CN112766633A - Electric wireless heterogeneous network management method and device based on flow balance - Google Patents
Electric wireless heterogeneous network management method and device based on flow balance Download PDFInfo
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Abstract
The invention provides a method and a device for managing a power wireless heterogeneous network based on flow balance, which comprises the following steps: outputting a first flow prediction result of a link through a graph neural network according to the current flow data; analyzing the variation trend of the current flow data to obtain a second flow prediction result of the link; analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result; and if the final prediction result exceeds a preset flow threshold value, carrying out flow equalization processing on the electric power wireless heterogeneous network. The flow prediction is carried out by two methods of the graph neural network and the change trend analysis respectively, and the two flow prediction results are combined, and meanwhile, the graph neural network is used for carrying out the flow prediction.
Description
Technical Field
The invention belongs to the technical field of power wireless networks, and particularly relates to a power wireless heterogeneous network management method and device based on flow balance.
Background
In recent years, with the rapid development of smart power grids, particularly the development of big data, cloud computing, internet of things and mobile services, a large amount of real-time and high-speed data demands are caused, the acquisition frequency of a power system is increased, and the existing communication management technology cannot meet the requirements of wireless heterogeneous network link management, so that the stability and the real-time performance of important power services are guaranteed. The power wireless heterogeneous network consists of wireless networks working in different frequency bands and different technical systems, has the function of providing a safe and reliable data transmission channel between a remote monitoring system and a power system server, and can meet most power communication requirements.
The common function of the power wireless heterogeneous network is to actively switch to another link when the quality of network signals transmitted by the connected link cannot meet the requirement, so that the smoothness of a service channel is guaranteed. Therefore, the flow prediction and the flow balance on the link directly relate to the switching effect of the power wireless heterogeneous network, and due to the fact that the topological structure of the power wireless heterogeneous network is complex, factors influencing the flow prediction result at each node in the power wireless heterogeneous network are different, the traditional flow prediction method cannot be applied to the power wireless heterogeneous network, and the need of link management of the power wireless heterogeneous network is difficult to meet.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a power wireless heterogeneous network management method based on flow balance, which comprises the following steps:
acquiring current flow data at nodes of each link of the power wireless heterogeneous network;
generating an input matrix according to the current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
analyzing the variation trend of the current flow data to obtain a second flow prediction result of the link;
analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result;
and if the final prediction result exceeds a preset flow threshold value, carrying out flow equalization processing on the electric power wireless heterogeneous network.
Optionally, the current traffic data includes total link traffic, node traffic of an ingress and egress link, ingress and egress port traffic, a link load rate, and source IP traffic.
Optionally, the generating an input matrix according to the current traffic data, inputting the input matrix into a pre-trained graph neural network, and outputting a first traffic prediction result of a link through the graph neural network includes:
identifying the flow type of each node on the link based on a fuzzy C-means algorithm;
forming an input matrix by the current data traffic, and inputting the input matrix into a graph neural network corresponding to the traffic type according to the identified traffic type;
and outputting the first flow prediction result of the link through the neural network.
Optionally, the analyzing a variation trend of the current traffic data to obtain a second traffic prediction result of the link includes:
rejecting data of the link at a peak-to-valley value of the selected time period;
calculating the average flow rate change of a plurality of adjacent sampling time points;
and calculating the flow value at the next moment according to the maximum flow change rate to serve as a second flow prediction result.
Optionally, the analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result includes:
calculating a difference between the first flow prediction result and the second flow prediction result;
if the difference value is smaller than a preset threshold value, taking a first flow prediction result as a final prediction result;
and if the difference is not smaller than the preset threshold, performing weighted calculation on the first flow prediction result and the second flow prediction result based on preset weight, and taking the weighted calculation result as a final prediction result.
Optionally, the method for managing the power wireless heterogeneous network further includes, after current traffic data at nodes of each link of the power wireless heterogeneous network is collected, establishing a link load mapping table;
the link load mapping table comprises a link number, a link load value and a preset flow threshold value.
Optionally, if the final prediction result exceeds the preset flow threshold, performing flow balancing processing on the power wireless heterogeneous network, including:
judging whether the final prediction result exceeds a preset flow threshold value of the link or not by inquiring a link load mapping table;
if the traffic exceeds the preset traffic threshold, searching whether an available link exists according to the routing path;
if the available link is found, acquiring the load capacity of the available link, and if the load capacity does not exceed the preset proportion of the route, sending echo information to inhibit the source IP flow;
and if the load capacity exceeds the preset proportion of the route and more than one available link is found, proportionally distributing the flow of the power wireless heterogeneous network according to the load capacity of the available link.
