CN114091716A - Network situation prediction method and device - Google Patents
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Abstract
The disclosure relates to a method and a device for predicting network situation, and relates to the technical field of communication. The method comprises the following steps: determining the current network characteristic type by utilizing a classification model according to the acquired current network characteristic information; and predicting the future network situation by utilizing a situation analysis model based on machine learning according to the current network feature type.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method and an apparatus for predicting a network situation, and a non-volatile computer-readable storage medium.
Background
With the continuous development of communication technology, the maintenance and guarantee of network security become the main problem of network operation and maintenance. Therefore, before the large-scale paralysis of the network occurs, the network security situation is reasonably evaluated, the emergency plan is started, and the problems can be found in time and remedied in time. The method has important significance for preventing large-area network paralysis and network guarantee.
In the related art, network security evaluation is mostly performed according to manual experience, so that the network situation is predicted.
Disclosure of Invention
The inventors of the present disclosure found that the following problems exist in the above-described related art: the network situation prediction accuracy is low due to the serious influence of human factors.
In view of this, the present disclosure provides a technical solution for predicting a network situation, which can improve the accuracy of predicting the network situation.
According to some embodiments of the present disclosure, there is provided a method for predicting a network situation, including: determining the current network characteristic type by utilizing a classification model according to the acquired current network characteristic information; and predicting the future network situation by utilizing a situation analysis model based on machine learning according to the current network feature type.
In some embodiments, determining the current network feature type using the classification model according to the obtained current network feature information includes: and determining the network characteristic type by utilizing a machine learning-based classification model according to at least one of the network performance type and the influence degree on the network situation which can be influenced by the current network characteristic information.
In some embodiments, the situational analysis model includes a situational assessment model and a situational prediction model, and predicting the future network situation using the situational analysis model based on the current network feature type includes: evaluating the current network situation information by utilizing a situation evaluation model according to the current network feature type; and predicting the future network situation by using a situation prediction model according to the current network situation information.
In some embodiments, evaluating the current network situational information using the situational assessment model based on the current network feature type includes: extracting at least one of the current network type, the risk factor and the situation index as an evaluation parameter by using a situation evaluation model according to the current network characteristic type; and evaluating the current network situation information by using a situation evaluation model according to the evaluation parameters.
In some embodiments, the method further comprises: determining a feedback correction value according to the predicted future network situation and the actual future network situation; and correcting the situation evaluation model by using the feedback correction value.
In some embodiments, the method further comprises: updating a network alarm strategy by utilizing a decision updating model according to the predicted future network situation; and alarming according to the updated network alarm strategy.
In some embodiments, the situational prediction model is generated from a random forest model.
In some embodiments, the current network characteristic information includes at least one of alarm information, network performance information, network element device information, network topology information, work order processing information.
According to other embodiments of the present disclosure, there is provided a network situation prediction apparatus, including: the characteristic classification unit is used for determining the type of the current network characteristic by utilizing a classification model according to the acquired current network characteristic information; and the situation analysis unit is used for predicting the future network situation by utilizing a situation analysis model based on machine learning according to the current network feature type.
In some embodiments, the feature classification unit determines the network feature type by using a machine learning-based classification model according to at least one of a network performance type which can be influenced by the current network feature information and an influence degree on a network situation.
In some embodiments, the situation analysis model includes a situation assessment model and a situation prediction model, and the situation analysis unit assesses current network situation information using the situation assessment model according to the current network feature type and predicts future network situations using the situation prediction model according to the current network situation information.
In some embodiments, the situation analysis unit extracts at least one of the current network type, the risk factor and the situation index as an evaluation parameter by using the situation evaluation model according to the current network feature type, and evaluates the current network situation information by using the situation evaluation model according to the evaluation parameter.
In some embodiments, the situation analysis unit determines a feedback correction value according to the predicted future network situation and the actual future network situation, and corrects the situation evaluation model by using the feedback correction value.
