to establish a two-dimensional coordinate statistical model of the performance data. In practical applications, the time statistical dimension can be used as the abscissa of the two-dimensional coordinate statistical model, and the position statistical dimension is used as the two-dimensional The ordinate of the coordinate statistical model; and the statistical state node corresponding to each performance data is set as the coordinate of the two-dimensional coordinate statistical model, wherein the origin in the coordinate is the original state node that does not perform statistics on the performance data. Wherein, the value of the performance attribute corresponds to the coordinate of each state node in the two-dimensional coordinate statistical model. Of course, the value of the performance attribute may directly indicate that each statistical state node is in the two-dimensional coordinate statistical model. coordinate of. Moreover, the original state node that does not perform statistics on the performance data corresponds to the original time dimension and the original location dimension. The path setting module 14 is connected to the modeling module 12 and configured to set a statistical execution path of the performance data on the two-dimensional coordinate statistical model; in actual application, the statistical execution path reflects the statistical state transition on the two-dimensional coordinate statistical model. . The statistical execution path represents the statistical business requirement corresponding to the performance counter. A plurality of statistical execution paths may be set on a two-dimensional coordinate statistical model; and each statistical execution path includes at least two statistical state nodes. The obtaining module 16 is connected to the path setting module 14 and configured to trigger the running of the statistical execution path according to a predetermined rule to obtain a summary result corresponding to the multi-dimensional statistical requirement. In a specific application, the running process of the foregoing statistical execution path may include: collecting original performance data of the performance data, and instantiating the two-dimensional coordinate statistical model; triggering the running of the statistical execution path according to the predetermined rule, and performing summary statistics on the performance data, A summary result corresponding to the multi-dimensional statistical requirement. In the foregoing embodiment, the statistical dimension of the performance data is divided into two dimensions, which reduces the statistical complexity of implementing multiple dimensions of the performance data, and can meet various user requirements without affecting or affecting the operational efficiency. In a specific implementation process, the dividing module 10 is configured to divide the statistical dimension into a time statistical dimension and a location statistical dimension, where the location statistical dimension is all the dimensions of the non-time statistical dimension in the multi-dimensional, for example, the location statistical dimension The method may include at least one of the following: a measurement object dimension, a network element dimension, a network dimension, an area dimension, a service type dimension, a user dimension, and the like; in a specific application, the path setting module 14 is configured to perform a path in each of the statistics. The two adjacent statistical state nodes set a data summary manner, wherein the data summary manner may be a means for data packet aggregation. Generally, it can be divided into statistical algorithms such as averaging, seeking maximum/small, and summation; and setting different statistical execution paths for two state nodes that are not adjacent according to different statistical requirements. In a specific implementation, the path setting module 14 may also be used to complete the following process: setting a statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model; performing a statistical execution path on the same dimension across the intermediate state node Set the statistical execution path. In the specific application process, as shown in FIG. 2, the obtaining module 16 may include: a triggering unit 162, configured to trigger a running of a statistical execution path in real time according to the newly generated performance data, so that real-time processing requirements for data can be realized; Or triggering the running of the statistical execution path when the preset time period is reached, so that the processing of the processing of the larger data amount can be realized; in summary, the above-mentioned processing procedure of the triggering unit 162 can meet different real-time requirements for the performance statistics. The obtaining unit 164 is configured to run the statistical execution path and obtain a summary result corresponding to the multi-dimensional statistical requirement. In a specific implementation process, the path setting module 14 is further configured to set a statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model, and/or a state execution node across the middle of the statistical execution path on the same dimension. Set the statistical execution path. FIG. 3 is a flowchart of a method for acquiring multi-dimensional statistical performance data in network management according to an embodiment of the present invention. As shown in FIG. 3, the method includes: Step S302: The dividing module performs two-dimensional division of the statistical dimension according to the multi-dimensional statistical requirement of the performance data. In actual application, the statistical dimension of the performance data is divided into two dimensions, for example: a time statistical dimension and a location statistical dimension, wherein the second dimension is all dimensions of the statistical dimension of the performance data except the first dimension. Collectively. Thus, since there are only two dimensions, the difficulty of multi-dimensional statistics for the performance data dimension is greatly simplified. Moreover, the same performance data may have one or more independent two-dimensional coordinate statistical models according to the statistical business requirements of the performance data. Step S304, the modeling module establishes