CN114422438B - Link adjustment method and device for power communication network - Google Patents
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Abstract
The application discloses a link adjustment method and a link adjustment device for an electric power communication network, wherein the method comprises the steps of obtaining exchanger information of the electric power communication network and link information of a connection exchanger, establishing a flow characteristic matrix according to the exchanger information and the link information, and establishing a link adjacency matrix according to the link information so as to represent flow states of a plurality of exchangers and connection relations among a plurality of links in the electric power communication network; training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model; and finally, predicting a first link flow parameter of the power communication network in a future target time period by using a link analysis model, wherein the first link flow parameter is used for adjusting a transmission link of the service data, so that the transmission link of the service data is effectively controlled.
Description
Technical Field
The present disclosure relates to the field of power communication network control technologies, and in particular, to a method and an apparatus for adjusting a link of a power communication network.
Background
The power communication network is used as a support platform indispensable for the safe operation of the smart power grid, is a foundation for realizing the automation of power dispatching, the marketization of the operation of the power grid and the informatization of the management of the power grid, and is an important means for ensuring the safe, stable, efficient and economic operation of the power grid. With the continuous expansion of the scale of the power communication network, the rapid increase of the type and the traffic volume of the power service, and the uneven network traffic distribution caused by sudden and uncertain service create a great challenge for guaranteeing the high-quality transmission service of the power service.
Under the conditions of rapid increase of traffic, strong burstiness of traffic and increased traffic transmission demands in the power communication network, the current power communication network only depends on a static routing algorithm, so that reliable and safe transmission of power communication traffic is difficult to ensure, and effective pre-control of the power traffic cannot be realized.
Disclosure of Invention
The application provides a link adjustment method and device of an electric power communication network, which are used for solving the technical problem that the electric power communication network cannot effectively pre-control electric power service flow.
In order to solve the above technical problem, in a first aspect, an embodiment of the present application provides a link adjustment method of an electric power communication network, including:
acquiring switch information of the power communication network and link information connected with a switch, wherein the switch information comprises switch flow parameters;
establishing a flow characteristic matrix according to the switch information and the link information, wherein the flow characteristic matrix is used for representing flow states of a plurality of switches in the power communication network;
establishing a link adjacency matrix according to the link information, wherein the link adjacency matrix is used for representing the connection relation among a plurality of links in the power communication network;
training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model;
and predicting a first link flow parameter of the power communication network in a future target time period by using the link analysis model, wherein the first link flow parameter is used for adjusting a transmission link of service data.
According to the embodiment, the flow characteristic matrix is built according to the switch information and the link information, and the link adjacency matrix is built according to the link information so as to represent the flow states of a plurality of switches and the connection relation among a plurality of links in the power communication network; training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model, so that the graph neural network can learn the flow characteristic of the power communication network by utilizing the flow state of the switch and the connection relation of the link to serve as a characteristic basis for predicting the link flow parameter of the power communication network in a future time period; and finally, predicting a first link flow parameter of the power communication network in a future target time period by using a link analysis model, wherein the first link flow parameter can be used for adjusting a transmission link of service data, so that the transmission link of the service data is effectively controlled according to the link flow parameter in the future time period, the service response speed is improved, the communication delay is reduced, the communication quality is improved, and the transmission reliability of the service data is further improved.
In one embodiment, establishing a traffic feature matrix according to switch information and link information includes:
carrying out data processing on the switch information and the link information, determining feature vectors between every two switch information, and establishing a plurality of feature vectors as a flow feature matrix;
the feature vectors are: e (j) = [ P ] a，q (t)，P b，g (t)，F a (t)，F b (t)]；
Wherein e (j) represents the feature vector of the jth link, j is the link number, the jth link is connected with the q port of the switch a and the g port of the switch b, P a，q (t) port status parameter indicating q port of switch a at time t, P b，g (t) represents the port state parameter of the g port of the switch b at the time t, F a (t) represents the flow table status parameter of switch a at time t, F b (t) represents the flow meter state parameters of switch b at time t, the switch flow parameters including the port state parameters and the flow meter state parameters。
In one embodiment, establishing the link adjacency matrix based on the link information includes:
processing the link information, determining the connection relation between every two links, and establishing multiple connection relations as adjacency matrix A epsilon R j×j ，R j×j Representing the connection relation between j links;
and (5) carrying out standardization processing on the adjacent matrix A to obtain a link adjacent matrix A'.
