CN114531403A - Power service network distinguishing method and system - Google Patents

Power service network distinguishing method and system Download PDF

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CN114531403A
CN114531403A CN202111349493.6A CN202111349493A CN114531403A CN 114531403 A CN114531403 A CN 114531403A CN 202111349493 A CN202111349493 A CN 202111349493A CN 114531403 A CN114531403 A CN 114531403A
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network
power
preset
data packet
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毛琳明
陈刚
汪泽州
储建新
张泰山
陈伦
赵燕波
赵国爱
蒋海忠
鲍建飞
干玉成
张汉斌
张洁
潘炫霖
刘海林
高原
朱凯熙
屠孝杰
李名文
潘克勤
曹志勇
谢益峰
陆建琴
雷健新
孙豪豪
朱杰
谢军明
查云杰
董子奕
李伟
孙帅
张依辰
俞晓伟
周弘毅
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State Grid Zhejiang Electric Power Co Ltd Haiyan County Power Supply Co
Haiyan Nanyuan Electric Power Engineering Co ltd
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State Grid Zhejiang Electric Power Co Ltd Haiyan County Power Supply Co
Haiyan Nanyuan Electric Power Engineering Co ltd
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Priority to CN202111349493.6A priority Critical patent/CN114531403A/en
Publication of CN114531403A publication Critical patent/CN114531403A/en
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    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2441—Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
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    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G06F18/2414—Smoothing the distance, e.g. radial basis function networks [RBFN]
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    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2408—Traffic characterised by specific attributes, e.g. priority or QoS for supporting different services, e.g. a differentiated services [DiffServ] type of service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2425—Traffic characterised by specific attributes, e.g. priority or QoS for supporting services specification, e.g. SLA
    • H04L47/2433—Allocation of priorities to traffic types
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
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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
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    • 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 invention discloses a method and a system for distinguishing power service networks, wherein the method comprises the following steps: acquiring a service data packet in a preset network; identifying a service class in the service data packet; and distinguishing the power services according to different service types and carrying out shunt transmission. The system comprises: a memory containing a power service network differentiation method program and a processor running the memory program. The method comprises the steps of acquiring target data in the power transmission network through a set service type identification method, and distinguishing service types of the target data; meanwhile, the power transmission network is segmented according to different service categories to match the requirements of different categories of service data, shunt transmission is carried out according to the current requirements of the transmission data, the safety and the timeliness of the data are guaranteed, the passing efficiency of the network is improved, and the occurrence of network congestion is reduced.