In some embodiments, further comprising: and the alarm unit is used for updating the network alarm strategy by utilizing the decision updating model according to the predicted future network situation and giving an alarm according to the updated network alarm strategy.
In some embodiments, the situational prediction model is generated from a random forest model.
In some embodiments, the current network characteristic information includes at least one of alarm information, network performance information, network element device information, network topology information, work order processing information.
According to still other embodiments of the present disclosure, there is provided a device for predicting a network situation, including: a memory; and a processor coupled to the memory, the processor configured to perform the method for predicting network posture in any of the above embodiments based on instructions stored in the memory device.
According to still further embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of predicting a network situation in any of the above embodiments.
In the above embodiment, the current network feature information is classified based on artificial intelligence, and the network situation is analyzed. Therefore, the network situation can be predicted based on the simplified network characteristics under the condition of not being influenced by human factors, and the situation prediction accuracy is improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:
FIG. 1 illustrates a flow diagram of some embodiments of a prediction method of network posture of the present disclosure;
FIG. 2 illustrates a flow diagram of some embodiments of step 120 in FIG. 1;
FIG. 3 illustrates a schematic diagram of some embodiments of a prediction method of network posture of the present disclosure;
FIG. 4 illustrates a block diagram of some embodiments of a prediction apparatus of network posture of the present disclosure;
FIG. 5 illustrates a block diagram of further embodiments of a prediction apparatus of network posture of the present disclosure;
fig. 6 illustrates a block diagram of still further embodiments of a prediction apparatus of network posture of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In order to solve the technical problems, the network security situation assessment technical scheme based on artificial intelligence and big data is provided by combining the construction of an intelligent command and scheduling system. The network security element acquisition (such as network flow, host log, application access record and the like), understanding, prediction and situation assessment can be realized in a large-scale network environment so as to mine potential threats and avoid risks in advance. The method has important significance for realizing final reasonable decision and action. For example, the technical solution of the present disclosure can be realized by the following embodiments.
Fig. 1 illustrates a flow diagram of some embodiments of a prediction method of network posture of the present disclosure.
As shown in fig. 1, the method includes: step 110, determining the current network characteristic type; and step 120, predicting future network situation.
In step 110, according to the obtained current network feature information (network situation element), a classification model is used to determine a current network feature type.
For example, current network characteristic information may be obtained from network traffic, host logs, application access records, and the like. The current network characteristic information comprises at least one of alarm information, network performance information, network element equipment information, network topology information and work order processing information.
In some embodiments, the network feature type is determined using a machine learning based classification model based on at least one of a network performance type that can be affected by current network feature information or a degree of impact on a network situation.
For example, the alarm information monitored by the alarm information monitoring module is "2993 BTS AND TC UNSYNCHRONIZATION CLEAR CALLS ON ABIS INTERFACE 24d 10d 07120 d 20d 6 d".
The alarm influence degree is an alarm at a BSC (Base Station Controller) level, and the network performance type mainly influenced is a call drop rate. The alarm information may be classified according to the degree of influence and the affected network performance.
For example, the types of network performance affected may also include bandwidth occupancy, call-on rate, call-off rate, device utilization, network failure rate, and the like.
In step 120, a future network situation is predicted using a machine learning based situation analysis model based on the current network feature type.
In some embodiments, the situational analysis model includes a situational assessment model and a situational prediction model. Step 120 may be implemented, for example, by the embodiment in fig. 2.
Fig. 2 illustrates a flow diagram of some embodiments of step 120 in fig. 1.
As shown in fig. 2, step 120 may include: step 1210, evaluating current network situation information; and step 1220, predicting future network situations.
In step 1210, current network situation information is evaluated using the situation evaluation model based on the current network feature type.
In some embodiments, at least one of the current network type, the risk factor and the situation index is extracted as an evaluation parameter by using a situation evaluation model according to the current network feature type; and evaluating the current network situation information by using a situation evaluation model according to the evaluation parameters.
