WO2022083549A1

WO2022083549A1 - Traffic signal conversion method and apparatus, electronic device, and storage medium

Info

Publication number: WO2022083549A1
Application number: PCT/CN2021/124448
Authority: WO
Inventors: 王大江; 叶友道; 张伟; 王振宇
Original assignee: 中兴通讯股份有限公司
Priority date: 2020-10-20
Filing date: 2021-10-18
Publication date: 2022-04-28
Also published as: CN114448840A

Abstract

Embodiments of the present invention relate to the technical field of communications. Disclosed is a traffic signal conversion method, comprising: acquiring traffic signals of an OTN at a plurality of time points; and using a graph convolutional network to convert the traffic signals into traffic signals having temporal features and spatial features. Also disclosed in the embodiments of the present invention are a traffic signal conversion apparatus, an electronic device, and a storage medium.

Description

Flow signal conversion method, device, electronic device and storage medium

cross reference

This application is based on the Chinese patent application with the application number "202011127237.8" and the application date is October 20, 2020, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated by reference. Apply.

technical field

The embodiments of the present application relate to the field of communication technologies, and in particular, to a traffic signal conversion method, apparatus, electronic device, and storage medium.

Background technique

In recent years, with the rapid development of OTN (Optical Transport Network), especially the ultra-100G optical transport network technology, to meet the needs of the three major application scenarios (eMBB, uRLLC, mMTC) in the 5G era, dig deep into the 5G backhaul. The application of AI technology in OTN networking scenarios has been highly recognized by the industry and has extremely important practical significance.

Predict the layer 2 service traffic carried by the ODU (optical data unit) layer in the OTN, and plan the expansion and reconstruction of the network element equipment nodes in the OTN network according to the prediction results, or perform the switching granularity ODUk and ODUFlex of the ODU layer services. The intelligent adjustment of BOD mode is an important function that OTN's SDON (software-defined optical network) management and control system needs to provide to operator users.

However, since ordinary neural networks can only process Euclidean spatial data, and the data involved in OTN is non-Euclidean spatial data, ordinary neural networks cannot perform effective machine learning on OTN to achieve OTN traffic prediction.

Application content

The embodiment of the present application provides a traffic signal conversion method, comprising: acquiring traffic signals at several times of OTN; using a graph convolution network to convert the traffic signals into traffic signals with temporal and spatial characteristics.

The embodiment of the present application also provides a traffic signal conversion device, including: an acquisition module, used for acquiring the traffic signals at several times of the OTN; and spatial characteristics of flow signals.

An embodiment of the present application further provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a program that can be executed by the at least one processor instructions, the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned traffic signal conversion method.

Embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned traffic signal conversion method is implemented.

Description of drawings

One or more embodiments are exemplified by the pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments.

1 is a schematic flowchart of a flow signal conversion method provided by a first embodiment of the present application;

Figure 2 is a schematic diagram of a traffic signal in an OTN networking environment;

Figure 3(a) is a schematic diagram of convolution of Euclidean spatial data using CNN;

FIG. 3(b) is a schematic diagram of convolving a non-Euclidean space using a circle convolution network in the traffic signal conversion method provided by the first embodiment of the present application;

4 is a schematic flowchart of a flow signal conversion method provided by a second embodiment of the present application;

5 is a schematic diagram of a model of a traffic signal of an ODU electrical layer according to time and space dimensions in the traffic signal conversion method provided by the second embodiment of the present application;

6 is a schematic flowchart of a traffic signal conversion method provided by a third embodiment of the present application;

FIG. 7 is a schematic diagram of the service flow of the ODU electrical layer topology in the OTN networking environment;

8 is a schematic diagram of a model of a traffic signal of an ODU electrical layer service according to time and space dimensions in the traffic signal conversion method provided by the third embodiment of the present application;

9 is another schematic flowchart of the traffic signal conversion method provided by the third embodiment of the present application;

10 is a schematic flowchart of a traffic signal conversion method provided by the fourth embodiment of the present application;

11 is a schematic diagram of a schematic diagram of a traffic signal conversion method provided by the fourth embodiment of the present application;

12 is a schematic structural diagram of a module of a flow signal conversion device provided by a fifth embodiment of the present application;

FIG. 13 is a schematic structural diagram of an electronic device provided by a sixth embodiment of the present application.