The invention also provides a power wireless heterogeneous network management device based on flow balance based on the same thought, which comprises:
a collecting unit: the wireless heterogeneous network traffic monitoring system is used for collecting current traffic data at nodes of each link of the wireless heterogeneous network;
a first prediction unit: the flow prediction device is used for generating an input matrix according to current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
a second prediction unit: the flow prediction method comprises the steps of analyzing the change trend of current flow data to obtain a second flow prediction result of a link;
an analysis unit: the flow rate prediction device is used for analyzing the first flow rate prediction result and the second flow rate prediction result to obtain a final prediction result;
an equalization unit: and the method is used for carrying out flow equalization processing on the electric power wireless heterogeneous network if the final prediction result exceeds a preset flow threshold value.
Optionally, the first prediction unit is configured to:
forming an input matrix by the current data traffic, and inputting the input matrix into a graph neural network corresponding to the traffic type according to the identified traffic type;
and outputting the first flow prediction result of the link through the neural network.
Optionally, the second prediction unit is configured to:
rejecting data of the link at a peak-to-valley value of the selected time period;
calculating the average flow rate change of a plurality of adjacent sampling time points;
and calculating the flow value at the next moment according to the maximum flow change rate to serve as a second flow prediction result.
The technical scheme provided by the invention has the beneficial effects that:
the flow prediction is carried out by two methods of the graph neural network and the change trend analysis respectively, and the two flow prediction results are combined, and meanwhile, the graph neural network is used for carrying out the flow prediction.
And carrying out flow balancing treatment on the predicted link which possibly exceeds the threshold, and adjusting the flow according to the load condition of the available link, thereby eliminating the hidden danger of the network link in advance, fully utilizing network resources and avoiding the risk of high load caused by the flow concentrated on a part of links.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for managing a wireless heterogeneous power network based on flow balancing according to the present invention;
fig. 2 is a block diagram of a power wireless heterogeneous network management device based on traffic balancing according to the present invention.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present invention provides a method for managing a wireless heterogeneous power network based on traffic balancing, including:
s1: acquiring current flow data at nodes of each link of the power wireless heterogeneous network;
s2: generating an input matrix according to the current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
s3: analyzing the variation trend of the current flow data to obtain a second flow prediction result of the link;
s4: analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result;
s5: and if the final prediction result exceeds a preset flow threshold value, carrying out flow equalization processing on the electric power wireless heterogeneous network.
In this embodiment, the current traffic data at a node is collected by a wireless terminal and a wireless base station network manager, where the current traffic data includes total link traffic, node traffic of an incoming and outgoing link, traffic of an incoming and outgoing port, a link load rate, and source IP traffic.
In this embodiment, the generating an input matrix according to the current traffic data, inputting the input matrix into a pre-trained graph neural network, and outputting a first traffic prediction result of a link through the graph neural network includes:
and identifying the traffic type at each node on the link based on a fuzzy C-means algorithm. The data set X is composed of the current flow data acquired at the ith sampling time in S1i＝{x1,x2,…,xn}，x1、x2、…、xnRespectively the total flow among link nodes, the flow of the in-out port, the flow of the link load rate and the source IP flow, forming a characteristic matrix by taking the data as the characteristics of the flow, and enabling the flow on the link to belong to the aggregation of various flow types based on a fuzzy C mean value algorithmClass loss function JmMinimization, the cluster loss function JmThe following were used:
wherein, XiFor the current flow data obtained at the ith sampling instant, cjM is a preset flow type clustering center, and is a constant for artificially setting and controlling the fuzzy degree of a clustering result, and the value range is more than or equal to 1 and less than or equal to m<∞，uijThe membership degree of the current flow data obtained at the ith sampling moment, which belongs to the jth flow, is n, the number of data sets, and c, the total number of flow types; the value ranges of n and c are positive integers, and X isi、cjThe value ranges of (A) are all positive numbers. Those skilled in the art will know how to calculate the degree of membership by the fuzzy C-means algorithm, and will not be described herein. And repeating the calculation process until the membership degree is stabilized in a preset range, and dividing the current flow data obtained at the ith sampling moment into flow types with the highest membership degree.
