CN115225560B - Route planning method in power communication service - Google Patents
Route planning method in power communication service Download PDFInfo
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- CN115225560B CN115225560B CN202210837342.3A CN202210837342A CN115225560B CN 115225560 B CN115225560 B CN 115225560B CN 202210837342 A CN202210837342 A CN 202210837342A CN 115225560 B CN115225560 B CN 115225560B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/02—Topology update or discovery
- H04L45/08—Learning-based routing, e.g. using neural networks or artificial intelligence
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
- H04L45/121—Shortest path evaluation by minimising delays
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
- H04L45/124—Shortest path evaluation using a combination of metrics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The application provides a route planning method in power communication service, which belongs to the technical field of power system communication and specifically comprises the following steps: carrying out flow prediction by adopting a prediction model based on a PSO-GBDT algorithm based on a data set to obtain predicted flow, wherein the data set comprises service flow data of the past 24 hours and service flow data of the same moment in a week; based on the predicted flow, planning a path by adopting a routing planning algorithm based on CA-Q-Learning to obtain a planning result, and performing delay evaluation based on the planning result to obtain an evaluation result; and selecting a planning result meeting the time delay limit value according to the evaluation result, and selecting a planning result with minimum time delay, the equilibrium of which is smaller than a first threshold value, from the planning results meeting the time delay limit value, so that the requirement of the equilibrium degree is ensured, and the final planning result can realize the minimum time delay.
Description
Technical Field
The application belongs to the technical field of power system communication, and particularly relates to a route planning method in power communication service.
Background
The electric power communication network is used as an important infrastructure for carrying information interaction among electric power systems, serves all links of production and management of the electric power systems, and effectively ensures safe, stable, economical and efficient operation of the electric power network. With the continuous deep construction of smart power grids, more and more services are carried by the power communication network, and once the power communication network fails, the safe and stable operation of a large power grid is directly affected, so that how to reasonably distribute service routes is an important content in the current power communication research field. Because of the randomness of the failures, the shortest route approach commonly used can concentrate a lot of traffic into certain links, making these links high-risk links. Therefore, the balanced distribution of the service is set as an optimization target of the power communication routing mode, and a corresponding routing optimization algorithm is provided, so that the operation risk of the power communication network can be reduced, the reliability of the communication network and the utilization rate of communication resources are improved, and the method has practical significance for guaranteeing the safe, stable and reliable operation of a large power grid.
The authors Yu Peng of the 'intelligent route distribution method facing dynamic service demands of the electric power Internet of things' in the 'Shuoshi paper' can predict service flow in real time and accurately, efficiently distribute bandwidth for the service, and meanwhile, distribute a route with minimum comprehensive risks for the service based on the predicted service flow and the distributed bandwidth, so that reliable and stable operation of an electric power system is effectively ensured, but evaluation of balance degree can be performed only after time delay in path planning in communication service demands of the electric power system meets requirements, if the two are mixed, a scheme with optimal time ductility can not be obtained, and when service flow prediction is performed, service flow data at the moment of contract are not combined, prediction accuracy is not high only by adopting service flow data of 24 hours in the past, and an initial value optimizing is not performed by adopting a prediction model based on a single algorithm, so that calculation efficiency is not high.
Aiming at the technical problems, the application provides a route planning method in power communication service.
Disclosure of Invention
In order to achieve the purpose of the application, the application adopts the following technical scheme:
according to one aspect of the present application, a method for route planning in power communication traffic is provided.
The route planning method in the power communication service is characterized by comprising the following steps:
s11, carrying out flow prediction by adopting a prediction model based on a PSO-GBDT algorithm based on a data set to obtain predicted flow, wherein the data set comprises service flow data of the past 24 hours and service flow data of the same moment in a week.
And S12, based on the predicted flow, planning a path by adopting a routing planning algorithm based on CA-Q-Learning to obtain a planning result, and performing delay evaluation based on the planning result to obtain an evaluation result.
S13, selecting a planning result meeting the time delay limit value according to the evaluation result, and selecting a planning result with minimum time delay, wherein the equilibrium degree of the planning result is smaller than a first threshold value, from the planning results meeting the time delay limit value.