a two-dimensional coordinate statistical model of the performance data according to the dimension divided by the dividing module. Step S306, on the two-dimensional coordinate statistical model, the path setting module sets a statistical execution path of the performance data. In practical applications, the statistical execution path reflects the statistical state transitions on the two-dimensional coordinate statistical model. The statistical execution path represents the statistical business requirement corresponding to the performance counter. A plurality of statistical execution paths may be set on a two-dimensional coordinate statistical model; and each statistical execution path includes at least two statistical state nodes. Step S308, the obtaining module triggers the running of the statistical execution path according to the predetermined rule, and obtains a summary result corresponding to the multi-dimensional statistical requirement. In a specific implementation process, the obtaining module 16 triggers the running of the statistical execution path according to a predetermined rule, including the following processing steps: (1) collecting original performance data of performance data, and instantiating a two-dimensional coordinate statistical model, that is, instantiating performance (2) triggering the operation of the statistical execution path according to a predetermined rule, and performing summary statistics on the performance data to obtain a summary result corresponding to the multi-dimensional statistical requirement. In a specific implementation process, the partitioning module may divide the statistical dimension according to the multi-dimensional statistical requirement for the performance data by using the following but not limited to the following manners: As described above, the dividing module may divide the statistical dimension into a time statistical dimension and a position statistics. The dimension, where the location statistics dimension includes at least one of the following: a measurement object dimension, a network element dimension, a network dimension, an area dimension, a business type dimension, and a user dimension. Wherein, the location statistic dimension is all dimensions of the non-time statistic dimension in the multi-dimensional. The time dimension includes: an original time dimension and an extended time dimension; the location dimension includes: an original location dimension and an extended location dimension. In the specific application process, the foregoing path setting module sets the statistical execution path of the performance data, and may include: setting a data summary manner for two adjacent statistical state nodes in each statistical execution path, where the data summary manner may be The means for summarizing data groups can be generally divided into statistical algorithms such as averaging, seeking maximum/small, summation, and setting different statistical execution paths for two state nodes that are not adjacent according to different statistical requirements. In a specific application, the acquiring module may trigger the running of the foregoing statistical execution path according to one of the following predetermined rules: triggering the running of the statistical execution path in real time according to the newly generated performance data, so that the real-time processing requirement of the data can be realized; The operation of the statistical execution path is triggered at a time point, so that timing processing for a large amount of data can be realized. In a preferred implementation process, the path setting module sets a statistical execution path of the performance data, and further includes at least one of the following steps: setting the statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model; The statistical execution path sets the statistical execution path across the intermediate state node. In order to better understand the above embodiments, the following detailed description will be made in conjunction with specific examples and related drawings. Since the minimum granularity of performance data is the performance counter, and the performance counter is the basis of all performance data, the multi-dimensional statistics of performance counters are taken as an example in the following examples. Example 1 This example proposes a method for realizing multi-dimensional statistics of performance counters in an integrated network management system. It is applicable to the performance management field in telecom network management. The method first divides the statistical dimension of the performance counters into two-dimensional divisions, and then builds them. The module module establishes a two-dimensional coordinate statistical model and the path setting module sets the statistical execution path and the like. Achieve a multi-dimensional performance data summary to meet different business statistics requirements. At the same time, the method can perform flexible statistical driving mode configuration according to the real-time requirement of the user, and meet the two real-time requirements of performance statistics and high-traffic statistics. The implementation method is divided into: dividing the statistical dimension of the performance counter by the dividing module, establishing a two-dimensional coordinate statistical model of the performance counter by the modeling module, setting the statistical execution path of the performance counter and the data summary mode setting of the path setting module, and obtaining the module setting performance. The counter statistical drive mode and the running performance counter execute the path by statistics to obtain summary results and the like. As shown in FIG. 4, the specific implementation process is as follows: Step S402: The dividing module divides the statistical dimension of the performance counter. The performance counters are divided into statistical dimensions according to statistical requirements. Divided into two categories of time and location. The time dimension is subdivided into an original time dimension RT and an extended time dimension (H, D, W, M, Y), and the position dimension is divided into an original position dimension RL and an extended position dimension (L1, L2, ... Ln). The specific division process may include the following steps:

(1), defining a performance counter original time dimension RT and a performance counter original position dimension RL; the original time dimension RT represents the finest time dimension dimension of the performance data original; the original location dimension RL represents the finest location dimension dimension of the performance data original For the performance counter data collected by the integrated network management, RT and RL are obtained from the performance data reported by each lower-level network management system. RT and RL are the most basic attributes of all performance counters.

(2) Defining the performance counter extended time dimension according to the performance data time dimension statistics requirement; for the performance data of the integrated network management, the extended time dimension of the performance counter can be generally determined as H, D, W, M, Y, wherein, H Indicates hourly (Hour) statistics, D for Day statistics, W for Week statistics, M for Month statistics, and Y for Year statistics.

(3) Define the performance counter extended position dimension according to the performance data location dimension statistics requirement; the extended position dimension of the performance counter can generally be defined as multiple levels, L1, L2, L..., Ln, and there is tolerance between each level Relationships, from L1 to Ln represent a hierarchy of positions from low to high. For the performance data of the integrated network management, the extended location dimension can be divided into measurement object, network element, professional network, region, service type, user and other extended location dimensions. Statistical dimensions other than the time dimension can be summarized as location dimensions. Step S404, the modeling module establishes a two-dimensional coordinate statistical model of the performance counter. According to the multi-dimensional statistical requirements of the counter, the two-dimensional coordinate statistical model of the performance counter is established. For details, refer to Figure 3. The principle of establishing the two-dimensional coordinate statistical model of the counter is as follows: The abscissa represents the time statistical dimension, and the ordinate represents the position statistical dimension. Each point in Figure 5 has its coordinates (X, Y), which represents a statistical state of the performance counter. The origin (RT, RL) indicates the state in which the performance counter is the most primitive, and the other nodes are in the statistical state. At the same time, the origin (RT, RL) is the necessary initial state, and the rest are optional according to statistical requirements. The scale of the abscissa indicates from left to right that the granularity of the time dimension is small to large, and in principle, it can only be summarized from small to large. From left to right, it is minutes, hours, months, and years. The scale of the ordinate from bottom to top indicates that the granularity of the position dimension is small to large, and in principle, it can only be summarized from small to large. For example, from bottom to top, network elements, network types, and so on. The scale (dimension) source in the abscissa and ordinate is determined by the business statistics needs of the performance counter. If the statistical requirement corresponding to the counter does not have a statistical requirement for the weekly (W) time dimension of the performance counter, there is no weekly (W) related node in the graph. The same counter can have one to many two-dimensional coordinate statistical models. And multiple two-dimensional coordinate statistical models are independent and have no relationship. The number of two-dimensional statistical models is determined by the statistical business needs of the counter. Step S406, the path setting module sets a statistical execution path and a data summary manner of the performance counter. The statistical execution path of the performance counter reflects the statistical state transitions on the two-dimensional coordinate statistical model map. The statistical execution path represents the statistical business requirement corresponding to the performance counter. The data summary method refers to the means of data group aggregation, which can be generally divided into statistical algorithms such as averaging, maximizing, minimizing, summing. The data aggregation method is determined by the counter's own attributes and its statistical business requirements. The principles for setting the statistical execution path and data summary mode are as follows: The statistical execution path is directional. On the same two-dimensional coordinate statistical model diagram, the setting direction of the statistical execution path can only be selected from left to right and from bottom to top. As shown in Figure 6, the statistical execution path 1: (RT, RL) -> (RT, L2) -> (D, L2) is the correct statistical execution path. A plurality of statistical execution paths may be defined on the same two-dimensional coordinate statistical model map; and each statistical execution path includes no less than two statistical state nodes. As shown in FIG. 6, the two-dimensional coordinate statistical model includes two statistical execution paths, a statistical execution path 1 and a statistical execution path 2. The data summary mode needs to be set for two adjacent statistical state nodes in each statistical execution path. The data summary mode set between the statistical state nodes is used to collect the data aggregation and aggregation methods adopted during the state transition. Different statistical execution paths can be defined between two state nodes that are not adjacent. As shown in Figure 6, statistical execution path 1 and statistical execution path 2 can be set between the origin (RT, RL) and the status point (D, L2). A statistical execution path on the same dimension can span intermediate state nodes. As shown in Figure 6, the statistical execution path can be directly defined: (RT, RL)-> (RT, L2), and the statistical execution path can also be defined: (RT, RL)-> (RT, L1) ->