In an embodiment, training a preset graph neural network model based on a flow characteristic matrix and a link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model, including:
carrying out convolution processing, correction processing and graph level operation on the flow characteristic matrix and the link adjacency matrix by utilizing the graph neural network model to obtain a second link flow parameter of the power communication network;
calculating a loss value between the second link flow parameter and a preset labeling parameter;
if the loss value is not smaller than the preset threshold, updating the model parameters of the graph neural network model, and predicting new second link flow parameters by using the updated graph neural network model until the loss value is smaller than the preset threshold to obtain a link analysis model.
In an embodiment, a convolutional processing, a correction processing and a graph level operation are performed on the traffic feature matrix and the link adjacency matrix by using a graph neural network model to obtain a second link traffic parameter of the power communication network, including:
carrying out convolution processing on the flow characteristic matrix and the link adjacency matrix by using a preset ReLU function to obtain a first neural network layer;
carrying out correction processing on the first neural network layer by using a preset softmax function to obtain a second neural network layer;
and performing graph level operation on the second neural network layer by using the preset link traffic occupation level to obtain a second link traffic parameter.
In an embodiment, the model parameters include a first weight matrix of the first neural network layer and a second weight matrix of the second neural network layer.
In an embodiment, using the link analysis model, predicting the first link traffic parameter of the power communication network over the future target time period further comprises:
acquiring service demand information of the power communication network, wherein the service demand information comprises transmission switch information for transmitting service data and receiving switch information for receiving the service data;
determining flow occupancy data of a plurality of transmission paths according to the first link flow parameters, the sending switch information and the receiving switch information, wherein the transmission paths are formed by a plurality of links between the sending switch and the receiving switch;
and comparing the transmission paths according to the flow occupancy data to determine a target transmission path corresponding to the service demand information.
In a second aspect, an embodiment of the present application provides a link analysis apparatus of a power communication network, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring switch information of a power communication network and link information connected with a switch, and the switch information comprises switch flow parameters;
the first establishing module is used for establishing a flow characteristic matrix according to the switch information and the link information, wherein the flow characteristic matrix is used for representing flow states of a plurality of switches in the power communication network;
the second establishing module is used for establishing a link adjacency matrix according to the link information, wherein the link adjacency matrix is used for representing the connection relation among a plurality of links in the power communication network;
the training module is used for training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model;
and the prediction module is used for predicting a first link flow parameter of the power communication network in a future target time period by using the link analysis model, wherein the first link flow parameter can be used for adjusting a transmission link of service data.
In a third aspect, embodiments of the present application provide a computer device comprising a processor and a memory for storing a computer program which, when executed by the processor, implements the steps of the link adjustment method of the power communication network of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the link adjustment method of the power communication network according to the first aspect.
It should be noted that, the beneficial effects of the second aspect to the fourth aspect are referred to the related description of the first aspect, and are not repeated herein.
Drawings
Fig. 1 is a flow chart of a link adjustment method of an electric power communication network according to an embodiment of the present application;
fig. 2 is a flow chart of a link adjustment method of an electric power communication network according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a link analysis device of an electric power communication network according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
As described in the related art, under the conditions of rapid increase of traffic volume, strong traffic burstiness and increased traffic transmission requirements in the power communication network, the current power communication network only depends on a static routing algorithm, so that reliable and safe transmission of power communication traffic is difficult to ensure, and effective pre-control of the power traffic flow cannot be realized.
Therefore, the embodiment of the application provides a link adjustment method and device for an electric power communication network, which are used for obtaining switch information of the electric power communication network and link information of a connection switch, establishing a flow characteristic matrix according to the switch information and the link information, and establishing a link adjacency matrix according to the link information so as to represent the flow states of a plurality of switches and the connection relation among a plurality of links in the electric power communication network; training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model, so that the graph neural network can learn the flow characteristic of the power communication network by utilizing the flow state of the switch and the connection relation of the link to serve as a characteristic basis for predicting the link flow parameter of the power communication network in a future time period; and finally, predicting a first link flow parameter of the power communication network in a future target time period by using a link analysis model, wherein the first link flow parameter can be used for adjusting a transmission link of service data, so that the transmission link of the service data is effectively controlled according to the link flow parameter in the future time period, the service response speed is improved, the communication delay is reduced, the communication quality is improved, and the transmission reliability of the service data is further improved.
