CN115086187A - Power communication channel planning method and device based on reinforcement learning and storage medium - Google Patents
Power communication channel planning method and device based on reinforcement learning and storage medium Download PDFInfo
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- 238000004891 communication Methods 0.000 title claims abstract description 80
- 230000002787 reinforcement Effects 0.000 title claims abstract description 40
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- 238000004590 computer program Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 10
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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
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G06Q50/40—
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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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- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a power communication channel planning method and device based on reinforcement learning and a storage medium. The method comprises the following steps: acquiring parameters of a starting station, an ending station and a communication channel; and inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning, and outputting an optimal communication channel. The invention improves the efficiency of planning the electric power communication channel for bearing the stability control service.
Description
Technical Field
The invention relates to the technical field of power communication channel planning, in particular to a power communication channel planning method and device based on reinforcement learning and a storage medium.
Background
And the electric power communication scheduling personnel plan the electric power communication network routing circuit bearing the stability control service in a manual mode. The routing route planning is carried out manually, and once the network scale is overlarge, the planning takes long time due to the large network complexity; in addition, after the number of routing nodes exceeds dozens, the manual work may not exhaust the whole number of paths, and the optimal path cannot be selected.
Disclosure of Invention
The invention provides a power communication channel planning method, a power communication channel planning device and a power communication channel planning storage medium based on reinforcement learning, and the efficiency of power communication channel planning for bearing stability control services is improved.
An embodiment of the invention provides a power communication channel planning method based on reinforcement learning, which comprises the following steps:
acquiring parameters of a starting station, an ending station and a communication channel;
and inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning, and outputting an optimal communication channel.
Further, the communication channel parameters include a maximum channel number, a port type, a bandwidth, a network type, a maximum circuit number of a transmission segment, a maximum network element number, a maximum kilometer length, a routing mode, whether SNCP is configured, a reserved fiber core number, and a maximum attenuation.
Further, the communication channel prediction model based on deep reinforcement learning is established according to the following models:
Q(s,c)＝Q(s,c)+c[Re+Re·maxc·Q(s’,c’)-Q(s,c)]
q represents a reinforcement learning model, s represents a current state, c represents input data corresponding to the current state, s 'represents a next state, c' represents input data corresponding to the next state, and Re represents a reward value.
Further, the communication channel prediction model based on deep reinforcement learning is trained according to the following steps:
step 1: initializing a Q value table, a learning rate, a discount factor and an exploration rate;
step 2: randomly selecting a group of training data from a training set as an initial state s to be input into the deep reinforcement learning-based communication channel prediction model;
and step 3: judging whether the current step number is larger than the total step number; if not, acquiring a random number num between 0 and 1; if yes, go to step 7;
and 4, step 4: judging whether the random number num is greater than the exploration speed alpha or not; if so, selecting the action corresponding to the maximum Q value of the current state; if not, randomly selecting an action;
and 5: executing the action selected in the step 4 to obtain the next state s' and reward of the model, and updating the Q value table;
step 6: setting s' to a current state; judging whether s' is in a final state, if so, entering the next step; if not, turning to the step 3;
and 7: updating the exploration rate alpha;
and 8: judging whether the current learning times are larger than the total learning times or not; if yes, ending the training; if not, go to step 2.
Another embodiment of the invention provides a power communication channel planning device for reinforcement learning, which comprises a planning data acquisition module and a communication channel planning module;
the planning data acquisition module is used for acquiring parameters of a starting station, an ending station and a communication channel;
and the communication channel planning module is used for inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning and outputting an optimal communication channel.
Another embodiment of the present invention provides a readable storage medium, where the readable storage medium includes a stored computer program, and when the computer program is executed, the computer program controls a device where the readable storage medium is located to execute the reinforcement learning power communication channel planning method according to any one of the method embodiments of the present invention.
The embodiment of the invention has the following beneficial effects:
the invention provides a power communication channel planning method, a device and a storage medium based on reinforcement learning. The invention realizes automatic planning of the electric power communication channel for bearing the stability control service and improves the efficiency of the electric power communication channel planning.
Drawings
Fig. 1 is a schematic flowchart of a power communication channel planning method based on reinforcement learning according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric power communication channel planning apparatus based on reinforcement learning according to an embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the invention provides a method for planning a power communication channel by reinforcement learning, which includes the following steps:
step S101: and acquiring parameters of a starting station, an ending station and a communication channel.