Detailed ways

In order to make the objectives, technical solutions and advantages of the embodiments of the present application more clear, each embodiment of the present application will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that, in each embodiment of the present application, many technical details are provided for the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be realized. The following divisions of the various embodiments are for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the various embodiments may be combined with each other and referred to each other on the premise of not contradicting each other.

The main purpose of the embodiments of this application is to propose a traffic signal conversion method, device, electronic device, and storage medium, which can perform effective machine learning on OTN to realize traffic prediction of OTN.

The first embodiment of the present application relates to a method for converting traffic signals. By acquiring traffic signals at several times of OTN, a graph convolution network is used to convert traffic signals at several times of OTN into traffic signals with temporal and spatial characteristics. The non-European spatial data of the OTN traffic signal, which is a graph convolutional network, is converted into European spatial data. Effective machine learning can be performed on the OTN traffic according to the converted traffic signal, thereby realizing OTN traffic prediction.

It should be noted that the execution body of the traffic signal conversion method provided by the embodiments of the present application may be a server, wherein the server may be implemented by a single server or a server cluster composed of multiple servers. The following will take the server as an example for description. .

The specific flow of the traffic signal conversion method provided by the embodiment of the present application is shown in FIG. 1 , and specifically includes the following steps:

S101: Acquire traffic signals of the OTN at several times.

In a specific example, acquiring the traffic signals of the OTN at several times may specifically be acquiring the traffic signals of the ODU (Optical Data Unit) electrical layer topology of the OTN at several times.

Please refer to FIG. 2 , which is a schematic diagram of traffic signals in an OTN networking environment. Specifically, the OTN network is composed of optoelectronic hybrid scheduling network element nodes, and each optoelectronic hybrid scheduling network element node is roughly divided into two parts: an optical cross-connect device and an ODU electrical layer cross-connect device. The ODU traffic signal on each optoelectronic hybrid scheduling network element node is scheduled by the ODU electrical layer cross-connect device of this optoelectronic hybrid scheduling network element. There are two types of traffic scheduling: ODU electrical layer pass-through traffic and ODU electrical layer add/drop traffic , the ODU electrical-layer pass-through traffic mainly refers to the ODU electrical-layer service traffic that is dispatched by the ODU electrical-layer cross-connect device and is not terminated; Layer business traffic.

S102: Convert the traffic signal into a traffic signal with temporal and spatial features using a graph convolutional network.

Since general machine learning methods (such as CNN (Convolutional Neural Network)) can only process Euclidean spatial data, and OTN is graph topology data, which is non-Euclidean spatial data, general machine learning methods cannot perform effective machine learning on OTN. , and then realize the traffic prediction of OTN.

Since the graph topology of OTN contains not only the feature information of the topological nodes, but also the topological structure information between the topological nodes and the topological nodes, the machine learning of OTN can be effectively performed only by extracting the information of these two dimensions. Graph Convolutional networks (GCN) can effectively extract the multi-dimensional data features of the irregular graph topology of OTN, convert the OTN traffic signal into a traffic signal with temporal and spatial characteristics, and further improve the OTN traffic signal. Traffic conducts effective machine learning for OTN traffic prediction.

Please refer to FIG. 3(a) and FIG. 3(b), wherein, FIG. 3(a) is a schematic diagram of using CNN to convolve Euclidean spatial data, and FIG. 3(b) is a flow conversion method provided by an embodiment of the present application. Schematic illustration of convolution of non-Euclidean spatial data using graph convolutional networks.

Optionally, when training the graph convolution network, the traffic signals of the OTN can be monitored and counted according to the time and space dimensions, and the statistical traffic can be used as the input of the graph convolution network to train the graph convolution network. After training, the traffic signals at several times of OTN obtained in S101 are used as the input of the graph convolution network, and the trained graph convolution network can be used to convert these traffic signals into traffic signals with temporal and spatial characteristics.