Referring to fig. 1, fig. 1 is a flow chart of a link adjustment method of an electric power communication network according to an embodiment of the present application. The link adjustment method of the power communication network of the embodiment of the application can be applied to computer equipment, wherein the computer equipment comprises, but is not limited to, smart phones, tablet computers, notebook computers, desktop computers, physical servers, independent servers and other computing equipment. As shown in fig. 1, the link adjustment method of the power communication network of the present embodiment includes steps S101 to S105, which are described in detail below:
step S101, obtaining switch information of the power communication network, and link information connecting the switches, wherein the switch information includes switch traffic parameters.
In this step, the power communication network can be regarded as being composed of a plurality of switches and links connecting the switches. The switch information includes, but is not limited to, a switch number, a port number, and a switch traffic parameter, where the switch traffic parameter includes a switch traffic total value and a port state parameter, and the port state parameter includes, but is not limited to, a transmit-receive data packet time, a transmit-receive data packet number, a transmit-receive packet loss rate, a transmit-receive data packet byte number, and a flow table state parameter. The link information includes, but is not limited to, the switch number and port number of the switch to which the link is connected.
Optionally, the control device (i.e. the above computer device) of the power communication network monitors the switch information and the link information through the monitoring unit by arranging the monitoring unit and the control unit on the switch and the link, and performs macro regulation and control on the power communication network by using an analysis result obtained by analyzing the switch information and the link information.
Step S102, a flow characteristic matrix is established according to the switch information and the link information, and the flow characteristic matrix is used for representing flow states of a plurality of switches in the power communication network.
In this step, the switch information and the link information are subjected to data processing, a feature vector E (j) between each two switch information is determined, and a plurality of the feature vectors are established as the traffic feature matrix e= [ E (1), E (2),. The term, E (j)] T 。
Optionally, the data processing includes, but is not limited to, data cleansing, data integration, data encoding, and the like.
Optionally, the feature vector is: e (j) = [ P ] a，q (t)，P b，g (t)，F a (t)，F b (t)]；
Wherein e (j) represents the feature vector of the jth link, j is the link number, the jth link is connected with the q port of the switch a and the g port of the switch b, P a，q (t) port status parameter indicating q port of switch a at time t, P b，g (t) represents the port state parameter of the g port of the switch b at the time t, F a (t)A flow meter status parameter representing the time t of the switch a, F b (t) represents a flow table state parameter of switch b at time t, said switch flow parameter comprising said port state parameter and said flow table state parameter.
Step S103, establishing a link adjacency matrix according to the link information, wherein the link adjacency matrix is used for representing the connection relation among a plurality of links in the power communication network.
In this step, the link information is processed to determine the connection relationship between every two links, and a plurality of connection relationships are established as an adjacency matrix A epsilon R j×j ，R j×j Representing the connection relation between j links; and carrying out standardization processing on the adjacent matrix A to obtain the link adjacent matrix A'.
Optionally, according to the switch number in the link information, determining two link information with the same switch number, and then connecting two links corresponding to the two link information, where the switches corresponding to the same switch number are link nodes.
Step S104, training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition, so as to obtain a link analysis model.
In the step, the flow characteristic matrix is used as a top point set, the link adjacency matrix is used as an edge set, and the top point set and the edge set are subjected to data processing by adopting the graph neural network model so as to carry out iterative training on the graph neural network model. The preset convergence condition may be that the iteration number of the graph neural network model reaches a preset number of times, or may be that the loss function value of the graph neural network model is smaller than a preset threshold value.
Step S105, predicting, by using the link analysis model, a first link traffic parameter of the power communication network in a future target time period, where the first link traffic parameter can be used to adjust a transmission link of service data.
In this step, the switch information and the link information of the power communication network at the current time are acquired, the switch information and the link information at the current time are input into the link analysis model, and the first link flow parameter is output. Optionally, the first link traffic parameter is a link traffic occupancy probability of the power communication network at a future time T, and the link traffic occupancy probability is a probability that the link is transmitting data.
In an embodiment, based on the embodiment shown in fig. 1, the step S104 includes:
performing convolution processing, correction processing and image level operation on the flow characteristic matrix and the link adjacency matrix by using the image neural network model to obtain a second link flow parameter of the power communication network;
calculating a loss value between the second link flow parameter and a preset labeling parameter;
if the loss value is not smaller than a preset threshold, updating the model parameters of the graph neural network model, and predicting a new second link flow parameter by using the updated graph neural network model until the loss value is smaller than the preset threshold, so as to obtain the link analysis model.
