WO2018233593A1 - 一种网络态势感知方法、装置、系统及机器可读介质 - Google Patents

一种网络态势感知方法、装置、系统及机器可读介质 Download PDF

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Publication number
WO2018233593A1
WO2018233593A1 PCT/CN2018/091793 CN2018091793W WO2018233593A1 WO 2018233593 A1 WO2018233593 A1 WO 2018233593A1 CN 2018091793 W CN2018091793 W CN 2018091793W WO 2018233593 A1 WO2018233593 A1 WO 2018233593A1
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network
situational awareness
scene
situational
library
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PCT/CN2018/091793
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English (en)
French (fr)
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廖开蒙
张勇天
黄晓军
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • 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
    • H04L43/50—Testing arrangements

Definitions

  • the present invention relates to the field of communications, and in particular, to a network situational awareness method, apparatus, system, and machine readable medium.
  • the situation refers to the current state of the thing and the subsequent trend of change.
  • the network situation refers to the current state of the network and the subsequent trend of change.
  • Network situational awareness is an intuitive and comprehensive perception of the current network conditions at a time-spaced scale, acquiring, synthesizing, predicting, and visualizing future trends for various elements that may cause changes in the situation.
  • Network situational awareness has great significance in improving network monitoring capability, emergency response capability and forecasting network trend, and is widely used in network security, power, transportation and other industries.
  • the current network situational awareness technology is mainly realized by extracting elements and establishing an evaluation index system.
  • the situational factors and evaluation indicators are relatively fixed and single, and it is impossible to accurately evaluate and predict the network situation in various scenarios.
  • the embodiment of the present application provides a network situation awareness method, device, system, and machine readable medium, which can implement dynamic perception of a network situation.
  • the embodiment of the present application provides a network situational awareness method, including: determining a scenario in which a network is located; determining a situational awareness result of the network according to a situational awareness model corresponding to a scenario in which the network is located.
  • the situational awareness models corresponding to different scenarios may be different.
  • the embodiment of the present application further provides a network situational awareness device, including: a scene determining module, configured to determine a scenario in which the network is located; and a sensing module configured to determine the behavior according to a situational awareness model corresponding to the scenario in which the network is located Situational awareness of the network.
  • a scene determining module configured to determine a scenario in which the network is located
  • a sensing module configured to determine the behavior according to a situational awareness model corresponding to the scenario in which the network is located Situational awareness of the network.
  • the embodiment of the present application further provides a network situational awareness system, including: a communication network element and a device having the foregoing network situational awareness device; wherein the network situational awareness device is configured to indicate when there is an abnormality in the situational awareness result of the network.
  • the communication network element performs at least one of network state repair and network trend correction.
  • An embodiment of the present application further provides an apparatus, including: a memory, a processor, and a network situational awareness program stored on the memory and operable on the processor, where the network situational awareness program is executed by the processor The steps of implementing the above network situational awareness method.
  • the embodiment of the present application further provides a machine readable medium storing a network situational awareness program, where the network situational awareness program is executed by a processor to implement the steps of the network situation awareness method.
  • the situational awareness may be performed based on the scenario, and the scenario may be dynamically changed, thereby implementing a network situation. Dynamic perception.
  • FIG. 1 is a schematic flowchart of a network situational awareness method according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart of an example of a network situational awareness method according to an embodiment of the present application
  • FIG. 3 is a schematic diagram 1 of a network situational awareness device according to an embodiment of the present application.
  • FIG. 4 is a second schematic diagram of a network situational awareness device according to an embodiment of the present disclosure.
  • FIG. 5 is a schematic diagram of a network situational awareness system according to an embodiment of the present application.
  • FIG. 6 is a schematic flowchart 1 of a method for network situational awareness according to an embodiment of the present disclosure
  • FIG. 7 is a second schematic flowchart of a method for network situational awareness according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic flowchart 3 of a method for network situational awareness according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic flowchart 4 of a method for network situational awareness according to an embodiment of the present disclosure.
  • FIG. 10 is a schematic diagram of a device according to an embodiment of the present application.
  • the embodiment of the present application provides a network situational awareness method, including:
  • the environment, time, and space in which the network is located are constantly changing, and the scene in which the network is located is constantly changing.
  • the situational awareness model of the corresponding scenario is selected to perform network situational awareness, and dynamic network situational awareness can be realized. For example, when the network enters busy time from idle time or enters busy time from busy time, it needs to switch the corresponding situational awareness model to evaluate the network health; or, compared with the non-typhoon scene, the typhoon scene needs to turn off the dry contact alarm. Put in the situational elements and conduct network availability assessments.
  • the method of this embodiment may further include: configuring a corresponding situational awareness model for one or more scenarios.
  • the situational awareness models corresponding to different scenarios are different. According to the characteristics of the scene, configuring a corresponding situational awareness model for each scene is beneficial to more accurately perceive the network situation of the network in different scenarios. Because there are trigger conditions and range differences in different scenarios, there will be differences in situation factors and evaluation methods. Therefore, different situational awareness models need to be configured for different scenarios.
  • the elements that need to be collected in the typhoon scene include the site dry contact alarm, the cell retreat alarm, and the site retreat alarm. You can select the formula to collect the sites and cells available on the entire network, and evaluate the network availability.
  • the scenario needs to collect traffic data such as traffic volume, traffic, and congestion rate, and the network health can be evaluated according to the weighted average method.
  • the network idle time scenario needs to collect indicator data such as handover success rate, access success rate, and dropped call rate to evaluate network health.