It can be understood that after using the graph convolutional network to convert the traffic signal into a traffic signal with temporal and spatial characteristics, the converted signal can be input into the neural network for training, so as to obtain the traffic prediction model of OTN, and then realize the Traffic forecast for OTN.

The traffic signal conversion method provided by the embodiment of the present application converts the traffic signals of OTN at several moments into traffic signals with temporal and spatial characteristics through a graph convolution network, and can convert the non-European spatial data of the OTN traffic signal into Euclidean spatial data, so that effective machine learning can be carried out according to the converted traffic signal, and then the traffic prediction of OTN can be realized.

The second embodiment of the present application relates to a traffic signal conversion method. The second embodiment is roughly the same as the first embodiment, with the main difference: in this embodiment, the traffic signals at several times of the ODU electrical layer topology of the OTN are obtained as a graph For the input of the convolutional network, the OTN traffic model is established according to the through traffic, the add-on traffic and the total traffic of the topology nodes in the ODU electrical layer topology to realize the conversion of OTN traffic signals.

The specific flow of the traffic signal conversion method provided by the implementation manner of the present application is shown in FIG. 4 , and specifically includes the following steps:

S201: Acquire the total traffic of the topology node at several times, the total traffic includes the through traffic and the add and drop traffic, and the topology node is a node included in the ODU electrical layer topology.

S202: Using a graph convolutional network according to

Convert the traffic signal into a traffic signal with temporal and spatial characteristics, where y' is the converted traffic signal, σ is the activation function, and α _jt is the convolution of the j-th dimension traffic attribute of the topology node at the t-th time Kernel parameter, L is the normalized graph Laplacian operator, x _jt is the j-th dimension traffic attribute of the topology node at the t-th time, k is the total dimension of the traffic attribute, M is the total number of times, The dimensions of the traffic attributes include total traffic, through traffic, and add and drop traffic.

For S201-S202, the specific instructions are as follows:

Please refer to FIG. 5 , which is a schematic diagram of a model of a traffic signal of an ODU electrical layer according to time and space dimensions. Specifically, for Figure 5, it can be defined: G _t =(V _t , E, W), where G _t is the ODU electrical layer topology (traffic network topology) at the t-th time, and V _t is the ODU electrical layer at the t-th time The topology node set of the topology, the number of topology nodes is set to n, E is the edge (ie link) set of the ODU electrical layer topology, and W (ie w _ij in the figure) is the weight adjacency matrix of adjacent topology nodes.

According to the above definition, the traffic signal in the ODU electrical layer topology can be defined as: X∈R ^M×n×k represents the k-dimensional traffic attribute of n topology nodes at M times, and x _i ∈R ^M×k represents the ith topology The k-dimensional traffic attribute of the node at M times, x _it ∈ R ^k represents the k-dimensional traffic attribute of the i-th topology node at the t-th time. For x _it , there are two ways to define it:

Mode 1: x _it =[Sfit , _Tfit , _Ufit ], at this time k=3, where _Tfit represents the through-flow of the _i -th topology node at the t-th time, and the flow unit can take the value of Gbit/15min, and have

Tf _itp represents the through traffic of port p of the ith topology node at the t th time, u represents the number of through traffic ports of the ith topology node at the t th time, and all of them are line-side ports; Uf _it represents the t th time The add/drop traffic of i topology nodes, the traffic unit can be Gbit/15min, and there are

Uf _itq represents the add/drop traffic of port q of the i-th topology node at the t-th time, m represents the number of add/drop ports of the i-th topology node at the t-th time, including the client-side port and the line-side port, and the two are The pair appears; Sfit represents the total flow of the _i -th topology node at the t-th time, and the flow unit can take the value of Gbit/15min, and there is _Sfit = _Tfit + _Ufit .

Mode 2: x _it =[ _Sfit ], at this time k=1.

According to the graph convolution formula: y=Θ*gx=σ(α(L)x), where y represents the output of the graph convolution, x represents the input of the graph convolution, and *g represents the graph convolution operator of the spectral method , representing the product of the graph signal x (that is, the flow of each topology node at the t-th moment) and the convolution kernel function Θ, then we have

Among them, y' is the converted traffic signal, σ is the activation function, α _jt is the convolution kernel parameter of the j-th dimension traffic attribute of the topology node at the t-th time, and L is the normalized graph Laplacian algorithm , x _jt is the j-th dimension traffic attribute of the topology node at the t-th time, k is the total dimension of the traffic attribute, M is the total number of times, and the dimension of the traffic attribute includes the total traffic, the through traffic and the add and drop traffic.