In this embodiment, a convolution process is performed on the flow feature matrix and the link adjacency matrix by using a preset ReLU function, so as to obtain a first neural network layer; carrying out correction processing on the first neural network layer by using a preset softmax function to obtain a second neural network layer; and carrying out graph level operation on the second neural network layer by utilizing a preset link traffic occupation level to obtain the second link traffic parameter.
Optionally, the flow characteristic matrix E and the link adjacency matrix a' are imported into a preset ReLU function to perform convolution processing, so as to obtain:
H 1 ＝ReLU[A′(ξE)w 1 ]；
wherein H is 1 Represents a first neural network layer, ζ represents a Bernoulli distribution coefficient, w 1 Is a first weight matrix;
will H 1 And (3) introducing a softmax function to carry out correction treatment, so as to obtain:
H 2 ＝softmax(A′H 1 w 2 )；
wherein H is 2 Representing a second neural network layer, w 2 Is a second weight matrix;
performing graph level operation on the second neural network layer, wherein the graph level operation is to guide the preset link occupancy rate grade gamma at the future time T into the second neural network layer H by setting the occupancy rate grade gamma at the future time T 2 In the process of obtaining the second link flow parameter, the second link flow parameter is a probability matrix Z with occupancy of each link of 1, 2, 3, … … and gamma at the future time T.
Optionally, the model parameters include a first weight matrix of the first neural network layer and a second weight matrix of the second neural network layer.
Optionally, the first weight matrix w is adjusted by a parameter adjustment algorithm 1 And a second weight matrix w 2 . It should be appreciated that the first weight matrix w 1 And a second weight matrix w 2 The parameter adjustment algorithm can be used for automatically adjusting the parameter, and the power communication network control unit can be used for manually adjusting the parameter.
In an embodiment, fig. 2 is a schematic flow chart of a link adjustment method of an electric power communication network according to another embodiment of the present application based on the embodiment shown in fig. 1. It will be appreciated that the same steps as in fig. 1 are not repeated here. As shown in fig. 2, after step S105, the method further includes:
step S201, obtaining service requirement information of the power communication network, wherein the service requirement information comprises transmission switch information for transmitting service data and receiving switch information for receiving the service data;
step S202, determining flow occupancy data of a plurality of transmission paths according to the first link flow parameters, the sending switch information and the receiving switch information, wherein the transmission paths are formed by a plurality of links from the sending switch to the receiving switch;
and step 203, comparing the transmission paths according to the flow occupancy data, and determining a target transmission path corresponding to the service demand information.
In step S201 to step S203, data analysis is performed on the received service requirement information, so as to obtain transmission switch information for transmitting service data and reception switch information for receiving the service data; classifying the received service demand information; and obtaining flow occupancy data of different transmission paths, namely a link flow prediction result, according to the probability matrix Z corresponding to the first link flow parameter, the sending switch information and the receiving switch information.
Optionally, calculating a comprehensive value of the flow occupancy rate on each transmission path, wherein the comprehensive value is the maximum value of the flow occupancy rate of the link on each transmission path; comparing and sequencing the flow occupancy integrated values of each transmission path, and sequencing the flow occupancy integrated values from small to large according to the integrated values; and comparing the service classification with each transmission path, wherein the sequence of the steps above is compared until the flow capacity of one transmission path meets the service classification, and the transmission path is selected as the transmission path of the service response.
Alternatively, the ranking may be based on average traffic response time and traffic flow.
In order to execute the link adjustment method of the power communication network corresponding to the method embodiment, corresponding functions and technical effects are achieved. Referring to fig. 3, fig. 3 shows a block diagram of a link analysis device of an electric power communication network according to an embodiment of the present application. For convenience of explanation, only a portion related to the present embodiment is shown, and the link analysis device of the power communication network provided in the embodiment of the present application includes:
an obtaining module 301, configured to obtain switch information of a power communication network and link information for connecting to a switch, where the switch information includes a switch flow parameter;
a first establishing module 302, configured to establish a traffic feature matrix according to the switch information and the link information, where the traffic feature matrix is used to characterize traffic states of a plurality of switches in the power communication network;
a second establishing module 303, configured to establish a link adjacency matrix according to the link information, where the link adjacency matrix is used to characterize a connection relationship between a plurality of links in the power communication network;
the training module 304 is configured to train a preset graph neural network model based on the flow feature matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition, so as to obtain a link analysis model;
a prediction module 305, configured to predict, using the link analysis model, a first link traffic parameter of the power communication network in a future target time period, where the first link traffic parameter can be used to adjust a transmission link of traffic data.