  • configuring a corresponding situational awareness model for one or more scenarios may include:
  • the situational awareness model library includes one or more situational awareness models
  • the scene library includes one or more scenes
  • the situational awareness model in the situational awareness model library and the scene in the scene library may have a one-to-one relationship, or may be a one-to-many relationship, or may be a many-to-one relationship. This application is not limited thereto.
  • each situational awareness model in the situational awareness model library may include a situational element and a situation assessment method, or may include a situational element, a situation assessment method, and a situational treatment suggestion.
  • the situation factors include factors (or elements) that affect the network situation, such as alarm data, performance indicator data, device status data, network status data, log data, weather data, news data, and the like.
  • the situation assessment method includes methods based on the assessment of situational factors and the prediction of network situation.
  • the situation assessment results can be in the form of scoring, grading, classification, etc.
  • the evaluation method based on formula or weighted average can obtain scores, and the results of classification can be obtained based on the evaluation methods of cluster analysis and association analysis.
  • the judgment condition that the situation assessment result has abnormality may include, for example, that the evaluation result is abnormal when the score is lower than a specific value, or the evaluation result is abnormal when the result level is lower than a certain level, or the classification result is considered to be in a certain classification.
  • the evaluation results are abnormal.
  • the situational treatment proposal includes the processing method of the situation assessment result and the trend prediction result.
  • the corresponding processing method may include performing device capacity expansion; for example, if the situation assessment result includes a lower health score of the cell, the corresponding processing method may include resetting. Cell reinitialization and so on.
  • the scene library is a collection of scenes, and the scene may be composed of one or more network external and internal influence conditions or factors.
  • Intrinsic and extrinsic conditions or factors are derived from specific time and space, external factors (weather, emergencies), user behavior, etc. For example, external conditions, operational events, high traffic periods, and focused areas of interest that are of interest to the user or have a large impact on the business can be selected as the scene.
  • the operation event may include network or service adjustment operations, such as network upgrade, network relocation, network expansion, network optimization, etc.
  • the operation log records the daily operations of the network, and may extract frequently occurring operations as operational events from the operation log. .
  • External conditions are derived from external factors (such as weather, emergencies), user behavior, etc.
  • External conditions may include meteorological events (such as typhoons, heavy rain, hail, heavy snow, etc.), social events (such as major conferences, exhibitions, events, etc.), Major webcasts, etc.
  • Each scene may include at least one of the following attribute information: a scene composition condition (or a scene trigger event), a scene range, and a trend prediction time period; wherein the scene range changes as the condition affects the range.
  • a scene composition condition or a scene trigger event
  • the scene range is the influence range of the typhoon
  • the trigger event of the high traffic scene is the peak traffic time of 10:00 to 12:00
  • the scene range is the whole network.
  • configuring a corresponding situational awareness model for one or more scenarios may include:
  • the situational awareness model library includes one or more situational awareness models, each of which includes scene information, situational factors, and situation assessment methods, or includes scene information, situational factors, situation assessment methods, and Situational treatment recommendations.
  • the consideration of the scene is added to the constructed situational awareness model, so that each situational awareness model can be for different scenarios.
  • scenario information used in constructing the scenario library or the situational awareness model library may be set by the user, or may be obtained according to output information of other data processing systems. This application is not limited thereto.
  • the situational awareness result may include a trend prediction result.
  • the method of the embodiment may further include: when there is an abnormality in the trend prediction result of the network, determining a trend correction manner of the network, and instructing the communication network element to perform network trend Correction
  • the situational awareness result may include a trend prediction result and a situation assessment result
  • the trend correction mode of the network is determined, and the communication network element is instructed to perform network trend correction.
  • the situation assessment result is the evaluation result of the current network state
  • the trend prediction result is the prediction result of the network future trend.
  • the network state can be repaired according to the situational treatment suggestion, so as to correct the network situation; when the trend prediction result is abnormal, the network trend can be corrected according to the situational treatment suggestion, so as to avoid in advance problem.
  • the method in this embodiment can be applied to a communication device such as a server.
  • a communication device such as a server.
  • the repair and correction mode can be determined, and the corresponding communication network element is instructed to perform processing according to the repair and correction mode. In this way, situation correction and trend correction can be automatically performed according to the situational awareness result, thereby achieving efficient network operation.
  • FIG. 2 is a schematic flowchart diagram of a network situational awareness method according to an embodiment of the present application. As shown in FIG. 2, the network situational awareness method of this embodiment includes:
  • the situational awareness model library includes a set of situational awareness models; the description of the situational awareness model is as described above, and thus will not be described herein;
  • the scene library is a collection of scenes; the description of the scene is as described above, and thus is not described herein;
  • the situational awareness model of the corresponding scene is selected to perform network situational awareness
  • the situational awareness result may include a situation assessment result (ie, an assessment of the current network state) and a trend prediction result (ie, Evaluation of future trends).
  • the situational awareness result may be in the form of score, classification, classification, etc.
  • the abnormality judgment condition may include, for example, an abnormality when the score is lower than a specific value, or an abnormality when the result level is lower than a certain level, or the result When the classification is in a certain category, the result is considered abnormal.
  • S206 repairing the situation; wherein, according to the judgment result of S205, if the decision situation is abnormal, it is necessary to perform situation correction according to the situational awareness model of the scene, perform state correction, or perform trend correction, and avoid the problem in advance. For example, if the current network health is low, you need to add a neighboring cell. When predicting that the future network capacity will exceed the current capacity limit, you need to give a capacity expansion decision and increase the capacity.
  • this embodiment provides a network situational awareness device, including:
  • the scenario determining module 301 is configured to determine a scenario in which the network is located;
  • the sensing module 302 is configured to determine a situational awareness result of the network according to a situational awareness model corresponding to the scenario in which the network is located.