For example, the current network situation information may be information that can reflect changes in network performance, such as an increasing call drop rate and a decreasing device utilization rate.
In step 1220, a future network situation is predicted using the situation prediction model based on the current network situation information.
In some embodiments, the situation assessment model and the situation prediction model may be machine learning models such as LSTM (Long Short-Term Memory network). For example, an LSTM regression network may be created using network key indicators (situation assessment results, current network feature information, etc.), the network situation may be predicted by combining a deep learning algorithm, and the network state may be updated by using the observed values. The network situation assessment process needs to take into account network tolerance limits and network complexity.
In some embodiments, the feedback modification value is determined based on the predicted future network situation and the actual future network situation; and correcting the situation evaluation model by using the feedback correction value.
In some embodiments, the network alarm policy is updated using a decision update model based on the predicted future network situation; and alarming according to the updated network alarm strategy. For example, the decision update model is generated from a decision tree model.
In some embodiments, if the prediction result is that the call drop rate index exceeds 1.1%, a link alarm is generated; if the bandwidth utilization rate exceeds 75%, an expansion processing alarm is generated; and if the bandwidth utilization rate exceeds 95% as a prediction result, generating a service interruption avoiding alarm. For example, the device utilization rate is used to monitor the device port bandwidth and CPU resource occupation, and generate an alarm when congestion occurs.
Fig. 3 illustrates a schematic diagram of some embodiments of a prediction method of network posture of the present disclosure.
As shown in fig. 3, a big data based security situation awareness system may be constructed as a prediction apparatus according to the prediction method of the present disclosure. For example, the prediction device may include a security situation element extraction module, an artificial-based energy-only analysis module (feature classification unit), a network security situation evaluation module (situation analysis unit), a network security situation prediction module (situation analysis unit), a network security situation alarm module (alarm unit), and a network security situation storage module.
In some embodiments, the network situation element extraction module is configured to obtain the network situation element. For example, the network situation elements mainly include comprehensive alarm information (such as an alarm name, an alarm time, an alarm type, a processing state, and an alarm level), network performance information (such as uplink and downlink traffic, a central processing unit utilization rate, jitter, a time delay, and a packet loss rate), network element device information (such as a circuit resource, an IP address resource, a board resource, and a logic resource), network topology information (topology, network segment network element interface information, link information), work order processing information, and the like.
In some embodiments, the artificial intelligence analysis module classifies the network situational elements using the advantages of artificial intelligence techniques to classify the data.
For example, the network situation elements may be subjected to self-organizing feature mapping according to the degree of influence of the network situation elements on the network situation, the type of network performance affected, and the like. Therefore, the classification of the network situation elements is realized.
In some embodiments, the network situation assessment module performs risk assessment on the network situation according to the classification of the network situation elements. For example, for different network elements, 3 types of network features can be extracted as evaluation parameters: network type, risk factor, situational index. And evaluating the network situation according to the extracted network characteristics.
For example, the evaluation results may be fed back to the artificial intelligence analysis module to modify its parameters and algorithms.
In some embodiments, the network security situation prediction module predicts the network situation trend according to the historical information of the network situation (the evaluation result of the network situation evaluation module) in combination with a prediction algorithm of artificial intelligence (such as a random forest algorithm).
For example, the predicted results may be fed back to the network situation assessment module to modify its parameters and algorithms.
In some embodiments, the network situation warning module determines or updates a corresponding network warning strategy through the constructed decision tree model according to the network situation prediction result. Therefore, the network situation alarm is carried out on the possible existence of the network security condition.
For example, the network situation prediction module may be fed back with the network alarm policy to modify its parameters and algorithms.
Therefore, by adding the information feedback mechanism, the bidirectional confirmation process of the information is realized, and the accuracy of situation prediction is improved.
