CN114938336A - Method and system for classifying network operation information - Google Patents
Method and system for classifying network operation information Download PDFInfo
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- CN114938336A CN114938336A CN202110150860.3A CN202110150860A CN114938336A CN 114938336 A CN114938336 A CN 114938336A CN 202110150860 A CN202110150860 A CN 202110150860A CN 114938336 A CN114938336 A CN 114938336A
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/088—Non-supervised learning, e.g. competitive learning
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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
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
Abstract
The embodiment of the invention provides a method and a system for classifying network operation information, wherein the method comprises the steps of respectively extracting network operation label classification characteristics of each first network operation information and network operation label classification characteristics of each second network operation information through a deep classification model, respectively using each second network operation information as target network operation information, calculating the characteristic correlation degree of the network operation label classification characteristics and the network operation label classification characteristics of each first network operation information, further determining the associated time sequence characteristic information of each second network operation information, screening the second network operation information, and using the screened second network operation information and the first network operation information together for model training, so that the accuracy of classification of the network operation information can be improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for classifying network operation information.
Background
How to improve the accuracy of classification of network operation information is a technical problem to be urgently solved in the field.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and a system for classifying network operation information, which can improve accuracy of classification of network operation information.
According to an aspect of an embodiment of the present invention, a method for classifying network operation information is provided, where the method includes:
acquiring a network operation information list; the network operation information list comprises first network operation information and second network operation information; the number of the first network operation information and the number of the second network operation information are more than one;
respectively extracting the network operation label classification characteristics of the first network operation information and the network operation label classification characteristics of the second network operation information through a deep classification model; the deep classification model is obtained according to the first network operation information through unsupervised learning;
respectively taking each piece of second network operation information as target network operation information, calculating the feature correlation degree of the network operation label classification feature of the target network operation information and the network operation label classification feature of each piece of first network operation information, and taking the feature correlation degree as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information; the dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information; the classification reference strength of the classification reference strength characteristic is inversely proportional to the frequent access level of the second network operation information;
determining the associated time sequence characteristic information of each second network operation information according to the characteristic correlation degree of each second network operation information on the network operation label classification characteristic; or, respectively taking each second network operation information as target network operation information, calculating the feature correlation degree between the frequent access feature vector of the target network operation information and the frequent access feature vector of each second network operation information, determining a classification reference parameter according to the feature correlation degree, and obtaining an associated time sequence feature information vector corresponding to the target network operation information; the dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information; the classification reference strength of the associated time sequence characteristic information vector is in direct proportion to the frequent access level of the second network operation information;
and screening the second network operation information based on the frequent access characteristic vector and the associated time sequence characteristic information of the second network operation information, wherein the screened second network operation information and the first network operation information are jointly used for model training.
According to another aspect of the present application, there is provided a classification system of network operation information, the system including:
the acquisition module is used for acquiring a network operation information list; the network operation information list comprises first network operation information and second network operation information; the number of the first network operation information and the number of the second network operation information are more than one;
the extraction module is used for respectively extracting the network operation label classification characteristics of the first network operation information and the network operation label classification characteristics of the second network operation information through a deep classification model; the deep classification model is obtained according to the first network operation information through unsupervised learning;
the calculation module is used for respectively taking each piece of second network operation information as target network operation information, calculating the feature correlation degree of the network operation label classification feature of the target network operation information and the network operation label classification feature of each piece of first network operation information, and taking the feature correlation degree as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information; the dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information; the classification reference strength of the classification reference strength characteristic is inversely proportional to the frequent access level of the second network operation information;
the determining module is used for determining the associated time sequence characteristic information of each piece of second network operation information according to the characteristic correlation degree of each piece of second network operation information on the network operation label classification characteristic; or, respectively taking each second network operation information as target network operation information, calculating the feature correlation degree between the frequently-accessed feature vector of the target network operation information and the frequently-accessed feature vector of each second network operation information, determining a classification reference parameter according to the feature correlation degree, and obtaining an associated time sequence feature information vector corresponding to the target network operation information; the dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information; the classification reference strength of the associated time sequence characteristic information vector is in direct proportion to the frequent access level of the second network operation information;
and the screening module is used for screening the second network operation information based on the frequent access characteristic vector and the associated time sequence characteristic information of the second network operation information, and the screened second network operation information and the first network operation information are jointly used for model training.