  • the network situational awareness device of this embodiment may further include: a configuration module 300 configured to configure a corresponding situational awareness model for one or more scenarios.
  • the situational awareness models corresponding to different scenarios are different.
  • the configuration module 300 can include:
  • the first building unit 3001 is configured to build a situational awareness model library, wherein the situational awareness model library includes one or more situational awareness models;
  • a second building unit 3002 configured to build a scene library, where the scene library includes one or more scenes;
  • the configuration unit 3003 is configured to establish a correspondence between the situational awareness model in the situational awareness model library and the scene in the scene library.
  • each situational awareness model in the situational awareness model library may include situational factors and situation assessment methods, or include situational factors, situation assessment methods, and situational processing suggestions.
  • Each scene in the scene library may include at least one of the following: a scene trigger condition, a scene range, and a trend prediction time period.
  • the configuration module 300 can be configured to configure a corresponding situational awareness model for one or more scenarios by constructing a situational awareness model library, wherein the situational awareness model library includes one or more situational awareness models, Each situational awareness model includes scene information, situational factors, and situation assessment methods, or includes scene information, situational factors, situation assessment methods, and situational processing recommendations.
  • the situational awareness result may include a trend prediction result; the sensing module 302 may be further configured to determine a trend correction mode of the network when the trend prediction result of the network is abnormal, and instruct the communication network element to perform network trend correction;
  • the situational awareness result can include a trend prediction result and a situation assessment result; the awareness module 302 can also be configured to perform at least one of the following:
  • the trend correction mode of the network is determined, and the communication network element is instructed to perform network trend correction.
  • the embodiment further provides a network situational awareness system, including a communication network element 502 and a device having a network situational awareness device 501.
  • the structure of the network situational awareness device 501 is as described in the foregoing device embodiment, and thus is not described herein.
  • the network situational awareness device 501 can be disposed on a communication device such as a server.
  • the scene information can be manually input by the user or can be determined based on the output information of other data systems. For example, external events, operational events, high traffic periods, and focused areas of interest that the user cares about or have a large impact on the business can be used as the source of the scene input.
  • data collected and configured by the network situational awareness device 501 may be stored in the database 503.
  • the database 503 can be deployed in a server with the network situational awareness device 501, or separately deployed on different servers. This application is not limited thereto.
  • the network situation sensing device 501 may instruct the communication network element 502 to perform at least one of network state repair and network trend correction to facilitate correcting the network situation and avoiding in advance. problem.
  • the network situational awareness device 501 after determining that the situational awareness result of the network is abnormal, determines the corresponding repair mode or the trend correction mode, and then notifies the communication network element 502 to perform the processing according to the determined repair mode or the trend correction mode.
  • the present embodiment configures the situational awareness model of each scenario of the network to dynamically sense the network situation, and automatically performs situation restoration and trend correction according to the situation assessment result, thereby implementing efficient network operation.
  • the application scenario of this embodiment is: when the network trend prediction result exceeds the limit, the trend correction is performed.
  • FIG. 6 is a schematic flowchart 1 of a method for network situational awareness according to an embodiment of the present disclosure. As shown in FIG. 6, the embodiment includes the following steps:
  • Constructing a situational awareness model wherein the situational element comprises a hotspot location, and the situation assessment method comprises a method for judging whether the indicator data exceeds a threshold, and the indicator data includes a voice quality, a dropped call rate, and an uplink and downlink traffic, and the situation processing recommendation includes an indicator.
  • the processing recommendations after the data exceeds the threshold, such as the upper and lower flow limit violations, are recommended to limit the network.
  • the sensing range includes a hotspot geographic area such as a pedestrian street or a Shanghai Nanjing Road.
  • the scene triggering event is a high flow period from 6 am to 12 pm, and 10 hours is used as a trend forecasting period.
  • S604 In the scenario of the hot spot, the situational awareness model of the scenario is used to dynamically evaluate the network quality of the hotspot and predict the trend.
  • the application scenario of this embodiment is: performing network situational awareness during typhoon season and non-typhoon season switching.
  • FIG. 7 is a second schematic flowchart of a method for network situational awareness according to an embodiment of the present disclosure. As shown in FIG. 7, the embodiment includes the following steps:
  • Constructing a situational awareness model library wherein the situation element may include a meteorological data typhoon season, and the situation assessment mode includes selecting a cell back-off number as a factor in the normal network availability assessment, and measuring network availability by a ratio of the returned community; the typhoon In the season, there are more typhoons, which will cause large-area base stations to be powered off. In order to ensure timely detection of power-off base stations for processing, it is necessary to use the dry-contact base station power-off alarm as a factor to measure network availability.
  • S703 Establish a correspondence between the scene constructed by S702 and the situational awareness model constructed by S701, and trigger the situational awareness of the typhoon scene as the typhoon weather event is detected.
  • S704 Using a situational awareness model of the typhoon scene to perform dynamic situational awareness.
  • S705 is similar to S605 in the first embodiment. If the current state is abnormal or the trend is abnormal, the related processing suggestions are processed.
  • the application scenario of this embodiment is: when the network state evaluation result exceeds the limit, the state is corrected.
  • the traffic peak will cause network congestion. Every day from 10 am to 12 am, when traffic is busy, it is necessary to perceive the state and development trend of the whole network during this time period.
  • FIG. 8 is a schematic flowchart 3 of a method for network situational awareness according to an embodiment of the present disclosure. As shown in FIG. 8, the embodiment includes:
  • Constructing a situational awareness model library, wherein the situation element includes a traffic peak, and the traffic capacity capacity ratio, the traffic capacity ratio, the congestion rate, and the like may be combined by using a weighted average method as a network health degree indicator as a situation assessment manner.
