CN114462679A - Network traffic prediction method, device, equipment and medium based on deep learning - Google Patents

Network traffic prediction method, device, equipment and medium based on deep learning Download PDF

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CN114462679A
CN114462679A CN202210003743.9A CN202210003743A CN114462679A CN 114462679 A CN114462679 A CN 114462679A CN 202210003743 A CN202210003743 A CN 202210003743A CN 114462679 A CN114462679 A CN 114462679A
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杜翠凤
何独一
蒋仕宝
罗春艳
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GCI Science and Technology Co Ltd
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Abstract

The invention relates to the technical field of communication, and discloses a network traffic prediction method, a device, equipment and a medium based on deep learning, wherein the method comprises the steps of obtaining network traffic data; preprocessing the network traffic data to obtain traffic prediction data; performing feature extraction on the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain a time feature and a space feature; and fusing the time characteristics and the space characteristics according to the multi-mode attention mechanism network to obtain a network flow prediction result. The method can reduce the error of the prediction result and improve the accuracy of the prediction result.

Description

Network traffic prediction method, device, equipment and medium based on deep learning
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a medium for predicting network traffic based on deep learning.
Background
The network flow is an important index for measuring the running state of the whole network, and the framework of a network flow prediction model is carried out aiming at the characteristics of the network flow, so that the performance of various performances on the network can be better understood, various congestions of the network can be early warned in advance, and the service quality of the network can be guaranteed.
At present, there are many methods for predicting network traffic, including neural networks, trend prediction, and the like. Although the above method reflects the characteristics of the traffic to some extent, since the above method does not process the characteristics of the traffic data, when the traffic is in a short-time burst state, the neural network is difficult to recognize the traffic data, and the prediction result has a large error.
Disclosure of Invention
The invention provides a network traffic prediction method, a device, equipment and a medium based on deep learning, which are used for reducing errors of prediction results and improving the accuracy of the prediction results.
In a first aspect, to solve the above technical problem, the present invention provides a method for predicting network traffic based on deep learning, including:
acquiring network flow data;
preprocessing the network traffic data to obtain traffic prediction data;
performing feature extraction on the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain a time feature and a space feature;
and fusing the time characteristics and the space characteristics according to the multi-mode attention mechanism network to obtain a network flow prediction result.
Preferably, before the acquiring network traffic data, the method further comprises:
setting different acquisition periods according to the data types;
and acquiring corresponding data based on the acquisition period.
Preferably, the preprocessing the network traffic data to obtain traffic prediction data includes:
segmenting the network traffic data to obtain a pre-training sequence;
obtaining observation time and prediction advance time according to the acquisition period and a preset time step;
and obtaining flow prediction data based on the pre-training sequence, the observation time and the prediction advance time.
Preferably, the fusing the temporal feature and the spatial feature according to the multi-modal attention mechanism network to obtain a network traffic prediction result includes:
performing weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time features and the space features;
and fusing the time characteristic and the space characteristic according to the weight value to obtain a network flow prediction result.
Preferably, the method further comprises:
constructing a loss function according to the Tanh activation function, the weight value, the time characteristic and the space characteristic;
adjusting the weight value based on a minimization of the loss function.
Preferably, the network traffic data comprises trend data, periodic data and proximity data.
In a second aspect, the present invention provides a deep learning-based network traffic prediction apparatus, including:
the data acquisition module is used for acquiring network flow data;
the preprocessing module is used for preprocessing the network traffic data to obtain traffic prediction data;
the characteristic extraction module is used for extracting the characteristics of the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain time characteristics and space characteristics;
and the characteristic fusion module is used for fusing the time characteristic and the spatial characteristic according to the multi-mode attention mechanism network to obtain a network flow prediction result.
Preferably, the feature fusion module comprises:
the weight extraction unit is used for carrying out weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time characteristics and the space characteristics;
and the feature fusion unit is used for fusing the time feature and the spatial feature according to the weight value to obtain a network traffic prediction result.
In a third aspect, the present invention further provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the deep learning based network traffic prediction method described in any one of the above when executing the computer program.
In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the deep learning based network traffic prediction method described in any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
by pre-training the network flow data, the fluctuation behavior of the flow data can be ironed to a great extent, the influence of random factors on the flow data is reduced, and the development direction of the data is closer to a real development track; meanwhile, feature extraction is carried out on the flow prediction data, then features are fused, in the fusion process, the multi-mode attention mechanism network is adopted to measure the influence weight of the time features and the space features, and finally network flow prediction is achieved.
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Fig. 1 is a schematic flowchart of a deep learning-based network traffic prediction method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of the results of the pre-processing provided by the embodiment of the present invention;