In one embodiment, the first establishing module 302 includes:
the first establishing unit is used for carrying out data processing on the switch information and the link information, determining feature vectors between every two switch information and establishing a plurality of feature vectors as the flow feature matrix;
the feature vector is: e (j) = [ P ] a，q (t)，P b，g (t)，F a (t)，F b (t)]；
Wherein e (j) represents the feature vector of the jth link, j is the link number, the jth link is connected with the q port of the switch a and the g port of the switch b, P a，q (t) port status parameter indicating q port of switch a at time t, P b，g (t) represents the port state parameter of the g port of the switch b at the time t, F a (t) represents the flow table status parameter of switch a at time t, F b (t) represents a flow table state parameter of switch b at time t, said switch flow parameter comprising said port state parameter and said flow table state parameter.
In an embodiment, the second establishing module 303 includes:
a second establishing unit, configured to perform data processing on the link information, determine a connection relationship between every two links, and establish a plurality of connection relationships as an adjacency matrix A∈R j×j ，R j×j Representing the connection relation between j links;
and the normalization unit is used for performing normalization processing on the adjacent matrix A to obtain the link adjacent matrix A'.
In one embodiment, the training module 304 includes:
the processing unit is used for carrying out convolution processing, correction processing and image level operation on the flow characteristic matrix and the link adjacency matrix by utilizing the image neural network model to obtain a second link flow parameter of the power communication network;
the calculating unit is used for calculating a loss value between the second link flow parameter and a preset labeling parameter;
and the updating unit is used for updating the model parameters of the graph neural network model if the loss value is not smaller than a preset threshold value, and predicting new second link flow parameters by utilizing the updated graph neural network model until the loss value is smaller than the preset threshold value to obtain the link analysis model.
In an embodiment, the processing unit comprises:
the convolution subunit is used for carrying out convolution processing on the flow characteristic matrix and the link adjacency matrix by utilizing a preset ReLU function to obtain a first neural network layer;
the correction subunit is used for correcting the first neural network layer by utilizing a preset softmax function to obtain a second neural network layer;
and the operation subunit is used for carrying out graph level operation on the second neural network layer by utilizing a preset link traffic occupation level to obtain the second link traffic parameter.
In an embodiment, the model parameters include a first weight matrix of the first neural network layer and a second weight matrix of the second neural network layer.
In an embodiment, the link analysis device further includes:
a second obtaining module, configured to obtain service requirement information of the power communication network, where the service requirement information includes sending switch information for sending service data and receiving switch information for receiving the service data;
the determining module is used for determining flow occupancy data of a plurality of transmission paths according to the first link flow parameters, the sending switch information and the receiving switch information, wherein the transmission paths are formed by a plurality of links from the sending switch to the receiving switch;
and the comparison module is used for comparing the transmission paths according to the flow occupancy data and determining a target transmission path corresponding to the service demand information.
The above-described link analysis device of the power communication network may implement the link adjustment method of the power communication network of the above-described method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the method embodiments described above, and in this embodiment, no further description is given.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 4, the computer device 4 of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41 and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the processor 40 implementing the steps in any of the method embodiments described above when executing the computer program 42.
The computer device 4 may be a smart phone, a tablet computer, a desktop computer, a cloud server, or other computing devices. The computer device may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of computer device 4 and is not intended to limit computer device 4, and may include more or fewer components than shown, or may combine certain components, or may include different components, such as input-output devices, network access devices, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), the processor 40 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may in some embodiments be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. The memory 41 may in other embodiments also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the computer device 4. The memory 41 is used for storing an operating system, application programs, boot loader (BootLoader), data, other programs, etc., such as program codes of the computer program. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
In addition, the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps in any of the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product which, when run on a terminal device, causes the terminal device to perform the steps of the method embodiments described above.
In several embodiments provided herein, it will be understood that 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.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a terminal device to perform all or part of the steps of the method described in the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiments have been provided for the purpose of illustrating the objects, technical solutions and advantages of the present application in further detail, and it should be understood that the foregoing embodiments are merely examples of the present application and are not intended to limit the scope of the present application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art, which are within the spirit and principles of the present application, are intended to be included within the scope of the present application.
Claims (10)
1. A method of link adjustment for an electrical power communication network, comprising:
acquiring switch information of a power communication network and link information for connecting a switch, wherein the switch information comprises switch flow parameters;
establishing a flow characteristic matrix according to the switch information and the link information, wherein the flow characteristic matrix is used for representing flow states of a plurality of switches in the power communication network;
establishing a link adjacency matrix according to the link information, wherein the link adjacency matrix is used for representing the connection relation among a plurality of links in the power communication network;
training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model;
and predicting a first link flow parameter of the power communication network in a future target time period by using the link analysis model, wherein the first link flow parameter is used for adjusting a transmission link of service data.