  • network health 20% ⁇ congestion rate + 40% ⁇ traffic capacity ratio + 40% ⁇ traffic capacity ratio.
  • S802 Establish a traffic peak scenario, where the scenario trigger event is a temporary traffic peak, and the sensing range is the entire network, and the trend prediction time period is from 10:00 am to 12:00 am.
  • S804 The situational awareness model corresponding to the traffic peak scene is used for dynamic situational awareness.
  • the traffic capacity ratio and the congestion rate indicator exceed a threshold; in this embodiment, The traffic capacity ratio and the congestion rate exceed the threshold.
  • the indicator exceeding the threshold is prioritized according to the weight. Since the weight of the traffic volume is 40% and the congestion rate is 20%, the first processing is performed.
  • the traffic capacity ratio indicator according to the situational awareness model, the processing of the traffic capacity ratio exceeds the limit, for example, limiting traffic access, processing the congestion rate indicator, and obtaining the congestion rate overrun according to the perceptual model. Suggestions, for example, restrict user access.
  • the application scenario of this embodiment is: major conference scene switching for network situational awareness.
  • FIG. 9 is a schematic flowchart diagram of a method for network situational awareness according to an embodiment of the present disclosure. As shown in FIG. 9, the embodiment includes:
  • Constructing a situational awareness model library wherein the situational factors include before the start of major conferences and major conferences, and the indicators such as handover success rate, dropped call rate, and access success rate can be combined with the weighted average method as the network health degree before the major conference.
  • the network health degree 30% ⁇ handover success rate + 40% ⁇ call drop rate + 30% ⁇ access success rate, network health is less than 70% is considered abnormal.
  • the traffic health capacity ratio, traffic capacity ratio, congestion rate and other indicators can be combined with the weighted average method as the network health index as the situation assessment factor.
  • Network health 60% ⁇ congestion rate + 30% ⁇ Traffic capacity accounted for +10% ⁇ traffic capacity ratio, network health is less than 90% considered abnormal.
  • the situational awareness model corresponding to the major conference is used for situational awareness; when the major conference is conducted, the situational awareness model corresponding to the major conference is used for dynamic situational awareness.
  • S905 is similar to S805 in the third embodiment. If the current state is abnormal or the trend is abnormal before the conference, the related processing is recommended to block and cancel the base station. If the current state is abnormal or the trend is abnormal during the conference, the base station needs to be expanded.