Compared with the prior art, the method and the system for classifying network operation information provided by the embodiment of the invention respectively extract the network operation label classification features of each first network operation information and the network operation label classification features of each second network operation information through a deep classification model, respectively take each second network operation information as target network operation information, calculate the feature correlation degree of the network operation label classification features of the second network operation information and the network operation label classification features of each first network operation information, further determine the correlation time sequence feature information of each second network operation information, and thus screen the second network operation information, and the screened second network operation information and the first network operation information are commonly used for model training, so that the accuracy of classification of the network operation information can be improved.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 illustrates a component diagram of a server provided by an embodiment of the invention;
fig. 2 is a flowchart illustrating a method for classifying network operation information according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of a classification system for network operation information according to an embodiment of the present invention.
Detailed Description
In order to make the technical solution better understood by the scholars in the technical field, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, but not all of the embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the scientific and technical project objects so used may be interchanged under appropriate circumstances such that embodiments of the invention described herein may be implemented in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 is a flowchart illustrating a method for classifying network operation information according to an embodiment of the present invention, where the method for classifying network operation information is executable by the server 100 shown in fig. 1, and the detailed steps of the method for classifying network operation information are described as follows.
Step S110, a network operation information list is obtained. The network operation information list includes first network operation information and second network operation information. The number of the first network operation information and the number of the second network operation information are both more than one.
Step S120, respectively extracting, by using a deep classification model, a network operation label classification feature of each first network operation information and a network operation label classification feature of each second network operation information. And the deep classification model is obtained according to the first network operation information through unsupervised learning.
Step S130, respectively taking each second network operation information as target network operation information, calculating a feature correlation degree between the network operation label classification feature of the target network operation information and the network operation label classification feature of each first network operation information, and taking the feature correlation degree as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information. The dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information. The classification reference strength of the classification reference strength characteristic is inversely proportional to a frequent access level of the second network operation information.
Step S140, determining the associated time sequence feature information of each second network operation information according to the feature correlation degree between the second network operation information on the network operation label classification feature. Or, each piece of second network operation information is used as target network operation information, the feature correlation degree between the frequently-accessed feature vector of the target network operation information and the frequently-accessed feature vector of each piece of second network operation information is calculated, and a classification reference parameter is determined according to the feature correlation degree to obtain the associated time sequence feature information vector corresponding to the target network operation information. The dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information. The classification reference strength of the associated time series characteristic information vector is in direct proportion to the frequent access level of the second network operation information.
Step S150, screening the second network operation information based on the frequent access characteristic vector and the associated time sequence characteristic information of the second network operation information, wherein the screened second network operation information and the first network operation information are jointly used for model training.
Based on the steps, the network operation label classification characteristics of each first network operation information and the network operation label classification characteristics of each second network operation information are respectively extracted through a deep classification model, each second network operation information is respectively used as target network operation information, the characteristic correlation degree of the network operation label classification characteristics of the second network operation information and the network operation label classification characteristics of each first network operation information is calculated, the correlation time sequence characteristic information of each second network operation information is further determined, the second network operation information is screened, the screened second network operation information and the first network operation information are jointly used for model training, and the accuracy of classification of the network operation information can be improved.
Fig. 3 shows a functional block diagram of a classification system 200 for network operation information according to an embodiment of the present invention, where the functions implemented by the classification system 200 for network operation information may correspond to the steps performed by the foregoing method. The classification system 200 of network operation information may be understood as the server 100 or a processor of the server 100, or may be understood as a component that is independent from the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of each functional module of the classification system 200 of network operation information are described in detail below.
The obtaining module 210 is configured to obtain a network operation information list. The network operation information manifest includes first network operation information and second network operation information. The number of the first network operation information and the number of the second network operation information are both more than one.
An extracting module 220, configured to extract, through a deep classification model, a network operation label classification feature of each first network operation information and a network operation label classification feature of each second network operation information respectively. And the deep classification model is obtained according to the first network operation information through unsupervised learning.
A calculating module 230, configured to take each piece of second network operation information as target network operation information, calculate a feature correlation between a network operation label classification feature of the target network operation information and a network operation label classification feature of each piece of first network operation information, and take the feature correlation as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information. The dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information. The classification reference strength of the classification reference strength characteristic is inversely proportional to a frequent access level of the second network operation information.