And forming an input matrix by the current data traffic, and inputting the input matrix into the graph neural network corresponding to the traffic type according to the identified traffic type. Because different types of traffic generally have different change rules, in consideration of the influence of the traffic type on the prediction result, different graph neural networks are trained in advance according to different traffic types in the embodiment, so that the prediction accuracy is further improved.
And finally, outputting a first flow prediction result of the link through the neural network. The traditional neural network faces a great challenge in processing a power wireless heterogeneous network with a complex topological structure, and because the number of adjacent nodes of each node is different and the adjacent nodes have an incidence relation, the graph neural network can describe the relation of each node in the power wireless heterogeneous network through the top point and the edge in the graph.
The graph neural network trained in advance comprises vertexes, edges connecting the vertexes and relationship characteristics of the edges, in this embodiment, the vertexes are nodes in a link, traffic data at the nodes are characteristics of the vertexes, the edges are topological relationships between adjacent nodes, and a traffic mapping relationship between two nodes is the relationship characteristics of the edges. And sequentially passing the input matrix through an input layer, a hidden layer and an output layer of the graph neural network to obtain a first flow prediction result of the link, wherein the first flow prediction result is the flow of the link at the next moment.
In this embodiment, the analyzing the variation trend of the current traffic data to obtain the second traffic prediction result of the link includes:
and rejecting data of the link at the peak-to-valley value of the selected time period. This situation is random and unsustainable due to certain emergencies, resulting in a surge of network traffic. Because such an incident affects the accuracy of the traffic trend prediction algorithm, culling is required.
The average flow rate of change is calculated for several adjacent sampling time points. In this embodiment, the average rate of change of five adjacent sampling time points of the link is calculated
Namely, the method comprises the following steps:
wherein, S represents the total link flow, t is the time to be predicted, and Δ t is the sampling period.
Calculating the flow value at the next moment according to the maximum flow rate change rate as a second flow prediction result, namely, the second flow prediction result
Comprises the following steps:
wherein S ist-ΔtRepresenting the total flow of the link at time t-at.
In this embodiment, the analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result includes:
calculating a difference between the first flow prediction result and the second flow prediction result;
if the difference is smaller than the preset threshold, the difference between the results obtained by the two prediction methods is not large, and therefore the first flow result which is close to the actual flow and has higher possibility is selected as the final prediction result.
If the difference is not smaller than the preset threshold, the result obtained by the two prediction methods is relatively large, in order to avoid the influence of deviation of a certain prediction result, the first flow prediction result and the second flow prediction result are subjected to weighted calculation based on the preset weight, and the weighted calculation result is used as a final prediction result, so that the correction effect on the prediction result is achieved, and the prediction accuracy is improved.
In this embodiment, the method for managing the power wireless heterogeneous network further includes, after acquiring current traffic data at nodes of each link of the power wireless heterogeneous network, establishing a link load mapping table;
the link load mapping table is shown in table 1, and includes a link number, a link load value, and a preset traffic threshold.
TABLE 1
| Link numbering | Link load value | Preset flow threshold |
| S12 | 40％ | 75％ |
| S13 | 60％ | 75％ |
| S14 | 20％ | 75％ |
| S23 | 20％ | 75％ |
| … | … | … |
In this embodiment, the link load value in the link load mapping table is updated in real time along with the current traffic data collected in S1. The preset flow threshold refers to a bandwidth utilization threshold.
In this embodiment, if the final prediction result exceeds the preset traffic threshold, performing traffic balancing processing on the power wireless heterogeneous network includes:
and judging whether the final prediction result exceeds a preset flow threshold value of the link or not by inquiring a link load mapping table, and if so, searching whether an available link exists according to the routing path. For example, S13Is 80%, i.e. S13The ratio of the traffic to the bandwidth at the next time is 80%, and it can be known from table 1 that the ratio exceeds the preset traffic threshold by 75%, so that whether there is an available link is found according to a routing path, that is, a link path obtained through the topology structure of the power wireless heterogeneous network. In this embodiment, the route with the link load value not exceeding 50% is selected as the available link, and as can be seen from table 1, S12、S14、S23All can not exceed 50%, therefore, nodes 1-2 are connectedThe routes of node 3-node 4 are taken as available links.
And if the available link is found, acquiring the load capacity of the available link, and if the load capacity does not exceed the preset proportion of the route, sending echo information to inhibit the source IP flow. In a communication network, echoes can attenuate delayed original signals, so the residual bandwidth of the available link cannot bear S13At all flow rates of (2), S is suppressed by echo information13Avoiding link overload.