  • the embodiment of the present application further provides a device, as shown in FIG. 10, the device includes: a memory 1002, a processor 1004, and a network situational awareness program stored on the memory 1002 and operable on the processor 1004, the network situation The steps of implementing the network situational awareness method described above when the perceptual program is executed by the processor.
  • the device may also include some other devices, such as input and output device 1006, transmission device 1008.
  • the embodiment of the present application further provides a machine readable medium storing a network situational awareness program, where the network situational awareness program is executed by the processor to implement the step of the network situation awareness method.
  • Such software may be distributed on a machine-readable medium, such as a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transitory media).
  • a computer-readable medium includes volatile and nonvolatile, implemented in any method or technology for storing information, such as computer readable instructions, data structures, program modules or other data. Sex, removable and non-removable media.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridge, magnetic tape, magnetic disk storage or other magnetic storage device, or may Any other medium used to store the desired information and that can be accessed by the computer.
  • communication media typically includes computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and can include any information delivery media. .

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Abstract

本申请公开了一种网络态势感知方法、装置、系统及机器可读介质。上述网络态势感知方法包括：确定网络所处的场景；以及根据网络所处的场景对应的态势感知模型，确定网络的态势感知结果。如此，基于场景进行网络态势感知，从而实现网络态势的动态感知。

Description

一种网络态势感知方法、装置、系统及机器可读介质 技术领域
本发明涉及通信领域，尤其涉及一种网络态势感知方法、装置、系统及机器可读介质。
背景技术
态势是指事物的当前状态和后续变化趋势，网络态势指网络的当前状态和后续变化趋势。网络态势感知就是在一定时空尺度下，对当前网络状况的直观全面感知，对可能引起态势变化的各种要素获取、综合、预测并可视化未来的发展趋势。
网络态势感知在提高网络的监控能力、应急响应能力和预测网络的趋势上具有较大的意义，在网络安全、电力、交通等行业得到广泛应用。当前网络态势感知技术主要通过提取要素、建立评估指标体系实现，然而，态势要素和评估指标相对固定和单一，无法准确对各种不同场景的网络态势进行评估和预测。
发明内容
本申请实施例提供一种网络态势感知方法、装置、系统及机器可读介质，能够实现网络态势的动态感知。
本申请实施例提供一种网络态势感知方法，包括：确定网络所处的场景；根据所述网络所处的场景对应的态势感知模型，确定所述网络的态势感知结果。在示例性实施方式中，不同场景对应的态势感知模型可以不同。
本申请实施例还提供一种网络态势感知装置，包括：场景确定模块，设置为确定网络所处的场景；感知模块，设置为根据所述网络所处的场景对应的态势感知模型，确定所述网络的态势感知结果。
本申请实施例还提供一种网络态势感知系统，包括：通信网元以及具有上述网络态势感知装置的设备；其中，所述网络态势感知装置设置为在 确定网络的态势感知结果存在异常时，指示所述通信网元进行网络状态修复和网络趋势纠偏中的至少一项。
本申请实施例还提供一种设备，包括：存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的网络态势感知程序，所述网络态势感知程序被所述处理器执行时实现上述网络态势感知方法的步骤。
本申请实施例还提供一种机器可读介质，存储有网络态势感知程序，所述网络态势感知程序被处理器执行时实现上述网络态势感知方法的步骤。
本申请实施例中，通过确定网络所处的场景，并根据该场景对应的态势感知模型确定网络的态势感知结果，可以基于场景进行态势感知，由于场景是动态变化的，因此可以实现网络态势的动态感知。
附图说明
图1为本申请实施例提供的一种网络态势感知方法的流程示意图；
图2为本申请实施例提供的网络态势感知方法的示例流程示意图；
图3为本申请实施例提供的网络态势感知装置的示意图一；
图4为本申请实施例提供的网络态势感知装置的示意图二；
图5为本申请实施例提供的网络态势感知系统的示意图；
图6为本申请实施例提供的网络态势感知的方法流程示意图一；
图7为本申请实施例提供的网络态势感知的方法流程示意图二；
图8为本申请实施例提供的网络态势感知的方法流程示意图三；
图9为本申请实施例提供的网络态势感知的方法流程示意图四；
图10为本申请实施例提供的设备示意图。
具体实施方式
以下结合附图对本申请实施例进行详细说明。
在附图的流程示意图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行。并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
如图1所示，本申请实施例提供一种网络态势感知方法，包括：