Description

Power service network distinguishing method and system
Technical Field
The invention relates to the technical field of power service differentiation, in particular to a method and a system for differentiating a power service network.
Background
The current power grid development faces deep change and transformation requirements, the power generation types on the power supply side are rich, new energy is developed rapidly, and the regulation capacity is reduced continuously; the safety red line on the side of the power grid is continuously tightened, and the redundancy of equipment and operation is high; the load side resource is in a deep sleep state, and the interaction mechanism capability is not established; the energy storage side facilities are configured less, difficult to utilize and have no policy.
With the gradual progress of national household appliance policy changing, the operation efficiency and the benefit of a distribution network become practical key problems to be faced by a power grid company, and a wireless power private network needs to support services with low time delay and high reliability, such as image or video transmission, emergency communication, millisecond-level accurate load shedding control service and millisecond-level three-remote service of a power distribution network, besides services with low speed and wide coverage attributes, such as traditional power consumption information acquisition, power distribution network electrical equipment monitoring and the like. How to quickly and accurately identify the service class in the wireless private network of the power line so as to adopt different transmission modes for different services is becoming a research direction.
Chinese patent CN103987071A provides a "power TD-LTE wireless service system," which adds Tag identifiers in information sent by a power UE terminal, and an LTE base station distinguishes user types by identifying the Tag identifiers of the UE terminal, thereby implementing transmission of different services to different subnets, implementing physical isolation of services, and improving security of a wireless private network, but this method lacks information acquisition of power services, and service classification, etc.
Disclosure of Invention
Aiming at the problems of low efficiency and low benefit of the power grid distribution network, the invention provides a power service network distinguishing method and a system.
The technical problem of the invention is mainly solved by the following technical scheme:
a power service network distinguishing method comprises the following steps:
s1, acquiring a service data packet in a preset network;
s2 identifying the traffic class in the traffic data packet;
s3, distinguishing the power service according to the different service types and branching transmission.
In order to improve the network transmission benefit and maximize the transmission efficiency of the base station, the types of the transmission data in the network need to be distinguished, the transmission data are classified according to different types, and the transmission rate can be greatly improved by orderly transmitting the data.
Preferably, in step S1, the acquiring the service data packet in the preset network includes the specific steps of:
s11, detecting the real-time data traffic in the preset network;
s12, recognizing a preset label value in the real-time data traffic;
s13 capturing the data traffic worth the preset label as the service data packet.
And acquiring a required service data packet from a plurality of data, wherein the required service data packet needs to be obtained by identifying the preset tag value in the real-time data flow.
Preferably, the step S2 of identifying the service label in the service data packet includes the specific steps of:
s21, acquiring service label values of different services according to the service data packet;
s22, inputting the service label value into the trained service class neural network model to obtain a target simulation result;
s23, the service type in the service data packet is judged according to the target simulation result.
Different service classes correspond to different service label values, and in order to be able to quickly distinguish the service label values from the service classes, the service classes can be distinguished by means of a trained neural network model of the service classes.
Preferably, in step S23, the determining the service type in the service data packet according to the target simulation result specifically includes:
judging the size of the target simulation result and a preset threshold range, wherein,
if the target simulation result is within a first preset threshold range, judging that the service type is a first service field;
if the target simulation result is within a second preset threshold range, judging that the service type is a second service field;
and if the target simulation result is within a third preset threshold range, judging that the service type is a third service field.
The first service field has the highest security level and the strongest privacy, and comprises electric power control information and link load information; the service category of the second service field has specificity with timeliness priority, and comprises a video monitoring information stream and an emergency communication information stream; the service category of the third service field is a common category, and includes power consumption acquisition information and mobile application information.
Preferably, step S23 further includes performing network segmentation on the preset network, specifically:
cutting out a first network fragment according to the precise control requirement so as to match the power service of the first service field;
cutting a second network fragment according to the time delay requirement to match the power service of the second service field;
and cutting a third network fragment according to reliability requirements to match the power service of the third service field.
The number of the preset network segmentations is always higher than the number of the service fields so as to meet the requirements of some specific service transmission.
Preferably, the service class neural network model training method comprises the following steps:
s221, acquiring service label information and service category information of historical time;
s222, preprocessing the service label information and the service category information of the historical time to obtain a training sample set;
s223, inputting the training sample set into the initialized business class neural network model for training;
s224, obtaining the accuracy of the output result, and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the business category neural network model.
When the business category neural network model is trained, firstly acquiring the business label information and the business category information of historical time; preprocessing the service label information and the service category information of the historical time to obtain a training sample set, wherein the training sample set comprises the service categories of the identification and elimination errors of the service labels; inputting the training sample set into an initialized neural network model for training; acquiring the accuracy of an output result; and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the final business class neural network model.
Preferably, the accuracy threshold Value is ValuecorrectAnd the Number of service typestypeThe relationship between the following formulas:
Valuecorrect=Valuestd+θ*Numbertype；
wherein, Valuestdθ is a parameter factor of the dynamic empirical value for standard accuracy, which is typically 78%.
A system for implementing a power services network differentiation method includes a memory and a processor. A computer readable storage medium is arranged in the memory, a power service network distinguishing method program is stored in the computer readable storage medium, the processor is connected with the memory, the processor can run all programs in the memory to complete all steps in the power service network distinguishing method, and the storage medium in the memory comprises a storage medium which comprises: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The invention has the beneficial effects that:
1. the safety of the data is ensured, and the data loss is reduced;
2. the network transmission benefit is improved, and the transmission efficiency of the base station is maximized;
3. the transmission speed of data in the network is improved;
4. the traffic class in the network transmission data can be identified and different networks can be assigned for data transmission.
Drawings
FIG. 1 is a flow chart of a power service network differentiation method of the present invention;
fig. 2 is a schematic structural diagram of a system for a power service network differentiation method according to the present invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
As shown in fig. 1, a power service network differentiation method includes the following steps:
s1, acquiring a service data packet in a preset network;
the method specifically comprises the following steps:
s11, detecting the real-time data traffic in the preset network;
s12, recognizing a preset label value in the real-time data traffic;
s13 capturing the data traffic worth the preset label as the service data packet.