A determining module 240, configured to determine, according to a feature correlation degree between the second network operation information on a network operation label classification feature, associated timing feature information of each second network operation information. Or, each piece of second network operation information is used as target network operation information, the feature correlation degree between the frequently-accessed feature vector of the target network operation information and the frequently-accessed feature vector of each piece of second network operation information is calculated, and a classification reference parameter is determined according to the feature correlation degree to obtain the associated time sequence feature information vector corresponding to the target network operation information. The dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information. The classification reference strength of the associated timing characteristic information vector is proportional to the frequent access level of the second network operation information.
And a screening module 250, configured to screen the second network operation information based on the frequent access feature vector and the associated timing sequence feature information of the second network operation information, where the screened second network operation information and the first network operation information are jointly used for model training.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data presentation object to another website, computer, server, or data presentation object by wire (e.g., coaxial cable, fiber optics, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device that includes one or more available media, a server, a data presentation object, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (2)
1. A method for classifying network operation information, the method comprising:
acquiring a network operation information list; the network operation information list comprises first network operation information and second network operation information; the number of the first network operation information and the number of the second network operation information are more than one;
respectively extracting the network operation label classification characteristics of the first network operation information and the network operation label classification characteristics of the second network operation information through a deep classification model; the deep classification model is obtained according to the first network operation information through unsupervised learning;
respectively taking each piece of second network operation information as target network operation information, calculating the feature correlation degree of the network operation label classification feature of the target network operation information and the network operation label classification feature of each piece of first network operation information, and taking the feature correlation degree as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information; the dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information; the classification reference strength of the classification reference strength characteristic is inversely proportional to the frequent access level of the second network operation information;
determining the associated time sequence characteristic information of each second network operation information according to the characteristic correlation degree of each second network operation information on the network operation label classification characteristic; or, respectively taking each second network operation information as target network operation information, calculating the feature correlation degree between the frequent access feature vector of the target network operation information and the frequent access feature vector of each second network operation information, determining a classification reference parameter according to the feature correlation degree, and obtaining an associated time sequence feature information vector corresponding to the target network operation information; the dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information; the classification reference strength of the associated time sequence characteristic information vector is in direct proportion to the frequent access level of the second network operation information;
and screening the second network operation information based on the frequent access characteristic vector and the associated time sequence characteristic information of the second network operation information, wherein the screened second network operation information and the first network operation information are jointly used for model training.
2. A system for classifying network operation information, the system comprising:
the acquisition module is used for acquiring a network operation information list; the network operation information list comprises first network operation information and second network operation information; the number of the first network operation information and the number of the second network operation information are more than one;
the extraction module is used for respectively extracting the network operation label classification characteristics of the first network operation information and the network operation label classification characteristics of the second network operation information through a deep classification model; the deep classification model is obtained according to the first network operation information through unsupervised learning;
the calculation module is used for respectively taking each piece of second network operation information as target network operation information, calculating the feature correlation degree of the network operation label classification feature of the target network operation information and the network operation label classification feature of each piece of first network operation information, and taking the feature correlation degree as a classification reference parameter to obtain a classification reference strength feature corresponding to the target network operation information; the dimension index of the classification reference strength characteristic is the same as the quantity of the first network operation information; the classification reference strength of the classification reference strength characteristic is inversely proportional to the frequent access level of the second network operation information;
the determining module is used for determining the associated time sequence characteristic information of each piece of second network operation information according to the characteristic correlation degree of each piece of second network operation information on the network operation label classification characteristic; or, respectively taking each second network operation information as target network operation information, calculating the feature correlation degree between the frequently-accessed feature vector of the target network operation information and the frequently-accessed feature vector of each second network operation information, determining a classification reference parameter according to the feature correlation degree, and obtaining an associated time sequence feature information vector corresponding to the target network operation information; the dimension index of the associated time sequence characteristic information vector is the same as the quantity of the second network operation information; the classification reference strength of the associated time sequence characteristic information vector is in direct proportion to the frequent access level of the second network operation information;
and the screening module is used for screening the second network operation information based on the frequent access characteristic vector and the associated time sequence characteristic information of the second network operation information, and the screened second network operation information and the first network operation information are jointly used for model training.
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| CN202110150860.3A CN114938336A (en) | 2021-02-04 | 2021-02-04 | Method and system for classifying network operation information |
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