S101、确定网络所处的场景；
S102、根据网络所处的场景对应的态势感知模型，确定网络的态势感知结果。
在本实施例中，网络所处的环境、时间、空间是不断变化的，导致网络所处的场景是不断变迁的。由于网络的场景是不断变化的，随着场景的变化，选择相应场景的态势感知模型进行网络态势感知，可以实现动态的网络态势感知。比如，网络从闲时进入到忙时或者从忙时进入到闲时，需要切换相应的态势感知模型进行网络健康度评估；或者，相比非台风场景，台风场景就需要把干接点断电告警放进态势要素，进行网络可用性评估。
其中，本实施例的方法还可以包括：给一个或多个场景配置对应的态势感知模型。
在示例性实施方式中，不同场景对应的态势感知模型不同。其中，根据场景特点，给每个场景配置相应的态势感知模型，有利于更加准确地感知网络在不同场景的网络态势。由于不同场景存在触发条件及范围差异，其态势要素和评估方式会存在差异，因此，需要给不同的场景配置不同的态势感知模型。比如，台风场景需要采集的要素包括站点干接点告警、小区退服告警、站点退服告警，可以选择公式统计全网可用的站点、小区，评估网络可用性。比如，网络忙时场景需要采集话务量、流量、拥塞率等指标数据，可以根据加权平均方法，评估网络健康度。比如，网络闲时场景需要采集切换成功率、接入成功率、掉话率等指标数据，来评估网络健康度。
在示例性实施方式中，给一个或多个场景配置对应的态势感知模型，可以包括：
构建态势感知模型库；态势感知模型库包括一个或多个态势感知模型；
构建场景库；场景库包括一个或多个场景；
建立态势感知模型库中的态势感知模型和场景库中的场景之间的对应关系。
其中，态势感知模型库中的态势感知模型和场景库中的场景可以是一对一的关系，或者，可以是一对多的关系，或者，可以是多对一的关系。本申请对此并不限定。
其中，态势感知模型库中的每个态势感知模型可以包括态势要素和态势评估方式，或者，可以包括态势要素、态势评估方式及态势处理建议。
其中，态势要素包括影响网络态势的因子(或元素)，比如，包括告警数据、性能指标数据、设备状态数据、网络状态数据、日志数据、气象数据、新闻数据等等。
其中，态势评估方式包括基于态势要素评估及预测网络态势的方法。评估网络态势的方法多种多样，比如基于数学模型的方法、基于知识推理的方法、基于模式识别的方法等等。态势评估结果可以是打分、分级、分类等形式，比如，基于公式或加权平均的评估方法可以得到分值，基于聚类分析、关联分析的评估方法可以得到分类的结果等等。态势评估结果存在异常的判断条件比如可以包括：分值低于某一具体数值时认为评估结果异常，或者，结果级别低于一定级别时认为评估结果异常，或者，分类结果处于某一分类时认为评估结果异常。
其中，态势处理建议包括态势评估结果和趋势预测结果的处理方法。比如，趋势预测结果包括网络未来容量会达到较高的级别，则对应的处理方法可以包括进行设备容量扩充；比如，态势评估结果包括小区的健康度分数较低，则对应的处理方法可以包括复位小区重新初始化等等。
其中，场景库是场景的集合，场景可以由一种或多种网络外在、内在影响条件或因素构成。内在、外在条件或因素来源于特定的时空、外界因素(天气、突发事件)、用户行为等。比如，可以挑选用户关心的或对业 务影响比较大的外部条件、操作事件、高话务时段、重点关注区域等作为场景。其中，操作事件可以包括网络或业务调整操作，比如，网络升级、网络搬迁改造、网络扩容、网络优化调整等；操作日志记录了网络日常操作，可以从操作日志中提取频繁发生的操作作为操作事件。外部条件来源于外界因素(如天气、突发事件)、用户行为等，外部条件可以包括气象事件(如台风、暴雨、冰雹、大雪等)、社会事件(如重大会议、展览、赛事等)、重大网络直播等。
其中，每个场景可以包括以下至少之一属性信息：场景构成条件(或场景触发事件)、场景范围、趋势预测时间段；其中，场景范围随着条件影响范围变化而变化。比如，台风场景的触发条件是台风发生，场景范围是台风的影响范围；比如，高话务场景的触发事件就是10点到12点话务高峰时段，场景范围是全网。
在示例性实施方式中，给一个或多个场景配置对应的态势感知模型，可以包括：
构建态势感知模型库，其中，态势感知模型库包括一个或多个态势感知模型，每个态势感知模型包括场景信息、态势要素及态势评估方式，或者，包括场景信息、态势要素、态势评估方式及态势处理建议。
在本实现方式中，在构建的态势感知模型中加入场景的考量，使得每个态势感知模型可以是针对不同的场景。
需要说明的是，在构建场景库或态势感知模型库时采用的场景信息可以是由用户设置的，或者，可以根据其他数据处理系统的输出信息得到。本申请对此并不限定。
在示例性实施方式中，态势感知结果可以包括趋势预测结果；本实施例的方法还可以包括：当网络的趋势预测结果存在异常时，确定网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏；
或者，态势感知结果可以包括趋势预测结果和态势评估结果；
本实施例的方法还可以包括以下至少之一：
当网络的态势评估结果存在异常时，确定网络的修复方式，并指示通信网元进行网络状态修复；
当网络的趋势预测结果存在异常时，确定网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏。
其中，态势评估结果是对当前网络状态的评估结果，趋势预测结果是对网络未来趋势的预测结果。
其中，当态势评估结果存在异常时，可以依据态势处理建议进行网络状态的修复，以便于修正网络态势；当趋势预测结果存在异常时，可以依据态势处理建议进行网络趋势的纠偏，以便于提前规避问题。
本实施例的方法可以应用于服务器等通信设备，在确定态势感知结果存在异常时，可以确定修复和纠偏方式，并按照修复和纠偏方式指示相应的通信网元进行处理。如此，可以根据态势感知结果自动进行态势修复和趋势纠偏，从而实现高效的网络运营。
图2为本申请实施例的网络态势感知方法的示例流程示意图。如图2所示，本实施例的网络态势感知方法包括：
S201、构建态势感知模型库；态势感知模型库包括态势感知模型的集合；关于态势感知模型的说明如前所述，故于此不再赘述；
S202、构建场景库；场景库是场景的集合；关于场景的说明如前所述，故于此不再赘述；
S203、配置场景的态势感知模型；
基于S201构建的态势感知模型库和S202构建的场景库，给每个场景确定对应的态势感知模型；
S204、动态感知网络态势；
随着场景变化，选择相应场景的态势感知模型进行网络态势感知；
S205、判断网络态势感知结果是否异常；若异常，则执行S206，否则，返回S204；
在S204得到基于场景的态势感知结果后，可以根据异常判断条件，确定态势感知结果是否异常，其中，态势感知结果可以包括态势评估结果(即对当前网络状态的评估)及趋势预测结果(即对未来趋势的评估)。其中，态势感知结果可以采用分值、分级、分类等形式，异常判断条件比如可以包括：分值低于某一具体数值时认为异常，或者，结果级别低于一定级别时认为异常，或者，结果处于某一分类时认为结果异常。
如果态势感知结果判断为正常，则随着场景变迁，继续动态感知网络态势，循环往复。