The method comprises the steps of firstly predicting flow by adopting a prediction model based on a PSO-GBDT algorithm, wherein a data set at the moment comprises service flow data of 24 hours in the past, and service flow data at the same moment in one week, so that the problem that the original prediction result is inaccurate due to the fact that the service flow data of 24 hours in the past are only relied on is solved, the prediction precision and reliability are further improved by combining the service flow data at the same moment in one week, the initial value of the GBDT algorithm is optimized by adopting the PSO algorithm, the problem that the original prediction model based on a single algorithm is not optimized for the initial value, the calculation efficiency is low is further improved, the calculation efficiency and the calculation precision are improved, the convergence speed is improved, after the predicted flow is obtained, the planning result is planned by adopting a routing planning algorithm optimized based on a CA algorithm, the delay evaluation is adopted to obtain the evaluation result, and the obtained equalization degree after the planning result meeting the time requirement is selected, the equalization degree is calculated, the first threshold value is set, the equalization degree meets the equalization degree requirement is met, the optimal equalization scheme is obtained when the planning result meeting the minimum time requirement is selected, the equalization scheme is obtained, and the equalization scheme is not met at the time, and the optimal scheme is obtained, and the equalization scheme is met.
By optimizing the initial value of the GBDT algorithm by adopting the PSO algorithm, the problem that the calculation efficiency is low due to the fact that the initial value is not optimized by adopting the original prediction model based on a single algorithm is solved, the calculation efficiency and the calculation precision are further improved, and the convergence rate is improved. The method has the advantages that the accuracy and reliability of prediction are further improved, the robustness of the whole model is improved through the service flow data at the same time in one week based on the service flow data of the past 24 hours, the planning result is obtained through planning of paths by adopting a Q-Learning routing algorithm optimized based on a CA algorithm, the prediction efficiency and accuracy of the algorithm are improved, the overall efficiency is further improved, the planning result meeting the time ductility limit value is selected firstly, and the planning result with the minimum time delay of which the equilibrium degree is smaller than the first threshold value is selected on the basis, so that the final planning result meets the requirement of the equilibrium degree, and the overall time delay is minimized.
Further, the data set includes traffic data of past 24 hours, average traffic data of past 24 hours, traffic data of the same time in one week, power load data of past 24 hours, and temperature.
Since the influence on the service flow data is not only related to the past service flow data but also related to the size of the power load data and related to the fluctuation of the power load data caused by temperature, the whole prediction accuracy and robustness are further improved by adding the above amount into the data set, and in addition, the influence of the prediction misalignment problem caused by the huge fluctuation of the service flow in a single day is reduced by adopting not only the past 24-hour service flow data but also the average flow data and the past 24-hour average service flow data.
Further, the traffic flow data of the past 24 hours is traffic flow data of each of the past 24 hours, and the power load data of the past 24 hours is power load data of each of the past 24 hours.
The further technical scheme is characterized in that principal component analysis is carried out on the data set by adopting a principal component analysis method based on PCA, dimension reduction is carried out on the data set to obtain a dimension reduction data set, and flow prediction is carried out on the basis of the dimension reduction data set.
The principal component analysis is performed by adopting the PCA principal component analysis method, so that the data set is further reduced, and the prediction precision is further improved on the basis of ensuring the prediction precision.
The PSO-GBDT algorithm is adopted to optimize the learning rate of the GBDT algorithm and the number of basic models.
The further technical scheme is characterized in that the planning result meeting the time ductility limit is a planning result with the time ductility smaller than a first time ductility threshold.
The further technical scheme is that the first delay threshold is determined according to the importance degree of the predicted flow and the scale of the predicted flow.
By setting the first delay threshold, the planning result can be accurately ensured to meet the delay requirement from the data angle, and the first delay threshold is determined by taking the importance degree of the predicted flow and the scale of the predicted flow as references, so that the reliability and the accuracy of the threshold are also ensured.