In some embodiments, the network situation storage module serves two purposes: the storage of the network situation information is realized; and the verification of the network prediction module is realized, and the main parameters and the prediction algorithm of network situation evaluation are modified by combining the actual network situation evaluation information.
In the embodiment, the situation analysis module based on artificial intelligence realizes comprehensive and accurate analysis on the network; accurate evaluation and feedback correction of the network situation are realized through a network situation prediction module; by using prediction algorithms such as random forests and the like, the prediction accuracy of the network security situation is improved, and the large-scale network situation which cannot be judged by people is possible; the artificial intelligent self-organizing feature extraction and classification reduces the complexity of network element data and realizes the extraction of network features.
Fig. 4 illustrates a block diagram of some embodiments of a prediction apparatus of network posture of the present disclosure.
As shown in fig. 4, the network situation prediction apparatus 4 includes a feature classification unit 41 and a situation analysis unit 42.
The feature classification unit 41 determines the current network feature type by using a classification model according to the acquired current network feature information.
The situation analysis unit 42 predicts the future network situation by using a situation analysis model based on machine learning according to the current network feature type.
In some embodiments, the feature classification unit 41 determines the network feature type by using a machine learning-based classification model according to at least one of a network performance type that can be influenced by the current network feature information and a degree of influence on the network situation.
In some embodiments, the situation analysis model includes a situation assessment model and a situation prediction model, and the situation analysis unit assesses current network situation information using the situation assessment model according to the current network feature type and predicts future network situations using the situation prediction model according to the current network situation information.
In some embodiments, the situation analysis unit 42 extracts at least one of the current network type, the risk factor, and the situation index as an evaluation parameter by using the situation evaluation model according to the current network feature type, and evaluates the current network situation information by using the situation evaluation model according to the evaluation parameter.
In some embodiments, the situation analysis unit 42 determines feedback corrections based on the predicted future network situation and the actual future network situation, and corrects the situation assessment model using the feedback corrections.
In some embodiments, the predicting device 4 of the network situation further includes: the alarm unit 43 is configured to update the network alarm policy by using the decision update model according to the predicted future network situation, and perform an alarm according to the updated network alarm policy.
In some embodiments, the situational prediction model is generated from a random forest model.
In some embodiments, the current network characteristic information includes at least one of alarm information, network performance information, network element device information, network topology information, work order processing information.
Fig. 5 illustrates a block diagram of further embodiments of a prediction apparatus of network posture of the present disclosure.
As shown in fig. 5, the network situation prediction apparatus 5 of this embodiment includes: a memory 51 and a processor 52 coupled to the memory 51, the processor 52 being configured to execute a prediction method of network situation in any one of the embodiments of the present disclosure based on instructions stored in the memory 51.
The memory 51 may include, for example, a system memory, a fixed nonvolatile storage medium, and the like. The system memory stores, for example, an operating system, application programs, a boot loader, a database, and other programs.
Fig. 6 illustrates a block diagram of still further embodiments of a prediction apparatus of network posture of the present disclosure.
As shown in fig. 6, the network situation prediction apparatus 6 of this embodiment includes: a memory 610 and a processor 620 coupled to the memory 610, the processor 620 being configured to perform a method of predicting a network situation in any of the above embodiments based on instructions stored in the memory 610.
The memory 610 may include, for example, system memory, fixed non-volatile storage media, and the like. The system memory stores, for example, an operating system, an application program, a boot loader, and other programs.
The network situation prediction device 6 may further include an input/output interface 630, a network interface 640, a storage interface 650, and the like. These interfaces 630, 640, 650 and the connections between the memory 610 and the processor 620 may be through a bus 660, for example. The input/output interface 630 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, a touch screen, a microphone, and a sound box. The network interface 640 provides a connection interface for various networking devices. The storage interface 650 provides a connection interface for external storage devices such as an SD card and a usb disk.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media having computer-usable program code embodied therein.