S206、修复态势；其中，根据S205的判断结果，如果决策态势异常，需要根据场景的态势感知模型的态势处理建议，进行状态修正，或者进行趋势纠偏，提前规避问题。比如，发现当前网络健康度比较低，则需要新增邻区；当预测未来网络容量会超过当前容量上限，则需要给出扩容决策，进行增加容量的操作。
如图3所示，本实施例提供一种网络态势感知装置，包括：
场景确定模块301，设置为确定网络所处的场景；
感知模块302，设置为根据网络所处的场景对应的态势感知模型，确定网络的态势感知结果。
如图4所示，本实施例的网络态势感知装置还可以包括：配置模块300，设置为给一个或多个场景配置对应的态势感知模型。
在示例性实施方式中，不同场景对应的态势感知模型不同。
如图4所示，配置模块300可以包括：
第一构建单元3001，设置为构建态势感知模型库，其中，态势感知模型库包括一个或多个态势感知模型；
第二构建单元3002，设置为构建场景库，其中，场景库包括一个或多个场景；
配置单元3003，设置为建立态势感知模型库中的态势感知模型和场景 库中的场景之间的对应关系。
其中，态势感知模型库中的每个态势感知模型可以包括态势要素和态势评估方式，或者，包括态势要素、态势评估方式及态势处理建议。
其中，场景库中的每个场景可以包括以下至少之一信息：场景触发条件、场景范围、趋势预测时间段。
在示例性实施方式中，配置模块300可以设置为通过以下方式给一个或多个场景配置对应的态势感知模型：构建态势感知模型库，其中，态势感知模型库包括一个或多个态势感知模型，每个态势感知模型包括场景信息、态势要素及态势评估方式，或者，包括场景信息、态势要素、态势评估方式及态势处理建议。
在示例性实施方式中，态势感知结果可以包括趋势预测结果；感知模块302还可以设置为当网络的趋势预测结果存在异常时，确定网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏；
或者，态势感知结果可以包括趋势预测结果和态势评估结果；感知模块302还可以设置为执行以下至少之一：
当网络的态势评估结果存在异常时，确定网络的修复方式，并指示通信网元进行网络状态修复；
当网络的趋势预测结果存在异常时，确定网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏。
此外，关于本实施例的装置的相关说明可以参照上述方法实施例的描述，故于此不再赘述。
如图5所示，本实施例还提供一种网络态势感知系统，包括通信网元502以及具有网络态势感知装置501的设备。其中，网络态势感知装置501的结构如上述装置实施例所述，故于此不再赘述。
其中，网络态势感知装置501可以设置在服务器等通信设备上。在第二构建单元构建场景库时，场景信息可以由用户手动输入，或者，可以根 据其他数据系统的输出信息确定。比如，用户关心的或对业务影响比较大的外部事件、操作事件、高话务时段、重点关注区域等信息，可以作为场景输入的来源。
在本实施例中，网络态势感知装置501采集和配置的数据(比如，态势感知模型、场景库等)可以存储在数据库503中。数据库503可以和网络态势感知装置501部署在一个服务器中，或者，分别部署在不同的服务器上。本申请对此并不限定。
在本实施例中，网络态势感知装置501在确定网络的态势感知结果存在异常时，可以指示通信网元502进行网络状态修复和网络趋势纠偏中的至少一项，以便于修正网络态势和提前规避问题。比如，网络态势感知装置501在确定网络的态势感知结果存在异常时，确定相应的修复方式或趋势纠偏方式后，通过信令通知通信网元502按照确定的修复方式或趋势纠偏方式进行处理。
综上所述，本实施例通过配置网络各个场景的态势感知模型，动态感知网络态势，根据态势评估结果自动进行态势修复和趋势纠偏，实现高效的网络运营。
下面通过多个实施例对本申请的方案进行说明。
实施例一
本实施例的应用场景是：当网络趋势预测结果超限，进行趋势纠偏。
图6为本申请实施例提供的网络态势感知的方法流程示意图一，如图6所示，本实施例包括如下步骤：
S601、构建态势感知模型，其中，态势要素包括热点地段，态势评估方式包括采用指标数据是否超过阀值进行判断的方法，指标数据包括语音质量、掉话率、上下行流量，态势处理建议包括指标数据超过阀值后的处理建议，例如上下行流量超限的处理建议是进行网络限速。
S602、建立热点地段的场景，比如，感知范围包括步行街、上海南京路等热点地理区域，场景触发事件是早上6点到晚上12点的高人流时段， 以10小时作为趋势预测时间段。
S603、建立S602中构建的场景和S601中构建的态势感知模型的对应关系，并采用该场景的态势感知模型。
S604、在热点地段场景下，采用该场景的态势感知模型，动态评估热点地段的网络质量并进行趋势预测。
S605、如果当前网络质量态势良好，但是根据趋势预测结果，语音质量和掉话率趋势正常，但上下行流量会在3小时后超过阀值，则获取上下行流量超限的处理建议，比如，下发相关命令到基站进行自动相关参数设置，动态进行网络限速等。
实施例二
本实施例的应用场景是：在台风季场景和非台风季场景切换进行网络态势感知。
图7为本申请实施例提供的网络态势感知的方法流程示意图二，如图7所示，本实施例包括如下步骤：
S701、构建态势感知模型库，其中，态势要素可以包括气象数据台风季，态势评估方式包括在正常网络可用性评估中，选取小区退服数为要素，通过退服小区占比来衡量网络可用性；台风季台风较多，会造成大面积基站断电，为保证及时发现断电基站进行处理，就需要用干接点基站断电告警为要素来衡量网络可用性。
S702、建立台风影响范围的场景，其中，场景触发事件是台风来临时刻，感知范围为台风经过的范围，以台风影响时间作为趋势预测时间段。
S703、建立S702构建的场景和S701构建的态势感知模型的对应关系，随着监测到台风气象事件，触发台风场景的态势感知。
S704、采用台风场景的态势感知模型，进行动态态势感知。
S705与实施例一中的S605类似，如果感知到目前状态不正常或者趋势不正常，获取相关的处理建议进行处理。
实施例三
本实施例的应用场景是：当网络状态评估结果超限，进行状态修正。
以话务网络为例，话务高峰会造成网络拥塞，每天上午10点到12点是话务忙时，在这个时间段有必要感知全网的状态和发展趋势。
图8为本申请实施例提供的网络态势感知的方法流程示意图三，如图8所示，本实施例包括：
S801、构建态势感知模型库，其中，态势要素包括话务高峰，可以以话务量容量占比、流量容量占比、拥塞率等指标，以加权平均法组合为网络健康度指标作为态势评估方式，网络健康度＝20％×拥塞率+40％×话务量容量占比+40％×流量容量占比。
S802、建立话务高峰场景，其中，场景触发事件是话务高峰来临时刻，感知范围为全网，以上午10点到12点作为趋势预测时间段。
S803、建立S802构建的话务高峰场景和S801构建的态势感知模型的对应关系；选取S802建立的话务高峰场景，随着时间到达上午10点，触发话务高峰场景的态势感知。
S804、采用话务高峰场景对应的态势感知模型进行动态态势感知。
S805、如果10点时，当前网络健康度态势已经超过阀值，根据加权平均公式，首先判断话务量容量占比、流量容量占比、拥塞率指标是否超过阀值；本实施例中，话务量容量占比、拥塞率超过阀值，超过阀值的指标再根据权重作为优先级优先处理，由于话务量容量占比的权重为40％，拥塞率的权重为20％，那么首先处理话务量容量占比指标；根据态势感知模型获取话务量容量占比超限的处理建议，比如，限制话务接入等，再处理拥塞率指标，根据感知模型获取拥塞率超限的处理建议，比如，限制用户接入等。