As an embodiment, the communication channel parameters include a maximum channel number, a port type, a bandwidth, a network type, a maximum circuit number of a transmission segment, a maximum network element number, a maximum kilometer length, a routing mode, whether SNCP is configured, a reserved fiber core number, and a maximum attenuation. The routing mode is direct or indirect.
Step S102: and inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning, and outputting an optimal communication channel.
As an embodiment, the communication channel prediction model based on deep reinforcement learning is built according to the following model:
Q(s,c)＝Q(s,c)+c[Re+Re·max c ·Q(s’,c’)-Q(s,c)]
q represents a reinforcement learning model, s represents a current state, c represents input data corresponding to the current state, s 'represents a next state, c' represents input data corresponding to the next state, and Re represents a reward value.
As an embodiment, the deep reinforcement learning-based communication channel prediction model is trained according to the following steps:
step S1021: initializing a Q value table, a learning rate, a discount factor and an exploration rate;
step S1022: randomly selecting a group of training data from a training set as an initial state s to be input into the deep reinforcement learning-based communication channel prediction model; and the training set is constructed according to historical data.
Step S1023: judging whether the current step number is larger than the total step number; if not, acquiring a random number num between 0 and 1; if yes, go to step S1027;
step S1024: judging whether the random number num is greater than the exploration speed alpha or not; if so, selecting an action a corresponding to the maximum Q value of the current state; if not, randomly selecting an action a;
step S1025: executing the action a to obtain the next state s' and the reward of the model, and updating the Q value table;
step S1026: setting s 'as a current state, judging whether s' is in a final state, and if so, entering the next step; if not, go to step S1023;
step S1027: updating the exploration rate;
step S1028: judging whether the current learning times are larger than the total learning times or not; if yes, ending the training; if not, go to step S1022.
Compared with the manual mode in the prior art, the invention has the following advantages:
1. the method adopts specific quantitative indexes to measure the advantages and the disadvantages of the corresponding channels, and is more accurate compared with the traditional manual mode; the invention realizes the automatic planning of the electric power communication channel for bearing the stability control service.
2. The invention can increase or delete with the change of the service and the network through the specific quantitative index, and the model can be applicable even if the complexity of the network is increased and the described fields are increased in the future. I.e. by adding a field (or variable) describing the state to the model state S.
On the basis of the above embodiment of the invention, the present invention correspondingly provides an embodiment of the apparatus, as shown in fig. 2;
the invention provides a power communication channel planning device based on reinforcement learning, which comprises a planning data acquisition module and a communication channel planning module;
the planning data acquisition module is used for acquiring parameters of a starting station, an ending station and a communication channel;
and the communication channel planning module is used for inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning and outputting an optimal communication channel.
For convenience and brevity of description, the embodiments of the apparatus according to the present invention include all the embodiments of the power communication channel planning method for reinforcement learning described above, and are not described herein again.
On the basis of the embodiment of the invention, the invention correspondingly provides an embodiment of a readable storage medium; another embodiment of the present invention provides a readable storage medium, which includes a stored computer program, and when the computer program is executed, the computer program controls a device on which the readable storage medium is located to execute the reinforcement learning power communication channel planning method according to any one of the method embodiments of the present invention.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used for storing the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The module/unit integrated with the terminal device may be stored in a computer-readable storage medium (i.e., the above-mentioned readable storage medium) if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the above embodiments may be implemented by hardware related to instructions of a computer program, and the computer program may be stored in a computer readable storage medium, and when executed, may include the processes of the above embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (7)
1. A power communication channel planning method based on reinforcement learning is characterized by comprising the following steps:
acquiring parameters of a starting station, an ending station and a communication channel;
and inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning, and outputting an optimal communication channel.
2. The method of claim 1, wherein the communication channel parameters comprise a maximum number of channels, a port type, a bandwidth, a network type, a maximum number of circuits for transmission segments, a maximum number of network elements, a maximum kilometer length, a routing mode, whether SNCP is configured, a reserved core number, and a maximum attenuation.
3. The reinforcement learning-based power communication channel planning method according to claim 2, wherein the deep reinforcement learning-based communication channel prediction model is established according to the following model:
Q(s,c)＝Q(s,c)+c[Re+Re·max c ·Q(s’,c’)-Q(s,c)]
q represents a reinforcement learning model, s represents a current state, c represents input data corresponding to the current state, s 'represents a next state, c' represents input data corresponding to the next state, and Re represents a reward value.