So far, the prediction method of the network situation, the prediction apparatus of the network situation, and the nonvolatile computer readable storage medium according to the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (18)
1. A method of predicting a network situation, comprising:
determining the current network characteristic type by utilizing a classification model according to the acquired current network characteristic information;
and predicting the future network situation by utilizing a situation analysis model based on machine learning according to the current network feature type.
2. The prediction method according to claim 1, wherein the determining the current network feature type using the classification model according to the obtained current network feature information comprises:
and determining the network characteristic type by utilizing the machine learning-based classification model according to at least one of the network performance type and the influence degree on the network situation, which can be influenced by the current network characteristic information.
3. The prediction method according to claim 1,
the situation analysis model comprises a situation evaluation model and a situation prediction model,
the predicting the future network situation by using a situation analysis model according to the current network feature type comprises the following steps:
evaluating the current network situation information by utilizing the situation evaluation model according to the current network feature type;
and predicting the future network situation by utilizing the situation prediction model according to the current network situation information.
4. The prediction method of claim 3, wherein the evaluating current network situational information using the situational assessment model based on the current network feature type comprises:
extracting at least one of the current network type, the risk factor and the situation index as an evaluation parameter by utilizing the situation evaluation model according to the current network feature type;
and evaluating the current network situation information by utilizing the situation evaluation model according to the evaluation parameters.
5. The prediction method of claim 3, further comprising:
determining a feedback correction value according to the predicted future network situation and the actual future network situation;
and correcting the situation evaluation model by using the feedback correction value.
6. The prediction method according to claim 1, further comprising:
updating a network alarm strategy by utilizing a decision updating model according to the predicted future network situation;
and alarming according to the updated network alarm strategy.
7. The prediction method according to claim 3,
the situation prediction model is generated according to a random forest model.
8. The prediction method according to any one of claims 1 to 7,
the current network characteristic information comprises at least one of alarm information, network performance information, network element equipment information, network topology information and work order processing information.
9. An apparatus for predicting a network situation, comprising:
the characteristic classification unit is used for determining the type of the current network characteristic by utilizing a classification model according to the acquired current network characteristic information;
and the situation analysis unit is used for predicting the future network situation by utilizing a situation analysis model based on machine learning according to the current network feature type.
10. The prediction apparatus according to claim 9,
and the feature classification unit determines the network feature type by utilizing the classification model based on machine learning according to at least one of the network performance type which can be influenced by the current network feature information and the influence degree on the network situation.
11. The prediction apparatus according to claim 9,
the situation analysis model comprises a situation evaluation model and a situation prediction model,
and the situation analysis unit evaluates current network situation information by using the situation evaluation model according to the current network feature type, and predicts the future network situation by using the situation prediction model according to the current network situation information.
12. The prediction apparatus according to claim 11, wherein the situation analysis unit extracts at least one of a current network type, a risk factor, and a situation index as an evaluation parameter using the situation evaluation model according to the current network feature type, and evaluates the current network situation information using the situation evaluation model according to the evaluation parameter.
13. The prediction apparatus according to claim 11,
and the situation analysis unit determines a feedback correction value according to the predicted future network situation and the actual future network situation, and corrects the situation evaluation model by using the feedback correction value.
14. The prediction apparatus of claim 9, further comprising:
and the alarm unit is used for updating the network alarm strategy by utilizing the decision updating model according to the predicted future network situation and giving an alarm according to the updated network alarm strategy.
15. The prediction apparatus according to claim 11,
the situation prediction model is generated according to a random forest model.
16. The prediction apparatus according to any one of claims 9 to 15,
the current network characteristic information comprises at least one of alarm information, network performance information, network element equipment information, network topology information and work order processing information.
17. An apparatus for predicting a network situation, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of predicting network posture of any of claims 1-8 based on instructions stored in the memory.
18. A non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of predicting a network situation of any one of claims 1-8.
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