FIG. 3 is a diagram illustrating a prediction process for a predicted value according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a prediction process for 8 predicted values according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a deep learning-based network traffic prediction apparatus according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, a first embodiment of the present invention provides a deep learning-based network traffic prediction method, including the following steps:
s11, acquiring network flow data;
s12, preprocessing the network traffic data to obtain traffic prediction data;
s13, extracting the characteristics of the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain time characteristics and space characteristics;
and S14, fusing the time characteristic and the spatial characteristic according to the multi-mode attention mechanism network to obtain a network flow prediction result.
In step S11, the network traffic data includes trend data, periodic data and proximity data, different acquisition periods need to be set according to data types, and then corresponding data is acquired based on the acquisition periods.
Specifically, before data acquisition, a working day and a resting day should be distinguished before data is sampled. The time acquisition cycle of the trend data is 1 week, so that the trend change condition of the data in the area is reflected; the time acquisition period of the periodic data is 1 day, so that the periodic change condition of the flow data is reflected; the acquisition period of the proximity data is 1 hour, so that the data change conditions of different adjacent times are reflected. Further, the finally obtained network traffic data comprises different time scales.
In step S12, preprocessing the network traffic data to obtain traffic prediction data includes:
segmenting the network traffic data to obtain a pre-training sequence;
obtaining observation time and prediction advance time according to the acquisition period and a preset time step;
and obtaining flow prediction data based on the pre-training sequence, the observation time and the prediction advance time.
Specifically, referring to fig. 2, fig. 2 is a pre-training sequence. For realizing data preprocessing, the data is segmented and acquired at Ts-(To+Ta) Starting until TsIn which T isoRepresenting history data, T, which can be directly acquiredsIndicating the predicted time of day. The historical data is divided into "coded" data and "predicted" data, the quantity between the "predicted" data being affected by different time steps T and time windows a to be advanced, the smaller the quantity of "predicted" data, T, if the steps and time windows advanced are largeraTo predict the lead time, T is indicated to be leadaThe moment only starts to predict TsThe data value of the time of day.
Further, if the step length T is set to 14, the observation time length is ToWhere α T is 14 α, the observation time is 6 α, and the advance time T is predictedaIf the size of α needs to be determined according to the different acquisition periods, then 8 α flow prediction data can be generated according to the observation time. For example, the predicted value of t ═ 7 is shown in fig. 3, and the predicted values of 8 to 14 are shown in fig. 4.
It should be noted that the pre-training sequence is completed, and the historical data is divided into "coding" data and "prediction" data, the "coding" data is used for summarizing the past flow characteristics, and the "prediction" data is used for reasoning what will happen in the future. Under the influence of different time steps, the number of predicted values of the network traffic data is more than that. The pre-training of the "predictive" data sequence is achieved by the fusion of multiple predictive data of the "predictive" data. The pre-training flattens the fluctuation behavior of the flow data to a great extent, reduces the influence of random factors on the flow data, and enables the development direction of the data to be closer to a real development track. Preferably, the pre-training sequence can be put into a long-short term memory network, and multi-scale prediction of the flow data is realized through different iteration times according to different positions of the pre-training sequence.
In step S13, feature extraction is performed on the traffic prediction data based on the convolutional neural network and the long-short term memory network, so as to obtain temporal features and spatial features. Among them, the Convolutional Neural Network (CNN) is good at capturing spatial relationships, and the Long Short-Term Memory Network (LSTM) is good at capturing temporal dependencies, thereby generating temporal and spatial features of trend data, periodic data and proximity data.
In step S14, the fusing the temporal features and the spatial features according to the multi-modal attention mechanism network to obtain a network traffic prediction result, including:
performing weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time features and the space features;
and fusing the time characteristic and the space characteristic according to the weight value to obtain a network flow prediction result.
Wherein, the process of the Tanh activation function is as follows:
Figure BDA0003454621960000061
Figure BDA0003454621960000062
in order to predict the outcome of the network traffic,w1、w2weight values, f, for temporal and spatial features, respectivelyCNNMeans spatial characteristics after splicing; f. ofLSTMRefers to the temporal characteristics after splicing.
Further, constructing a loss function according to the Tanh activation function, the weight value, the time characteristic and the space characteristic;
adjusting the weight value based on a minimization of the loss function.
Specifically, the loss function is:
Figure BDA0003454621960000063
in this embodiment, a multi-modal Attention mechanism network (modular Attention Net) is used to fuse the temporal features and the spatial features, and adaptive adjustment of three types of data and two types of feature weights is realized by combining errors.
By pre-training the network flow data, the fluctuation behavior of the flow data can be ironed to a great extent, the influence of random factors on the flow data is reduced, and the development direction of the data is closer to a real development track; meanwhile, feature extraction is carried out on the flow prediction data, then features are fused, in the fusion process, the multi-mode attention mechanism network is adopted to measure the influence weight of the time features and the space features, and finally network flow prediction is achieved.
Referring to fig. 5, a second embodiment of the present invention provides a deep learning-based network traffic prediction apparatus, including:
the data acquisition module is used for acquiring network flow data;
the preprocessing module is used for preprocessing the network traffic data to obtain traffic prediction data;
the characteristic extraction module is used for extracting the characteristics of the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain time characteristics and space characteristics;
and the characteristic fusion module is used for fusing the time characteristic and the spatial characteristic according to the multi-mode attention mechanism network to obtain a network flow prediction result.