And if the load capacity exceeds the preset proportion of the route and more than one available link is found, proportionally distributing the flow of the power wireless heterogeneous network according to the load capacity of the available link.
Example two
As shown in fig. 2, the present invention provides a power wireless heterogeneous network management apparatus 6 based on traffic balancing, including:
the acquisition unit 61: the wireless heterogeneous network traffic monitoring system is used for collecting current traffic data at nodes of each link of the wireless heterogeneous network;
the first prediction unit 62: the flow prediction device is used for generating an input matrix according to current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
the second prediction unit 63: the flow prediction method comprises the steps of analyzing the change trend of current flow data to obtain a second flow prediction result of a link;
the analyzing unit 64: the flow rate prediction device is used for analyzing the first flow rate prediction result and the second flow rate prediction result to obtain a final prediction result;
the equalizing unit 65: and the method is used for carrying out flow equalization processing on the electric power wireless heterogeneous network if the final prediction result exceeds a preset flow threshold value.
In this embodiment, the current traffic data at a node is collected by a wireless terminal and a wireless base station network manager, where the current traffic data includes total link traffic, node traffic of an incoming and outgoing link, traffic of an incoming and outgoing port, a link load rate, and source IP traffic.
In this embodiment, the first prediction unit 62 is specifically configured to:
and identifying the traffic type at each node on the link based on a fuzzy C-means algorithm. The data set X is composed of the current flow data acquired at the ith sampling time in S1i＝{x1,x2,…,xn}，x1、x2、…、xnRespectively the total flow among link nodes, the flow of the in-out port, the flow of the link load rate and the source IP flow, forming a characteristic matrix by taking the data as the characteristics of the flows, and enabling the flows on the links to belong to a clustering loss function J of various flow types based on a fuzzy C mean algorithmmMinimization, the cluster loss function JmThe following were used:
wherein, XiFor the current flow data obtained at the ith sampling instant, cjM is a preset flow type clustering center, and is a constant for artificially setting and controlling the fuzzy degree of a clustering result, and the value range is more than or equal to 1 and less than or equal to m<∞，uijThe membership degree of the current flow data obtained at the ith sampling moment, which belongs to the jth flow, is n, the number of data sets, and c, the total number of flow types; the value ranges of n and c are positive integers, and X isi、cjThe value ranges of (A) are all positive numbers. Those skilled in the art will know how to calculate the degree of membership by the fuzzy C-means algorithm, and will not be described herein. And repeating the calculation process until the membership degree is stabilized in a preset range, and dividing the current flow data obtained at the ith sampling moment into flow types with the highest membership degree.
And forming an input matrix by the current data traffic, and inputting the input matrix into the graph neural network corresponding to the traffic type according to the identified traffic type. Because different types of traffic generally have different change rules, in consideration of the influence of the traffic type on the prediction result, different graph neural networks are trained in advance according to different traffic types in the embodiment, so that the prediction accuracy is further improved.
And finally, outputting a first flow prediction result of the link through the neural network. The traditional neural network faces a great challenge in processing a power wireless heterogeneous network with a complex topological structure, and because the number of adjacent nodes of each node is different and the adjacent nodes have an incidence relation, the graph neural network can describe the relation of each node in the power wireless heterogeneous network through the top point and the edge in the graph.
The graph neural network trained in advance comprises vertexes, edges connecting the vertexes and relationship characteristics of the edges, in this embodiment, the vertexes are nodes in a link, traffic data at the nodes are characteristics of the vertexes, the edges are topological relationships between adjacent nodes, and a traffic mapping relationship between two nodes is the relationship characteristics of the edges. And sequentially passing the input matrix through an input layer, a hidden layer and an output layer of the graph neural network to obtain a first flow prediction result of the link, wherein the first flow prediction result is the flow of the link at the next moment.
In this embodiment, the second prediction unit 63 is specifically configured to:
and rejecting data of the link at the peak-to-valley value of the selected time period. This situation is random and unsustainable due to certain emergencies, resulting in a surge of network traffic. Because such an incident affects the accuracy of the traffic trend prediction algorithm, culling is required.
The average flow rate of change is calculated for several adjacent sampling time points. In this embodiment, the average rate of change of five adjacent sampling time points of the link is calculated
Namely, the method comprises the following steps:
wherein, S represents the total link flow, t is the time to be predicted, and Δ t is the sampling period.
Calculating the flow at the next moment according to the maximum flow change rateThe value being a second flow prediction, i.e. having a second flow prediction
Comprises the following steps:
wherein S ist-ΔtRepresenting the total flow of the link at time t-at.