The further technical scheme is that the evaluation process of the balance degree is as follows:
s21, extracting the planning result meeting the time delay limit value;
s22, giving an equilibrium degree weight to the importance degree of the predicted flow in different lines in the planning result;
and S23, calculating the balance degree based on the balance degree weight and the planning result, and extracting all the planning results with the balance degree smaller than a first threshold value.
The equalization degree weight is given according to the importance degree of the predicted flow, so that the equalization degree is evaluated by considering not only the equalization problem of single flow, but also the more important the flow data, for example, the importance of the transmission of the flow data of different data centers and the importance of the transmission of the flow of the transformer substation and the data centers are certainly inconsistent, and the equalization degree obtained by evaluation is more reliable and effective.
The further technical scheme is that the specific calculation formula of the equilibrium degree is as follows:
wherein N is i K is the predicted traffic that passes over the ith link i For the weight on the predicted traffic passing on the ith link, W is the total number of links, P is the sum ofThe degree of balance of the device,is the average of the predicted flows.
In another aspect, an embodiment of the present application provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed in a computer, causes the computer to execute a method of route planning method in an electric power communication service as described above.
In another aspect, an embodiment of the present application provides a computer program product, where the computer program product stores instructions that, when executed by a computer, cause the computer to implement a method for route planning in a power communication service as described above. .
Drawings
The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a flowchart of a route planning method in power communication traffic according to embodiment 1.
Fig. 2 is a flowchart of an evaluation process of the degree of balance according to embodiment 1.
Fig. 3 is a structural diagram of a computer-readable storage medium according to embodiment 2.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
The electric power communication network is used as an important infrastructure for carrying information interaction among electric power systems, serves all links of production and management of the electric power systems, and effectively ensures safe, stable, economical and efficient operation of the electric power network. With the continuous deep construction of smart power grids, more and more services are carried by the power communication network, and once the power communication network fails, the safe and stable operation of a large power grid is directly affected, so that how to reasonably distribute service routes is an important content in the current power communication research field. Because of the randomness of the failures, the shortest route approach commonly used can concentrate a lot of traffic into certain links, making these links high-risk links. Therefore, the balanced distribution of the service is set as an optimization target of the power communication routing mode, and a corresponding routing optimization algorithm is provided, so that the operation risk of the power communication network can be reduced, the reliability of the communication network and the utilization rate of communication resources are improved, and the method has practical significance for guaranteeing the safe, stable and reliable operation of a large power grid.
The authors Yu Peng of the 'intelligent route distribution method facing dynamic service demands of the electric power Internet of things' in the 'Shuoshi paper' can predict service flow in real time and accurately, efficiently distribute bandwidth for the service, and meanwhile, distribute a route with minimum comprehensive risks for the service based on the predicted service flow and the distributed bandwidth, so that reliable and stable operation of an electric power system is effectively ensured, but evaluation of balance degree can be performed only after time delay in path planning in communication service demands of the electric power system meets requirements, if the two are mixed, a scheme with optimal time ductility can not be obtained, and when service flow prediction is performed, service flow data at the moment of contract are not combined, prediction accuracy is not high only by adopting service flow data of 24 hours in the past, and an initial value optimizing is not performed by adopting a prediction model based on a single algorithm, so that calculation efficiency is not high.
Example 1
To solve the above-mentioned problems, according to an aspect of the present application, as shown in fig. 1, a route planning method in power communication service is provided.
The route planning method in the power communication service is characterized by comprising the following steps:
s11, carrying out flow prediction by adopting a prediction model based on a PSO-GBDT algorithm based on a data set to obtain predicted flow, wherein the data set comprises service flow data of the past 24 hours and service flow data of the same moment in a week.
And S12, based on the predicted flow, planning a path by adopting a routing planning algorithm based on CA-Q-Learning to obtain a planning result, and performing delay evaluation based on the planning result to obtain an evaluation result.
S13, selecting a planning result meeting the time delay limit value according to the evaluation result, and selecting a planning result with minimum time delay, wherein the equilibrium degree of the planning result is smaller than a first threshold value, from the planning results meeting the time delay limit value.
For example, in all the planning results, the time delay in the first planning result is 20ms, the equalization degree is 0.8, the time delay in the second planning result is 10ms, the equalization degree is 0.9, the time delay in the third planning result is 5ms, the equalization degree is 0.95, the first threshold is 0.91, and the selected result is the second planning result.