Since the data traffic in the preset network is various, how to obtain the required service data packet from many data is to identify the preset tag value in the real-time data traffic, for example, the preset tag value may be a string of initial codes "0000 xxxx", and when the preset tag value is identified, capture the corresponding data to obtain the service data packet. The data flow is multiplexed, so that multi-section identification can be set, and the service data packet can be captured in a multi-channel identification mode, thereby avoiding the situation of data omission.
S2 identifying the traffic class in the traffic data packet;
the method specifically comprises the following steps:
s21, acquiring service label values of different services according to the service data packet;
s22, inputting the service label value into the trained service class neural network model to obtain a target simulation result;
s23, the service type in the service data packet is judged according to the target simulation result.
Different service classes correspond to different service label values, and in order to be able to quickly distinguish the service label values from the service classes, the service classes can be distinguished by means of a trained neural network model of the service classes. The business category neural network model requires a large amount of historical data for training, and the larger the data amount is, the more accurate the result is. The business classification neural network model can be trained as input through the business label value of the historical business classification condition, certainly, when the neural network model is trained, not only need pass through the business label value is trained, still needs to combine the type of business itself to train, trains through a large amount of historical data, and the result that obtains also can be more accurate, combines business classification information every time again, can make the output result of business classification neural network more accurate.
According to an embodiment, the training method of the business class neural network model in step S22 includes:
s221, acquiring service label information and service category information of historical time;
s222, preprocessing the service label information and the service category information of the historical time to obtain a training sample set;
s223, inputting the training sample set into the initialized business class neural network model for training;
s224, obtaining the accuracy of the output result, and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the business category neural network model.
It should be noted that, when the service class neural network model is trained, the service label information and the service class information of the historical time are obtained first; preprocessing the service label information and the service category information of the historical time to obtain a training sample set, wherein the training sample set comprises the service categories of the identification and elimination errors of the service labels; inputting the training sample set into an initialized neural network model for training; acquiring the accuracy of an output result; and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the final business class neural network model. The accuracy threshold is a dynamic threshold, and different accuracy thresholds can be obtained according to different numbers of service categories, wherein the accuracy threshold Value iscorrectAnd the Number of service typestypeThe relationship between the following equations:
Valuecorrect=Valuestd+θ*Numbertype；
wherein, ValuestdFor standard accuracy, it is typically set to 78%, θ is a parameter factor, and is a dynamic empirical value.
Step S23, determining the service type in the service data packet according to the target simulation result, specifically, determining the service type in the service data packet according to the target simulation result
Judging the size of the target simulation result and a preset threshold range, wherein,
if the target simulation result is within a first preset threshold range, judging that the service type is a first service field;
if the target simulation result is within a second preset threshold range, judging that the service type is a second service field;
and if the target simulation result is within a third preset threshold range, judging that the service type is a third service field.
And when the business label value is identified to be brought into the trained business class neural network model to obtain the target simulation result, judging the size relation between the target simulation result and the threshold range to judge the business class. More specifically, if the target simulation result is within a first preset threshold range, the service class is determined to be a first service domain, for example, if the target simulation result is "15", and the first preset threshold range is [1-50], the service class is determined to be a first service domain; if the target simulation result is within a second preset threshold range, determining that the service type is a second service field, for example, if the target simulation result is '143', and the second preset threshold range is [51-200], determining that the service type is the second service field; if the target simulation result is within a third preset threshold range, determining that the service class is a third service domain, for example, if the target simulation result is "415", and the second preset threshold range is [ 201-; the first service field has the highest security level and the strongest privacy, and comprises power control information and link load information; the service category of the second service field has specificity with timeliness priority, and comprises a video monitoring information stream and an emergency communication information stream; the service category of the third service field is a common category, and includes power consumption acquisition information and mobile application information. With the continuous development of the service of the power network, more and more service classes are present in the power network for transmission, and more threshold ranges need to be set to satisfy different service classes.
Furthermore, according to the embodiment, there is a corresponding network split for different business domains required in step S23:
cutting out a first network fragment according to the precise control requirement so as to match the power service of the first service field;
cutting a second network fragment according to the time delay requirement to match the power service of the second service field;
and cutting a third network fragment according to reliability requirements to match the power service of the third service field.
More specifically, after identifying the service type in the real-time data traffic, the transmission network needs to be divided for different services to ensure the validity and the orderliness of transmission, and specifically, a first network segment is divided according to a precise control need to match the power service in the first service field, so as to meet the information security requirement and the privacy requirement in the first service field; cutting out a second network fragment according to the time delay requirement to match the power service of the second service field so as to meet the requirement of the priority level of the information timeliness of the second service field; and cutting a third network fragment according to reliability requirements to match the power service in the third service field so as to meet the requirement of information stability in the third service field. The number of the preset network segmentations is always higher than the number of the service fields so as to meet the requirements of some specific service transmission.
S3, distinguishing the power service according to the different service types and branching transmission.
The data transmitted in the power private network are various in types, including various types, in order to improve the network transmission benefit and maximize the transmission efficiency of the base station, the types of the transmission data in the network need to be distinguished, the transmission data are classified according to different types, the transmission rate can be greatly improved by orderly transmitting the data, after the data of different types are distinguished, the safety of the data can be ensured, the data loss situation is reduced, after the service data packet is obtained, the service types in the service data packet are identified for distinguishing, and then different transmission networks are selected for transmission according to the different service types.
As shown in fig. 2, the present invention further provides a system for implementing the power service network differentiation method, which is composed of a memory 21 and a processor 22, wherein the memory 21 includes a power service network differentiation method program for implementing the power service network differentiation method. The storage media in the memory 21 include storage media including: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. The processor 22 is connected to the memory 21, and the processor 22 can execute the power service network distinguishing method program when the system is running, so as to complete all the steps in the power service network distinguishing method.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although the terms service area, preset tag value, preset threshold, etc. are used more often herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.