Step S408, the acquiring module sets a statistical driving mode of the performance counter. The statistical drive mode is divided into data real-time drive and timing drive. Data real-time driving is suitable for real-time processing requirements of data, that is, On-Line Transaction Processing (OLTP) type statistical requirements, timing driving is suitable for performance requirements of large data processing, that is, online analytical processing (On- Line Analytical Processing (abbreviated as OLAP) type statistical requirements. Step S410, obtaining a statistical execution path of the module running performance counter. Specifically, the following processes are included: 1. Collecting raw performance data of the counter, instantiating the time dimension model and the location dimension model; 2. Completing the summary statistics of the performance counter according to the statistical driving mode of the set performance counter and the statistical execution path. The above examples can flexibly realize the statistical requirements of multiple dimensions of performance counters, meet the requirements of high real-time statistics and large data processing efficiency, and the whole processing method is clear, efficient, and has good scalability and novelty. Example 2 This example uses the statistics of the counter "number of successful calls" in the radio network cell as an example. Assume that the service requirements of the integrated network management user are statistics on counter values of different time dimensions and location dimensions such as network elements and regions. In this example, the following two specific requirements are taken as an example: 1) Statistics of the number of "call successes" of monthly granularity in different provinces, and 2) Statistics of "number of successful calls" in different cities. Assume that the time granularity of the "call success count" counter reported to the integrated network management system in the lower-level network management system is 5 minutes (Min). 1. According to user requirements, the partitioning module divides the statistical dimension of the counter "call success times": Divide the time dimension. The original time dimension RT is 5Min. The extended time dimensions are: hour (H), day (D), month (M). Divide the location dimension. The original location dimension RL is Cell. The extended location dimensions are: Base Transceiver Station (BTS), Base Station System (BSS), City, and Province.

2. The modeling module establishes a two-dimensional coordinate statistical model of "call success number" according to the principle of establishing a two-dimensional coordinate statistical model of the performance counter. The two-dimensional coordinate statistical model of the performance counter is shown in Figure 7.

3. The path setting module sets the statistical execution path and data summary mode of the performance counter "call success times". In this example, the statistics execution path A and the statistics execution path B are set, and the statistics of the execution path A are used to meet the requirement of "counting the number of successful call times of different cities in different cities", and the statistical execution path B is performed. The statistics are used to meet the "counting the number of successful call times of different granularity in different provinces". The schematic diagram of the statistical execution paths A and B is shown in Fig. 8. The detailed information is as follows: Statistical execution path A: origin (5Min, Cell) -> (5Min, City) -> (D, City). Among them, the data statistics mode of the origin (5Min, Cell) -> (5Min, City) is set to sum, and the data statistics mode of (5Min, City) -> (D, City) is set to sum. The statistical execution path A completes the summary of the location dimensions (Cell->City), and then summarizes the time dimensions (5Min->D). Statistical execution path B: origin (5Min, Cell) -> (M, Cell) -> (M, province) ₀ where the origin (5Min, Cell) -> (M, Cell) data statistics mode is set to sum, (M, Cell) -> (M, province ) The data statistics method is set to sum. The statistical execution path first completes the summation of the time dimension (5Min->M), and then performs a summary of the location dimensions (Cell->Province).

4. Get the statistical drive mode of the module setting counter "call success times". Since the amount of data involved in the counter in the application is large, and the statistical requirement is mainly to complete the performance data by day and month summary statistics, the real-time requirement is not high, so the statistical driving mode is set to be the timing driving in this example. 5. Obtain the statistical execution path of the module running counter "call success times" and complete the summary statistics. Obtain the following information from other resource systems or modules: BTS information to which each CELL belongs, BSS information to which each BTS belongs, City to which each BSS belongs, and Province to which each City belongs; The integrated network management system starts collecting the original value of the counter from the lower-level network management, including the value of the 5-minute granularity under each CELL; starting the timing task, performing the execution on the set execution path and B periodically, and obtaining the statistical result. If the user's needs change, for example, by increasing the counter value according to the BSS statistical hour granularity, the statistical execution path C can be increased to meet the user's needs. In addition, each statistical execution path can be modified, and the data summary algorithm of the statistical execution path can also be adjusted. From the above description, it can be seen that the present invention achieves the following technical effects: According to the present invention, according to the multi-dimensional statistical requirement of the performance data, the statistical dimension is divided into two dimensions, and the multi-dimensional performance data in the related technology is solved. Statistics make the implementation of performance management more complicated and cumbersome, and it is difficult to meet various user requirements, and the scalability is poor, and the operation of the statistical execution path is triggered in real time according to the newly generated performance data and the preset is reached. The technical means for triggering the operation of the statistical execution path at the time point solves the problem that the related technologies cannot meet the different time requirements of the performance data of different users in the related art, thereby achieving the complexity of reducing the statistics of the multiple dimensions of the performance data. Degree, and the effect of meeting a variety of user needs. Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computing device, which can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Alternatively, they may be implemented by program code executable by the computing device, such that they may be stored in the storage device by the computing device and, in some cases, may be different from the order herein. The steps shown or described are performed, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps are fabricated as a single integrated circuit module. Thus, the invention is not limited to any specific combination of hardware and software. The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes can be made to the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.