The method comprises the steps of firstly predicting flow by adopting a prediction model based on a PSO-GBDT algorithm, wherein a data set at the moment comprises service flow data of 24 hours in the past, and service flow data at the same moment in one week, so that the problem that the original prediction result is inaccurate due to the fact that the service flow data of 24 hours in the past are only relied on is solved, the prediction precision and reliability are further improved by combining the service flow data at the same moment in one week, the initial value of the GBDT algorithm is optimized by adopting the PSO algorithm, the problem that the original prediction model based on a single algorithm is not optimized for the initial value, the calculation efficiency is low is further improved, the calculation efficiency and the calculation precision are improved, the convergence speed is improved, after the predicted flow is obtained, the planning result is planned by adopting a routing planning algorithm optimized based on a CA algorithm, the delay evaluation is adopted to obtain the evaluation result, and the obtained equalization degree after the planning result meeting the time requirement is selected, the equalization degree is calculated, the first threshold value is set, the equalization degree meets the equalization degree requirement is met, the optimal equalization scheme is obtained when the planning result meeting the minimum time requirement is selected, the equalization scheme is obtained, and the equalization scheme is not met at the time, and the optimal scheme is obtained, and the equalization scheme is met.
By optimizing the initial value of the GBDT algorithm by adopting the PSO algorithm, the problem that the calculation efficiency is low due to the fact that the initial value is not optimized by adopting the original prediction model based on a single algorithm is solved, the calculation efficiency and the calculation precision are further improved, and the convergence rate is improved. The method has the advantages that the accuracy and reliability of prediction are further improved, the robustness of the whole model is improved through the service flow data at the same time in one week based on the service flow data of the past 24 hours, the planning result is obtained through planning of paths by adopting a Q-Learning routing algorithm optimized based on a CA algorithm, the prediction efficiency and accuracy of the algorithm are improved, the overall efficiency is further improved, the planning result meeting the time ductility limit value is selected firstly, and the planning result with the minimum time delay of which the equilibrium degree is smaller than the first threshold value is selected on the basis, so that the final planning result meets the requirement of the equilibrium degree, and the overall time delay is minimized.
In another possible embodiment, the data set includes traffic data for the last 24 hours, average traffic data for the last 24 hours, traffic data for the same time of the week, power load data for the last 24 hours, and temperature.
Since the influence on the service flow data is not only related to the past service flow data but also related to the size of the power load data and related to the fluctuation of the power load data caused by temperature, the whole prediction accuracy and robustness are further improved by adding the above amount into the data set, and in addition, the influence of the prediction misalignment problem caused by the huge fluctuation of the service flow in a single day is reduced by adopting not only the past 24-hour service flow data but also the average flow data and the past 24-hour average service flow data.
In another possible embodiment, the past 24 hours of traffic flow data is the past 24 hours of traffic flow data and the past 24 hours of power load data is the past 24 hours of power load data.
In another possible embodiment, principal component analysis of the dataset based on PCA principal component analysis is used, dimension reduction of the dataset is performed to obtain a dimension-reduced dataset, and flow prediction is performed based on the dimension-reduced dataset.
The principal component analysis is performed by adopting the PCA principal component analysis method, so that the data set is further reduced, and the prediction precision is further improved on the basis of ensuring the prediction precision.
In another possible embodiment, the PSO-GBDT algorithm is a PSO algorithm that optimizes the learning rate of the GBDT algorithm and the number of base models.
In a further possible embodiment, the planning result satisfying the time ductility limit is a planning result having the time delay smaller than a first time delay threshold.
In another possible embodiment, the first delay threshold is determined according to the importance of the predicted traffic and the scale of the predicted traffic.
By setting the first delay threshold, the planning result can be accurately ensured to meet the delay requirement from the data angle, and the first delay threshold is determined by taking the importance degree of the predicted flow and the scale of the predicted flow as references, so that the reliability and the accuracy of the threshold are also ensured.