Claims (8)

1. A power service network distinguishing method is characterized by comprising the following steps:
s1, acquiring a service data packet in a preset network;
s2 identifying the traffic class in the traffic data packet;
s3, distinguishing the power service according to the different service types and branching transmission.
2. The method for distinguishing between power service networks according to claim 1, wherein the step S1 is to obtain a service data packet in a preset network, and the specific steps are as follows:
s11, detecting the real-time data traffic in the preset network;
s12, recognizing a preset label value in the real-time data traffic;
s13 capturing the data traffic worth the preset label as the service data packet.
3. The method for distinguishing between power service networks according to claim 1, wherein the step S2 is to identify the service label in the service data packet, and includes the specific steps of:
s21, acquiring service label values of different services according to the service data packet;
s22, inputting the service label value into the trained service class neural network model to obtain a target simulation result;
s23, the service type in the service data packet is judged according to the target simulation result.
4. The method according to claim 3, wherein the step S23 is to determine the service type in the service data packet according to the target simulation result, specifically, the service type is determined according to the target simulation result
Judging the size of the target simulation result and a preset threshold range, wherein,
if the target simulation result is within a first preset threshold range, judging that the service type is a first service field;
if the target simulation result is within a second preset threshold range, judging that the service type is a second service field;
and if the target simulation result is within a third preset threshold range, judging that the service type is a third service field.
5. The method according to claim 3 or 4, wherein the step S23 further includes performing network segmentation on the preset network, specifically:
cutting out a first network fragment according to the precise control requirement so as to match the power service of the first service field;
cutting a second network fragment according to the time delay requirement to match the power service of the second service field;
and cutting a third network fragment according to reliability requirements to match the power service of the third service field.
6. The method for differentiating the power service network according to claim 3, wherein the training method of the service class neural network model in the step S22 is as follows:
s221, acquiring service label information and service category information of historical time;
s222, preprocessing the service label information and the service category information of the historical time to obtain a training sample set;
s223, inputting the training sample set into the initialized business class neural network model for training;
s224, obtaining the accuracy of the output result, and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the business category neural network model.
7. The method according to claim 6, wherein the accuracy threshold Value is ValuecorrectAnd the Number of service typestypeThe relationship between the following formulas:
Valuecorrect=Valuestd+θ*Numbertype；
wherein, ValuestdFor standard accuracy, θ is a parameter factor of the dynamic empirical value.
8. A system for power service network differentiation, comprising a memory and a processor, wherein the memory is provided with a computer-readable storage medium, the computer-readable storage medium is stored with a power service network differentiation method program, and the processor is connected with the memory.
CN202111349493.6A 2021-11-15 2021-11-15 Power service network distinguishing method and system Pending CN114531403A (en)

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