Preferably, the apparatus further comprises:
the period setting module is used for setting different acquisition periods according to the data types;
and the data acquisition module is used for acquiring corresponding data based on the acquisition period.
Preferably, the preprocessing module comprises:
the data segmentation unit is used for segmenting the network traffic data to obtain a pre-training sequence;
the time obtaining module is used for obtaining observation time and prediction advance time according to the acquisition period and a preset time step;
and the preprocessing unit is used for obtaining flow prediction data based on the pre-training sequence, the observation time and the prediction advance time.
Preferably, the feature fusion module comprises:
the weight extraction unit is used for carrying out weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time characteristics and the space characteristics;
and the feature fusion unit is used for fusing the time feature and the spatial feature according to the weight value to obtain a network traffic prediction result.
Preferably, the apparatus further comprises:
the function construction module is used for constructing a loss function according to the Tanh activation function, the weight value, the time characteristic and the space characteristic;
a weight value adjusting module for adjusting the weight value based on the minimization of the loss function.
It should be noted that, the deep learning-based network traffic prediction apparatus provided in the embodiment of the present invention is configured to execute all the process steps of the deep learning-based network traffic prediction method in the above embodiment, and working principles and beneficial effects of the two are in one-to-one correspondence, so that details are not repeated.
In conclusion, the invention can largely flatten the fluctuation behavior of the flow data by pre-training the network flow data, reduce the influence of random factors on the flow data and enable the development direction of the data to be closer to the real development track; meanwhile, feature extraction is carried out on the flow prediction data, then features are fused, in the fusion process, the multi-mode attention mechanism network is adopted to measure the influence weight of the time features and the space features, and finally network flow prediction is achieved.
The embodiment of the invention also provides the terminal equipment. The terminal device includes: a processor, a memory, and a computer program, such as a deep learning based network traffic prediction program, stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in each of the deep learning based network traffic prediction method embodiments described above, such as step S11 shown in fig. 1. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above-mentioned device embodiments, such as a feature extraction module.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, an intelligent tablet and other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above components are merely examples of a terminal device and do not constitute a limitation of a terminal device, and that more or fewer components than those described above may be included, or certain components may be combined, or different components may be included, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used for storing the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A network traffic prediction method based on deep learning is characterized by comprising the following steps:
acquiring network flow data;
preprocessing the network traffic data to obtain traffic prediction data;
performing feature extraction on the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain a time feature and a space feature;
and fusing the time characteristics and the space characteristics according to the multi-mode attention mechanism network to obtain a network flow prediction result.
2. The deep learning based network traffic prediction method of claim 1, wherein prior to the obtaining network traffic data, the method further comprises:
setting different acquisition periods according to the data types;
and acquiring corresponding data based on the acquisition period.
3. The deep learning-based network traffic prediction method according to claim 2, wherein the preprocessing the network traffic data to obtain traffic prediction data includes:
segmenting the network traffic data to obtain a pre-training sequence;
obtaining observation time and prediction advance time according to the acquisition period and a preset time step;
and obtaining flow prediction data based on the pre-training sequence, the observation time and the prediction advance time.
4. The deep learning-based network traffic prediction method according to claim 1, wherein the fusing the temporal features and the spatial features according to a multi-modal attention mechanism network to obtain a network traffic prediction result comprises:
performing weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time features and the space features;
and fusing the time characteristic and the space characteristic according to the weight value to obtain a network flow prediction result.
5. The deep learning based network traffic prediction method of claim 4, further comprising:
constructing a loss function according to the Tanh activation function, the weight value, the time characteristic and the space characteristic;
adjusting the weight value based on a minimization of the loss function.
6. The deep learning based network traffic prediction method of claim 1, wherein the network traffic data comprises trending data, periodic data, and proximity data.
7. A deep learning-based network traffic prediction device is characterized by comprising:
the data acquisition module is used for acquiring network flow data;
the preprocessing module is used for preprocessing the network traffic data to obtain traffic prediction data;
the characteristic extraction module is used for extracting the characteristics of the flow prediction data based on a convolutional neural network and a long-short term memory network to obtain time characteristics and space characteristics;
and the characteristic fusion module is used for fusing the time characteristic and the spatial characteristic according to the multi-mode attention mechanism network to obtain a network flow prediction result.
8. The deep learning based network traffic prediction device of claim 7, wherein the feature fusion module comprises:
the weight extraction unit is used for carrying out weight extraction according to the multi-modal attention mechanism network to obtain weight values corresponding to the time characteristics and the space characteristics;
and the feature fusion unit is used for fusing the time feature and the spatial feature according to the weight value to obtain a network traffic prediction result.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the deep learning based network traffic prediction method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the deep learning-based network traffic prediction method according to any one of claims 1 to 6.
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