In another possible embodiment, as shown in fig. 2, the evaluation process of the equalization degree is:
s21, extracting the planning result meeting the time delay limit value;
s22, giving an equilibrium degree weight to the importance degree of the predicted flow in different lines in the planning result;
and S23, calculating the balance degree based on the balance degree weight and the planning result, and extracting all the planning results with the balance degree smaller than a first threshold value.
The equalization degree weight is given according to the importance degree of the predicted flow, so that the equalization degree is evaluated by considering not only the equalization problem of single flow, but also the more important the flow data, for example, the importance of the transmission of the flow data of different data centers and the importance of the transmission of the flow of the transformer substation and the data centers are certainly inconsistent, and the equalization degree obtained by evaluation is more reliable and effective.
In another possible embodiment, the specific calculation formula of the equalization degree is:
wherein N is i K is the predicted traffic that passes over the ith link i For the weight on the predicted traffic passing on the ith link, W is the total number of links, P is the degree of equalization,is the average of the predicted flows.
Example 2
As shown in fig. 3, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, which when executed in a computer, causes the computer to perform a method of route planning in an electric power communication service as described above.
Example 3
An embodiment of the present application provides a computer program product, where the computer program product stores instructions that, when executed by a computer, cause the computer to implement a method for route planning in an electric power communication service as described above.
In embodiments of the present application, the term "plurality" refers to two or more, unless explicitly defined otherwise. The terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly attached, detachably attached, or integrally attached. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to specific circumstances.
In the description of the embodiments of the present application, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience in describing the embodiments of the present application and to simplify the description, and do not indicate or imply that the devices or units referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the embodiments of the present application.
In the description of the present specification, the terms "one embodiment," "a preferred embodiment," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above is only a preferred embodiment of the present application and is not intended to limit the embodiment of the present application, and various modifications and variations can be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.
Claims (4)
1. The route planning method in the power communication service is characterized by comprising the following steps:
s11, carrying out flow prediction by adopting a prediction model based on a PSO-GBDT algorithm based on a data set to obtain predicted flow, wherein the data set comprises service flow data of the past 24 hours and service flow data of the same moment in a week;
the data set comprises service flow data of past 24 hours, average service flow data of past 24 hours, service flow data of the same moment in a week, power load data of past 24 hours and temperature;
s12, based on the predicted flow, planning a path by adopting a routing planning algorithm based on CA-Q-Learning to obtain a planning result, and performing delay evaluation based on the planning result to obtain an evaluation result;
s13, selecting a planning result meeting the time delay limit value according to the evaluation result, and selecting a planning result with minimum time delay, wherein the equilibrium degree of the planning result is smaller than a first threshold value, from the planning results meeting the time delay limit value;
the evaluation process of the balance degree comprises the following steps:
s21, extracting the planning result meeting the time delay limit value;
s22, giving an equilibrium degree weight to the importance degree of the predicted flow in different lines in the planning result;
s23, calculating the balance degree based on the balance degree weight and the planning result, and extracting all the planning results with the balance degree smaller than a first threshold value;
the PSO-GBDT algorithm is that a PSO algorithm is adopted to optimize the learning rate of the GBDT algorithm and the number of basic models;
the planning result meeting the time ductility limit is the planning result with the time ductility smaller than a first time delay threshold;
the first delay threshold is determined according to the importance degree of the predicted flow and the scale of the predicted flow;
the specific calculation formula of the equilibrium degree is as follows:
wherein N is i K is the predicted traffic that passes over the ith link i For the weight on the predicted traffic passing on the ith link, W is the total number of links, P is the degree of equalization,is the average of the predicted flows.
2. A method of route planning in power communication traffic according to claim 1 wherein said traffic flow data for the past 24 hours is traffic flow data for each hour of the past 24 hours and the power load data for the past 24 hours is power load data for each hour of the past 24 hours.
3. The route planning method according to claim 1, wherein principal component analysis is performed on the data set based on a principal component analysis method of PCA, dimension reduction is performed on the data set to obtain a dimension-reduced data set, and flow prediction is performed based on the dimension-reduced data set.
4. A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method of route planning method in an electrical communication service as claimed in any of claims 1-3.
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