2. The link adjustment method of a power communication network according to claim 1, wherein the establishing a traffic feature matrix based on the switch information and the link information comprises:
performing data processing on the switch information and the link information, determining feature vectors between every two switch information, and establishing a plurality of feature vectors as the flow feature matrix;
the feature vector is: e (j) = [ P ] a，q (t)，P b，g (t)，F a (t)，F b (t)]；
Wherein e (j) represents the feature vector of the jth link, j is the link number, the jth link is connected with the q port of the switch a and the g port of the switch b, P a，q (t) port status parameter indicating q port of switch a at time t, P b，g (t) represents the port state parameter of the g port of the switch b at the time t, F a (t) represents the flow table status parameter of switch a at time t, F b (t) represents a flow table state parameter of switch b at time t, said switch flow parameter comprising said port state parameter and said flow table state parameter.
3. The link adjustment method of a power communication network according to claim 1, wherein the establishing a link adjacency matrix based on the link information includes:
processing the link information, determining the connection relation between every two links, and establishing a plurality of connection relations as an adjacent matrix A epsilon R j×j ，R j×j Representing the connection relation between j links;
and carrying out standardization processing on the adjacent matrix A to obtain the link adjacent matrix A'.
4. A method for adjusting a link of an electric power communication network according to any one of claims 1 to 3, wherein training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model includes:
performing convolution processing, correction processing and image level operation on the flow characteristic matrix and the link adjacency matrix by using the image neural network model to obtain a second link flow parameter of the power communication network;
calculating a loss value between the second link flow parameter and a preset labeling parameter;
if the loss value is not smaller than a preset threshold, updating the model parameters of the graph neural network model, and predicting a new second link flow parameter by using the updated graph neural network model until the loss value is smaller than the preset threshold, so as to obtain the link analysis model.
5. The method for link adjustment of an electrical power communication network as set forth in claim 4, wherein said convolving, modifying and performing a graph-level operation on said traffic characteristic matrix and said link adjacency matrix using said graph neural network model to obtain a second link traffic parameter of said electrical power communication network, comprising:
performing convolution processing on the flow characteristic matrix and the link adjacency matrix by using a preset ReLU function to obtain a first neural network layer;
carrying out correction processing on the first neural network layer by using a preset softmax function to obtain a second neural network layer;
and carrying out graph level operation on the second neural network layer by utilizing a preset link traffic occupation level to obtain the second link traffic parameter.
6. The link adaptation method of a power communication network according to claim 5, wherein the model parameters comprise a first weight matrix of the first neural network layer and a second weight matrix of the second neural network layer.
7. A method of link adjustment of a power communication network according to any one of claims 1 to 3, characterized in that the predicting, using the link analysis model, a first link traffic parameter of the power communication network over a future target time period further comprises:
acquiring service demand information of the power communication network, wherein the service demand information comprises sending switch information for sending service data and receiving switch information for receiving the service data;
determining flow occupancy data of a plurality of transmission paths according to the first link flow parameters, the sending switch information and the receiving switch information, wherein the transmission paths consist of a plurality of links from the sending switch to the receiving switch;
and comparing the transmission paths according to the flow occupancy data, and determining a target transmission path corresponding to the service demand information.
8. A link analysis device of an electric power communication network, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring switch information of a power communication network and link information connected with a switch, and the switch information comprises switch flow parameters;
the first establishing module is used for establishing a flow characteristic matrix according to the switch information and the link information, wherein the flow characteristic matrix is used for representing flow states of a plurality of switches in the power communication network;
the second establishing module is used for establishing a link adjacency matrix according to the link information, wherein the link adjacency matrix is used for representing the connection relation among a plurality of links in the power communication network;
the training module is used for training a preset graph neural network model based on the flow characteristic matrix and the link adjacency matrix until the graph neural network model reaches a preset convergence condition to obtain a link analysis model;
and the prediction module is used for predicting a first link flow parameter of the power communication network in a future target time period by using the link analysis model, wherein the first link flow parameter is used for adjusting a transmission link of service data.
9. A computer device comprising a processor and a memory for storing a computer program which, when executed by the processor, implements the steps of the link adjustment method of a power communication network as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the link adjustment method of an electric power communication network according to any one of claims 1 to 7.
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