In the present embodiment, the analyzing unit 64 is configured to:
calculating a difference between the first flow prediction result and the second flow prediction result;
if the difference is smaller than the preset threshold, the difference between the results obtained by the two prediction methods is not large, and therefore the first flow result which is close to the actual flow and has higher possibility is selected as the final prediction result.
If the difference is not smaller than the preset threshold, the result obtained by the two prediction methods is relatively large, in order to avoid the influence of deviation of a certain prediction result, the first flow prediction result and the second flow prediction result are subjected to weighted calculation based on the preset weight, and the weighted calculation result is used as a final prediction result, so that the correction effect on the prediction result is achieved, and the prediction accuracy is improved.
In this embodiment, the power wireless heterogeneous network management apparatus 6 further includes a mapping unit, configured to establish a link load mapping table after acquiring current traffic data at nodes of each link of the power wireless heterogeneous network;
the link load mapping table is shown in table 1, and includes a link number, a link load value, and a preset traffic threshold.
TABLE 1
| Link numbering | Link load value | Preset flow threshold |
| S12 | 40％ | 75％ |
| S13 | 60％ | 75％ |
| S14 | 20％ | 75％ |
| S23 | 20％ | 75％ |
| … | … | … |
In this embodiment, the link load value in the link load mapping table is updated in real time along with the current traffic data collected in the collecting unit 61. The preset flow threshold refers to a bandwidth utilization threshold.
In this embodiment, the equalizing unit 65 is configured to:
and judging whether the final prediction result exceeds a preset flow threshold value of the link or not by inquiring a link load mapping table, and if so, searching whether an available link exists according to the routing path. For example, S13Is 80%, i.e. S13The ratio of the flow and the bandwidth at the next moment is 80 percentAs can be seen from table 1, the preset flow threshold is exceeded by 75%, and therefore, whether there is an available link is found according to a routing path, that is, a link path obtained through the topology structure of the power wireless heterogeneous network. In this embodiment, the route with the link load value not exceeding 50% is selected as the available link, and as can be seen from table 1, S12、S14、S23Each may not exceed 50%, and the routes of node 1-node 2-node 3-node 4 are therefore taken as available links.
And if the available link is found, acquiring the load capacity of the available link, and if the load capacity does not exceed the preset proportion of the route, sending echo information to inhibit the source IP flow. In a communication network, echoes can attenuate delayed original signals, so the residual bandwidth of the available link cannot bear S13At all flow rates of (2), S is suppressed by echo information13Avoiding link overload.
And if the load capacity exceeds the preset proportion of the route and more than one available link is found, proportionally distributing the flow of the power wireless heterogeneous network according to the load capacity of the available link.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The power wireless heterogeneous network management method based on flow balance is characterized by comprising the following steps:
acquiring current flow data at nodes of each link of the power wireless heterogeneous network;
generating an input matrix according to the current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
analyzing the variation trend of the current flow data to obtain a second flow prediction result of the link;
analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result;
and if the final prediction result exceeds a preset flow threshold value, carrying out flow equalization processing on the electric power wireless heterogeneous network.
2. The power wireless heterogeneous network management method based on traffic balancing according to claim 1, wherein the current traffic data includes total link traffic, ingress and egress link node traffic, ingress and egress port traffic, link load rate, and source IP traffic.
3. The method for managing the power wireless heterogeneous network based on the flow balance according to claim 1, wherein the generating an input matrix according to the current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network comprises:
identifying the flow type of each node on the link based on a fuzzy C-means algorithm;
forming an input matrix by the current data traffic, and inputting the input matrix into a graph neural network corresponding to the traffic type according to the identified traffic type;
and outputting the first flow prediction result of the link through the neural network.
4. The method for managing the power wireless heterogeneous network based on the traffic balance according to claim 1, wherein the analyzing a variation trend of the current traffic data to obtain a second traffic prediction result of the link comprises:
rejecting data of the link at a peak-to-valley value of the selected time period;
calculating the average flow rate change of a plurality of adjacent sampling time points;
and calculating the flow value at the next moment according to the maximum flow change rate to serve as a second flow prediction result.
5. The method for managing the power wireless heterogeneous network based on the flow balance according to claim 1, wherein the analyzing the first flow prediction result and the second flow prediction result to obtain a final prediction result comprises:
calculating a difference between the first flow prediction result and the second flow prediction result;
if the difference value is smaller than a preset threshold value, taking a first flow prediction result as a final prediction result;
and if the difference is not smaller than the preset threshold, performing weighted calculation on the first flow prediction result and the second flow prediction result based on preset weight, and taking the weighted calculation result as a final prediction result.