实施例四
政要、企业等经常需要召开重大会议，保障要求比较高。本实施例的 应用场景是：重大会议场景切换进行网络态势感知。
图9为本申请实施例提供的网络态势感知的方法流程示意图四，如图9所示，本实施例包括：
S901、构建态势感知模型库，其中，态势要素包括重大会议和重大会议开始前，重大会议前可以以切换成功率、掉话率、接入成功率等指标，以加权平均法组合为网络健康度指标作为态势评估方式，网络健康度＝30％×切换成功率+40％×掉话率+30％×接入成功率，网络健康度低于70％认为是异常的。重大会议时可以以话务量容量占比、流量容量占比、拥塞率等指标，以加权平均法组合为网络健康度指标作为态势评估要素，网络健康度＝60％×拥塞率+30％×话务量容量占比+10％×流量容量占比，网络健康度低于90％认为是异常的。
S902、建立重大会议前场景和重大会议时场景，其中，重大会议时场景的触发事件是重大会议开始时刻，感知范围为重大会议所在范围，以重大会议进行期间作为趋势预测时间段。
S903、建立重大会议前场景和重大会议开始前对应的态势感知模型的关联关系，建立重大会议时场景和重大会议对应的态势感知模型的关联关系；选取S902里建立的重大会议前场景和重大会议时场景，随着时间到达会议开始时间，触发重大会议时场景的态势感知。
S904、在重大会议开始前，采用重大会议开始前对应的态势感知模型进行态势感知；在重大会议进行时，采用重大会议对应的态势感知模型进行动态态势感知。
S905与实施例三中的S805类似，如果在会议前感知到目前状态不正常或者趋势不正常，获取相关的处理建议进行基站的闭塞、解闭。如果在会议中感知到目前状态不正常或者趋势不正常，需要进行基站的扩容。
随着时间的变化，重大会议前后对网络健康度的理解和处理方法是不一样的，这也说明动态感知网络态势的必要性。
此外，本申请实施例还提供一种设备，如图10所示，该设备包括： 存储器1002、处理器1004以及存储在存储器1002上并可在处理器1004上运行的网络态势感知程序，网络态势感知程序被处理器执行时实现上述的网络态势感知方法的步骤。可选地，该设备还可以包括一些其他的器件，例如，输入输出设备1006，传输设备1008。
此外，本申请实施例还提供一种机器可读介质，存储有网络态势感知程序，所述网络态势感知程序被处理器执行时实现上述的网络态势感知方法的步骤。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些组件或所有组件可以被实施为由处理器，如数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在机器可读介质(比如，计算机可读介质)上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
虽然本申请所揭露的实施方式如上，但所述的内容仅为便于理解本申请而采用的实施方式，并非用以限定本申请。任何本申请所属领域内的技术人员，在不脱离本申请所揭露的精神和范围的前提下，可以在实施的形 式及细节上进行任何的修改与变化，但本申请的专利保护范围，仍须以所附的权利要求书所界定的范围为准。

Claims (15)

  1. 一种网络态势感知方法，包括：
    确定网络所处的场景；
    根据所述网络所处的场景对应的态势感知模型，确定所述网络的态势感知结果。
  2. 根据权利要求1所述的方法，其中，所述方法还包括：
    给一个或多个场景配置对应的态势感知模型。
  3. 根据权利要求2所述的方法，其中，所述给一个或多个场景配置对应的态势感知模型，包括：
    构建态势感知模型库；所述态势感知模型库包括一个或多个态势感知模型；
    构建场景库；所述场景库包括一个或多个场景；
    建立所述态势感知模型库中的态势感知模型和所述场景库中的场景之间的对应关系。
  4. 根据权利要求3所述的方法，其中，所述态势感知模型库中的每个态势感知模型包括态势要素和态势评估方式，或者，包括态势要素、态势评估方式及态势处理建议。
  5. 根据权利要求3所述的方法，其中，所述场景库中的每个场景包括以下至少之一信息：场景触发事件、场景范围、趋势预测时间段。
  6. 根据权利要求2所述的方法，其中，所述给一个或多个场景配置对应的态势感知模型，包括：
    构建态势感知模型库，所述态势感知模型库包括一个或多个态势感知模型，每个态势感知模型包括场景信息、态势要素及态势评估方式，或者，包括场景信息、态势要素、态势评估方式及态势处理建议。
  7. 根据权利要求1所述的方法，其中，所述态势感知结果包括趋势预测结果；所述方法还包括：
    当所述网络的趋势预测结果存在异常时，确定所述网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏。
  8. 根据权利要求1所述的方法，其中，所述态势感知结果包括趋势预测结果和态势评估结果；所述方法还包括以下至少之一：
    当所述网络的态势评估结果存在异常时，确定所述网络的修复方式，并指示通信网元进行网络状态修复；
    当所述网络的趋势预测结果存在异常时，确定所述网络的趋势纠偏方式，并指示通信网元进行网络趋势纠偏。
  9. 根据权利要求1所述的方法，其中，不同场景对应的态势感知模型不同。
  10. 一种网络态势感知装置，包括：
    场景确定模块，设置为确定网络所处的场景；
    感知模块，设置为根据所述网络所处的场景对应的态势感知模型，确定所述网络的态势感知结果。
  11. 根据权利要求10所述的装置，其中，所述装置还包括：配置模块，设置为给一个或多个场景配置对应的态势感知模型。
  12. 根据权利要求11所述的装置，其中，所述配置模块，包括：
    第一构建单元，设置为构建态势感知模型库，其中，所述态势感知模型库包括一个或多个态势感知模型；
    第二构建单元，设置为构建场景库，其中，所述场景库包括一个或多个场景；
    配置单元，设置为建立所述态势感知模型库中的态势感知模型和 所述场景库中的场景之间的对应关系。
  13. 一种网络态势感知系统，包括：通信网元以及具有如权利要求10至12中任一项所述的网络态势感知装置的设备；其中，所述网络态势感知装置设置为在确定网络的态势感知结果存在异常时，指示所述通信网元进行网络状态修复和网络趋势纠偏中的至少一项。
  14. 一种设备，包括：存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的网络态势感知程序，所述网络态势感知程序被所述处理器执行时实现如权利要求1至9中任一项所述的网络态势感知方法的步骤。
  15. 一种机器可读介质，存储有网络态势感知程序，所述网络态势感知程序被处理器执行时实现如权利要求1至9中任一项所述的网络态势感知方法的步骤。
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