In the traffic signal conversion method provided by the embodiment of the present application, by acquiring the traffic signals at several times in the ODU electrical layer topology of the OTN as the input of the graph convolution network, according to the through traffic of the topology nodes in the ODU electrical layer topology, the add and drop traffic and the total traffic to establish the OTN traffic model, which can effectively realize the conversion of OTN traffic signals.

The third embodiment of the present application relates to a traffic signal conversion method. The third embodiment is roughly the same as the first embodiment, and the main difference is that: in this embodiment, the services included in the topology nodes in the ODU electrical layer topology are obtained in several The traffic signal at the moment is used as the input of the graph convolution network, and the OTN traffic model is established according to the total number of services included in the ODU electrical layer, so as to realize the conversion of the OTN traffic signal.

The specific flow of the traffic signal conversion method provided by the embodiment of the present application is shown in FIG. 6 , and specifically includes the following steps:

S301: Acquire traffic signals of services included in a topology node at several times, where the topology node is a node included in the ODU electrical layer topology.

S302: Use graph convolutional network according to

Convert the traffic signal into a traffic signal with temporal and spatial characteristics, where y' is the converted traffic signal, σ is the activation function, and α _jt is the traffic attribute of the jth service of the topology node at the tth time. convolution kernel parameters, L is the normalized graph Laplacian operator, x _jt is the traffic attribute of the jth service of the topology node at the t-th time, r is the total number of services included in the topology node, M is the total number of moments.

For S301-S302, the details are as follows:

Please refer to FIG. 7 , which is a schematic diagram of service flow of an ODU electrical layer topology in an OTN networking environment. Establishing an OTN traffic model for the scenario corresponding to the figure can realize the prediction of ODU service traffic with BOD (Bandwidth On Demand) feature of OTN after traffic signal conversion. Usually, the traffic size of a single ODU service is determined by the actual size of the traffic added to and from the service at the head and end nodes. Therefore, the BOD traffic bandwidth adjustment request can be sent to the head node of the service through the SDON controller, and the service path passes through. Intermediate nodes are not directly related.

Please refer to FIG. 8 , which is a schematic diagram of a model of a traffic signal of an ODU electrical layer service according to time and space dimensions. Specifically, for Figure 8, it can be defined: G _t =(V _t , E, W), where G _t is the ODU electrical layer topology (traffic network topology) at the t-th time, and V _t is the ODU electrical layer at the t-th time The topology node set of the topology, the number of topology nodes is set to n, E is the edge (ie link) set of the ODU electrical layer topology, W (ie w _ij in the figure) is the weight adjacency matrix of adjacent topology nodes and has W∈R ^n×n , r represents the total number of services included in the topology node.

According to the above definition, the traffic signal of the services included in the topology nodes in the ODU electrical layer topology can be defined as: X∈R ^M×n×r represents the traffic attributes of r services of n topology nodes at M times, and x _i ∈ R ^M×r represents the traffic attributes of the r services of the ith topology node at M times, and x _it ∈ R ^r represents the traffic attributes of the r services of the ith topology node at the t time. For x _it , considering that the add/drop traffic of the topology nodes in each ODU electrical layer topology is not the same, in order to ensure the unity of the dimension of the service traffic signal based on the topology nodes of the ODU electrical layer topology, x _it is defined in the following way :

x _it =[x _it1 ,x _it1 ,...x _itp ,...x _itr ], where x _itp represents the add/drop traffic of the ODU electrical layer service whose service ID is p at the t-th moment of the i-th topology node. The unit can take the value of Gbit/15min. If the ith topology node is not the first and last node of the service p, that is, the service is not added or dropped on the ith topology node, then x _itp =0.