6. The power wireless heterogeneous network management method based on flow balance according to claim 1, further comprising establishing a link load mapping table after collecting current flow data at nodes of each link of the power wireless heterogeneous network;
the link load mapping table comprises a link number, a link load value and a preset flow threshold value.
7. The method as claimed in claim 6, wherein if the final prediction result exceeds a preset traffic threshold, performing traffic balancing on the power wireless heterogeneous network includes:
judging whether the final prediction result exceeds a preset flow threshold value of the link or not by inquiring a link load mapping table;
if the traffic exceeds the preset traffic threshold, searching whether an available link exists according to the routing path;
if the available link is found, acquiring the load capacity of the available link, and if the load capacity does not exceed the preset proportion of the route, sending echo information to inhibit the source IP flow;
and if the load capacity exceeds the preset proportion of the route and more than one available link is found, proportionally distributing the flow of the power wireless heterogeneous network according to the load capacity of the available link.
8. The wireless heterogeneous power network management device based on flow balance is characterized by comprising:
a collecting unit: the wireless heterogeneous network traffic monitoring system is used for collecting current traffic data at nodes of each link of the wireless heterogeneous network;
a first prediction unit: the flow prediction device is used for generating an input matrix according to current flow data, inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network;
a second prediction unit: the flow prediction method comprises the steps of analyzing the change trend of current flow data to obtain a second flow prediction result of a link;
an analysis unit: the flow rate prediction device is used for analyzing the first flow rate prediction result and the second flow rate prediction result to obtain a final prediction result;
an equalization unit: and the method is used for carrying out flow equalization processing on the electric power wireless heterogeneous network if the final prediction result exceeds a preset flow threshold value.
9. The traffic balance-based power wireless heterogeneous network management device according to claim 8, wherein the first prediction unit is configured to:
identifying the flow type of each node on the link based on a fuzzy C-means algorithm;
weighting current flow data at the nodes according to preset flow type weights, and forming an input matrix by the weighted current flow data according to the sequence of acquisition time;
and inputting the input matrix into a pre-trained graph neural network, and outputting a first flow prediction result of a link through the graph neural network.
10. The traffic balance-based power wireless heterogeneous network management device according to claim 8, wherein the second prediction unit is configured to:
rejecting data of the link at a peak-to-valley value of the selected time period;
calculating the average flow rate change of a plurality of adjacent sampling time points;
and calculating the flow value at the next moment according to the maximum flow change rate to serve as a second flow prediction result.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09179902A (en) * | 1995-12-25 | 1997-07-11 | Hitachi Ltd | Highly reliable physical distribution managing method and its system |
| CN104410582A (en) * | 2014-12-10 | 2015-03-11 | 国家电网公司 | Traffic balancing method for electric power communication network based on traffic prediction |
| CN110084359A (en) * | 2019-03-20 | 2019-08-02 | 山东师范大学 | Dynamic intuitionistic fuzzy Cognitive Map construction method, Time Series Forecasting Methods and system |
| CN110995520A (en) * | 2020-02-28 | 2020-04-10 | 清华大学 | Network flow prediction method and device, computer equipment and readable storage medium |
| CN111092862A (en) * | 2019-11-29 | 2020-05-01 | 中国电力科学研究院有限公司 | Method and system for detecting abnormal communication flow of power grid terminal |
-
2020
- 2020-12-22 CN CN202011527098.8A patent/CN112766633B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09179902A (en) * | 1995-12-25 | 1997-07-11 | Hitachi Ltd | Highly reliable physical distribution managing method and its system |
| CN104410582A (en) * | 2014-12-10 | 2015-03-11 | 国家电网公司 | Traffic balancing method for electric power communication network based on traffic prediction |
| CN110084359A (en) * | 2019-03-20 | 2019-08-02 | 山东师范大学 | Dynamic intuitionistic fuzzy Cognitive Map construction method, Time Series Forecasting Methods and system |
| CN111092862A (en) * | 2019-11-29 | 2020-05-01 | 中国电力科学研究院有限公司 | Method and system for detecting abnormal communication flow of power grid terminal |
| CN110995520A (en) * | 2020-02-28 | 2020-04-10 | 清华大学 | Network flow prediction method and device, computer equipment and readable storage medium |
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