4. The reinforcement learning-based power communication channel planning method according to any one of claims 1 to 3, wherein the deep reinforcement learning-based communication channel prediction model is trained according to the following steps:
step 1: initializing a Q value table, a learning rate, a discount factor and an exploration rate;
step 2: randomly selecting a group of training data from a training set as an initial state s to be input into the deep reinforcement learning-based communication channel prediction model;
and step 3: judging whether the current step number is larger than the total step number; if not, acquiring a random number num between 0 and 1; if yes, go to step 7;
and 4, step 4: judging whether the random number num is greater than the exploration speed alpha or not; if so, selecting the action corresponding to the maximum Q value of the current state; if not, randomly selecting an action;
and 5: executing the action selected in the step 4 to obtain the next state s' and reward of the model, and updating the Q value table;
and 6: setting s' to a current state; judging whether s' is in a final state, if so, entering the next step; if not, turning to the step 3;
and 7: updating the exploration rate alpha;
and 8: judging whether the current learning times are larger than the total learning times or not; if yes, ending the training; if not, go to step 2.
5. A power communication channel planning device based on reinforcement learning is characterized by comprising a planning data acquisition module and a communication channel planning module;
the planning data acquisition module is used for acquiring parameters of a starting station, an ending station and a communication channel;
and the communication channel planning module is used for inputting the parameters of the starting station, the ending station and the communication channel into a communication channel prediction model based on deep reinforcement learning and outputting an optimal communication channel.
6. The reinforcement learning-based power communication channel planning device according to claim 5, wherein the communication channel parameters in the planning data obtaining module include a maximum channel number, a port type, a bandwidth, a network type, a maximum circuit number of transmission segments, a maximum network element number, a maximum kilometer length, a routing mode, whether to configure SNCP, a reserved fiber core number, and a maximum attenuation.
7. A readable storage medium comprising a stored computer program which, when executed, controls an apparatus on which the readable storage medium is located to perform the reinforcement learning-based power communication channel planning method according to any one of claims 1 to 4.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230716A (en) * | 2016-07-22 | 2016-12-14 | 江苏省电力公司信息通信分公司 | A kind of ant group algorithm and power telecom network communication service intelligent allocation method |
| CN110601973A (en) * | 2019-08-26 | 2019-12-20 | 中移（杭州）信息技术有限公司 | Route planning method, system, server and storage medium |
| CN111010294A (en) * | 2019-11-28 | 2020-04-14 | 国网甘肃省电力公司电力科学研究院 | Electric power communication network routing method based on deep reinforcement learning |
| CN111191918A (en) * | 2019-12-27 | 2020-05-22 | 国网江苏省电力有限公司信息通信分公司 | Service route planning method and device for smart power grid communication network |
| WO2021135449A1 (en) * | 2020-06-30 | 2021-07-08 | 平安科技（深圳）有限公司 | Deep reinforcement learning-based data classification method, apparatus, device, and medium |
| CN113095578A (en) * | 2021-04-16 | 2021-07-09 | 广东电网有限责任公司电力调度控制中心 | Design method, device, terminal and medium for optimal communication path of transformer substation |
| CN114025264A (en) * | 2021-11-15 | 2022-02-08 | 国网天津市电力公司信息通信公司 | Routing planning method for power communication SDH optical transmission network |
-
2022
- 2022-08-01 CN CN202210918026.9A patent/CN115086187B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230716A (en) * | 2016-07-22 | 2016-12-14 | 江苏省电力公司信息通信分公司 | A kind of ant group algorithm and power telecom network communication service intelligent allocation method |
| CN110601973A (en) * | 2019-08-26 | 2019-12-20 | 中移（杭州）信息技术有限公司 | Route planning method, system, server and storage medium |
| CN111010294A (en) * | 2019-11-28 | 2020-04-14 | 国网甘肃省电力公司电力科学研究院 | Electric power communication network routing method based on deep reinforcement learning |
| CN111191918A (en) * | 2019-12-27 | 2020-05-22 | 国网江苏省电力有限公司信息通信分公司 | Service route planning method and device for smart power grid communication network |
| WO2021135449A1 (en) * | 2020-06-30 | 2021-07-08 | 平安科技（深圳）有限公司 | Deep reinforcement learning-based data classification method, apparatus, device, and medium |
| CN113095578A (en) * | 2021-04-16 | 2021-07-09 | 广东电网有限责任公司电力调度控制中心 | Design method, device, terminal and medium for optimal communication path of transformer substation |
| CN114025264A (en) * | 2021-11-15 | 2022-02-08 | 国网天津市电力公司信息通信公司 | Routing planning method for power communication SDH optical transmission network |
Non-Patent Citations (1)
| Title |
|---|
| 皮特潘；DQN备忘, pages 1 * |
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