According to the graph convolution formula: y=Θ*gx=σ(α(L)x), there are:

Among them, y' is the converted traffic signal, σ is the activation function, α _jt is the convolution kernel parameter of the traffic attribute of the j-th service of the topology node at the t-th time, and L is the normalized graph Lapla Si operator, x _jt is the traffic attribute of the j-th service of the topology node at the t-th time, r is the total number of services included in the topology node, and M is the total number of times.

In a specific example, please refer to FIG. 9 , which is another schematic flowchart of the traffic signal conversion method provided by the embodiment of the present application, which specifically includes the following steps:

S301': Obtain the traffic signals of the services included in the topology node at several times, and the topology node is a node included in the ODU electrical layer topology.

S302': Using a graph convolutional network according to

Convert the traffic signal into a traffic signal with temporal and spatial characteristics, where y' is the converted traffic signal, σ is the activation function, and α _t is the convolution kernel of the add and drop traffic attributes of the topology node at the t-th time parameters, L is the normalized graph Laplacian operator, x _t is the traffic attribute of the topological node at the t-th time, and M is the total number of times.

That is, x _it can also be defined in the following way:

x _it = [Uf _it ] and there is

Among them, _Ufit represents the add/drop traffic of the i-th topology node at the t-th time, m represents the number of add/drop ports of the i-th topology node at the t-th time, including only the ports on the client side, and Uf _itj represents the traffic at the t-th time The add/drop traffic of the client-side port j of the ith topology node at the moment, the traffic unit is, for example, Gbit/15min.

According to the graph convolution formula: y=Θ*gx=σ(α(L)x), there are:

Wherein, y' is the converted traffic signal, the σ is the activation function, the α _t is the convolution kernel parameter of the add-and-drop traffic attribute of the topology node at the t-th moment, and the L is the normalization The graph Laplacian of , the x _t is the attribute of the add/drop traffic of the topology node at the t-th time, and the M is the total number at the time.

In the traffic signal conversion method provided by the embodiment of the present application, the OTN is established according to the total number of services included in the ODU electrical layer by acquiring the services included in the topology nodes in the ODU electrical layer topology at several times as the input of the graph convolution network. The traffic model can effectively realize the conversion of OTN traffic signals.

A fourth embodiment of the present application provides a traffic signal conversion method, which converts an OTN traffic signal into a traffic signal with temporal and spatial characteristics by using a graph convolutional network, and then predicts OTN according to the traffic signal with temporal and spatial characteristics traffic, which can effectively predict OTN traffic.

The specific flow of the traffic signal conversion method provided by the embodiment of the present application is shown in FIG. 10 , which specifically includes the following steps:

S401: Acquire traffic signals of the OTN at several times.

S402: Use a graph convolutional network to convert the traffic signal into a traffic signal with temporal and spatial features.

S403: Predict the traffic of the OTN according to the traffic signal with temporal characteristics and spatial characteristics.

Wherein, S401-S402 are the same as S101-S102 in the first embodiment. For details, please refer to the relevant description in the first embodiment. In order to avoid repetition, details are not repeated here.

In S403, when predicting the OTN traffic according to the traffic signal with temporal and spatial features, specifically, a deep neural network (DNN) may be used to predict the OTN traffic according to the traffic signal with temporal and spatial features. Of course, other neural networks can also be used to make predictions, such as a long short-term memory network (LSTM).

Please refer to FIG. 11 , which is a schematic diagram of a schematic diagram of a traffic signal conversion method provided by an embodiment of the present application. During specific training, the traffic signals of the ODU layer topology at different times of the OTN can be used as the input of the graph convolution network (GCN), and the temporal and spatial characteristics of the traffic signals of the ODU layer topology can be extracted through the graph convolution network. The extracted traffic signal with temporal and empty features is used as the input of DNN, and the graph convolutional network and DNN are trained by back-propagation algorithm in the form of supervised learning, and finally the prediction of OTN traffic is realized.