Claims

Claim

A device for acquiring multi-dimensional statistical performance data in network management, comprising:

The partitioning module is set to perform two-dimensional division of the statistical dimension according to the multi-dimensional statistical requirement for the performance data;

a modeling module, configured to establish a two-dimensional coordinate statistical model of the performance data;

a path setting module, configured to set a statistical execution path of the performance data on the two-dimensional coordinate statistical model;

The obtaining module is configured to trigger the running of the statistical execution path according to a predetermined rule, and obtain a summary result corresponding to the multi-dimensional statistical requirement.

The device according to claim 1, wherein the dividing module is configured to divide the statistical dimension into a time statistical dimension and a location statistical dimension, wherein the location statistical dimension comprises at least one of the following: Dimensions, NE Dimensions, Network Dimensions, Regional Dimensions, Business Type Dimensions, User Dimensions.

The device according to claim 1, wherein the path setting module is configured to set a data summary manner for two adjacent statistical state nodes in each of the statistical execution paths; and according to different statistical requirements The two state nodes that are not adjacent set different statistical execution paths.

The device according to any one of claims 1 to 3, wherein the acquiring module comprises:

The triggering unit is configured to trigger the running of the statistical execution path in real time according to the newly generated performance data, or trigger the running of the statistical execution path when the preset time period is reached;

The obtaining unit is configured to run the statistical execution path to obtain a summary result corresponding to the multi-dimensional statistical requirement.

The device according to any one of claims 1 to 3, wherein the path setting module is further configured to set the statistical execution path according to a predetermined direction on the same two-dimensional coordinate statistical model, and / or set the statistical execution path for a statistical execution path on the same dimension across the intermediate state node.

6. A method for obtaining multi-dimensional statistical performance data in network management, comprising:

The partitioning module divides the statistical dimension into two dimensions according to the multi-dimensional statistical requirements of the performance data; The modeling module establishes a two-dimensional coordinate statistical model of the performance data according to the dimension divided by the dividing module;

And on the two-dimensional coordinate statistical model, the path setting module sets a statistical execution path of the performance data;

The obtaining module triggers the running of the statistical execution path according to a predetermined rule, and obtains a summary result corresponding to the multi-dimensional statistical requirement.

The method according to claim 6, wherein the dividing module performs two-dimensional division of the statistical dimension according to the multi-dimensional statistical requirement of the performance data, including:

The dividing module divides the statistical dimension into a time statistical dimension and a location statistical dimension, wherein the location statistical dimension includes at least one of the following: a measurement object dimension, a network element dimension, a network dimension, an area dimension, a service type dimension, User dimension.

The method according to claim 6, wherein the path setting module sets a statistical execution path of the performance data, including:

The path setting module sets a data summary manner for two adjacent statistical state nodes in each of the statistical execution paths; and sets different statistical execution paths for two non-adjacent state nodes according to different statistical requirements. .

The method according to any one of claims 6 to 8, wherein the obtaining module triggers the running of the statistical execution path according to one of the following predetermined rules:

The running of the statistical execution path is triggered in real time according to the newly generated performance data; and the running of the statistical execution path is triggered when a preset time point is reached.

The method according to any one of claims 6 to 8, wherein the path setting module sets a statistical execution path of the performance data, and further includes at least one of the following steps:

On the same two-dimensional coordinate statistical model, the statistical execution path is set according to a predetermined direction; and the statistical execution path is set across a middle state node for a statistical execution path on the same dimension.