In practical applications, a traffic prediction model can be formed according to FIG. 11 on the basis of the third embodiment. Specifically, the traffic prediction model can be obtained by training and applied according to the following steps: 1. According to the OTN group defined in the third embodiment; The service flow model of the ODU electrical layer topology in the network environment, collect the flow signals X∈R ^M×n×r of r services in the ODU electrical layer topology at M times; 2. Use the collected service flow signals as a graph convolution network The input of the DNN is used to extract the temporal and spatial features of the traffic signal through the graph convolution network; 3. The traffic signal with the temporal and spatial characteristics outputted by the graph convolutional network is used as the input of the DNN, and the M times later The service traffic signal of the actual ODU electrical layer is used as the output of the DNN, and the traffic prediction sample is marked, so as to train the supervised model of the graph convolution network and DNN; 4. Obtain the traffic prediction model according to the trained graph convolution network and DNN , using the traffic prediction model to predict the service traffic signal of the ODU electrical layer; 5. According to the traffic prediction result, perform global or local BOD optimization bandwidth adjustment on the service bandwidth resources of the ODU electrical layer in the OTN.

The traffic signal conversion method provided by the embodiment of the present application converts the traffic signal of OTN into a traffic signal with temporal and spatial characteristics by using a graph convolution network, and then predicts the traffic of OTN according to the traffic signal with temporal and spatial characteristics, It can effectively realize the prediction of OTN traffic; further, through the traffic prediction of OTN, the topology nodes of the ODU electrical layer topology can be expanded and upgraded, or the services of the ODU electrical layer can be reconfigured to cope with the OTN traffic tide. network traffic congestion or unbalanced network service load caused by the effect.

In addition, those skilled in the art can understand that the division of steps of the various methods above is only for the purpose of describing clearly, and may be combined into one step or split into several steps during implementation, and decomposed into multiple steps, as long as the same logical relationship is included , are all within the protection scope of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs, but not changing the core design of the algorithm and process are all within the protection scope of this patent.

The fifth embodiment of the present application relates to a flow signal conversion device 500. As shown in FIG. 12, it includes an acquisition module 501 and a conversion module 502. The functions of each module are described in detail as follows:

The acquisition module 501 is used to acquire the traffic signals of the OTN at several moments;

The conversion module 502 is used to convert the traffic signal into a traffic signal with temporal features and spatial features by using a graph convolutional network.

Further, the acquisition module 501 is specifically used for:

Obtain the traffic signals at several times in the ODU electrical layer topology of the OTN.

Further, the acquisition module 501 is specifically used for:

Obtain the total traffic of the topology node at several times, the total traffic includes the through traffic and the add and drop traffic, and the topology node is the node included in the ODU electrical layer topology.

Further, the conversion module 502 is specifically used for:

Using a graph convolutional network according to

Further, the acquisition module 501 is specifically used for:

The traffic signals of the services included in the topology node at several times are obtained, and the topology node is a node included in the ODU electrical layer topology.

Further, the conversion module 502 is specifically used for:

Using a graph convolutional network according to

Further, the conversion module 502 is specifically used for:

Using a graph convolutional network according to

Further, the flow signal conversion device provided by the embodiment of the present application further includes a prediction module, wherein the prediction module is used for: predicting the flow of the OTN according to the flow signal with temporal characteristics and spatial characteristics.

Further, the prediction model is specifically used for: predicting the traffic of the OTN according to the traffic signal with temporal and spatial characteristics by using a deep neural network.

It is not difficult to find that this embodiment is a system example corresponding to the first, second, third and fourth embodiments, and this embodiment can be implemented in cooperation with the first, second, third and fourth embodiments . The related technical details mentioned in the first, second, third, and fourth embodiments are still valid in this embodiment, and are not repeated here in order to reduce repetition. Correspondingly, the related technical details mentioned in this embodiment can also be applied to the first, second, third and fourth embodiments.

It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit may be a physical unit, a part of a physical unit, or multiple physical units. A composite implementation of the unit. In addition, in order to highlight the innovative part of the present application, this embodiment does not introduce units that are not closely related to solving the technical problem raised by the present application, but this does not mean that there are no other units in this embodiment.

The sixth embodiment of the present application relates to an electronic device, as shown in FIG. 13 , including: at least one processor 601 ; and a memory 602 communicatively connected to the at least one processor 601 ; Instructions executed by one processor 601, the instructions are executed by at least one processor 601, so that at least one processor 601 can execute the above-mentioned traffic signal conversion method.

The memory and the processor are connected by a bus, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors and various circuits of the memory. The bus may also connect together various other circuits, such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface between the bus and the transceiver. A transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other devices over a transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor.

The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Instead, memory may be used to store data used by the processor in performing operations.

The seventh embodiment of the present application relates to a computer-readable storage medium storing a computer program. The above method embodiments are implemented when the computer program is executed by the processor.

That is, those skilled in the art can understand that all or part of the steps in the method for implementing the above embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium and includes several instructions to make a device ( It may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

Those of ordinary skill in the art can understand that the above-mentioned embodiments are specific embodiments for realizing the present application, and in practical applications, various changes in form and details can be made without departing from the spirit and the spirit of the present application. scope.

Claims

A flow signal conversion method, comprising:

Obtain OTN traffic signals at several moments;

The traffic signal is converted into a traffic signal with temporal and spatial features using a graph convolutional network.
The traffic signal conversion method according to claim 1, wherein the acquiring the traffic signals of the OTN at several times is specifically:

Acquire traffic signals at several times in the ODU electrical layer topology of the OTN.
The traffic signal conversion method according to claim 2, wherein the acquiring the traffic signals of the ODU electrical layer topology of the OTN at several times is specifically:

Acquire the total traffic of a topology node at several times, where the total traffic includes pass-through traffic and add/drop traffic, and the topology node is a node included in the ODU electrical layer topology.
The traffic signal conversion method according to claim 3, wherein the converting the traffic signal into a traffic signal with temporal characteristics and spatial characteristics by using a graph convolutional network comprises:

Live function, the α jt is the convolution kernel parameter of the j-th dimension traffic attribute of the topology node at the t-th moment, the L is the normalized graph Laplacian, and the x jt is The jth dimension traffic attribute of the topology node at the t th time, the k is the total dimension of the traffic attribute, the M is the total number at the time, and the dimension of the traffic attribute includes the total traffic, the through flow and the add and drop flow.
The traffic signal conversion method according to claim 2, wherein the acquiring the traffic signals of the ODU electrical layer topology of the OTN at several times is specifically:

Acquire traffic signals of services included in a topology node at several times, where the topology node is a node included in the ODU electrical layer topology.
The traffic signal conversion method according to claim 5, wherein the converting the traffic signal into a traffic signal with temporal characteristics and spatial characteristics by using a graph convolutional network comprises:

Live function, the α jt is the convolution kernel parameter of the traffic attribute of the jth service of the topology node at the tth moment, the L is the normalized graph Laplacian, and the x jt is the traffic attribute of the jth service of the topology node at the tth time, the r is the total number of services included in the topology node, and the M is the total number at the time.
The traffic signal conversion method according to claim 5, wherein the converting the traffic signal into a traffic signal with temporal characteristics and spatial characteristics by using a graph convolutional network comprises:

Using a graph convolutional network according to
Convert the flow signal into a flow signal with temporal and spatial characteristics, where y' is the converted flow signal, the σ is the activation function, and the α t is the topological node at the t-th moment. The convolution kernel parameter of the add/drop traffic attribute, the L is the normalized graph Laplacian operator, the x t is the add/drop traffic attribute of the topology node at the t-th moment, and the M is the total number of times at that time.
The traffic signal conversion method according to any one of claims 1-7, wherein after using a graph convolution network to convert the traffic signal into a traffic signal with temporal characteristics and spatial characteristics, the method further comprises:

The traffic of the OTN is predicted according to the traffic signal with temporal and spatial characteristics.
The traffic signal conversion method according to claim 8, wherein the predicting the traffic of the OTN according to the traffic signal with temporal characteristics and spatial characteristics comprises:

A deep neural network is used to predict the traffic of the OTN according to the traffic signal with temporal and spatial characteristics.
A flow signal conversion device, comprising:

The acquisition module is used to acquire OTN traffic signals at several moments;

The conversion module is used to convert the traffic signal into a traffic signal with temporal characteristics and spatial characteristics by using a graph convolutional network.
An electronic device comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any one of claims 1 to 9 The flow signal conversion method.
A computer-readable storage medium storing a computer program, when the computer program is executed by a processor, the flow signal conversion method according to any one of claims 1 to 9 is implemented.