CN107154870B - Flow monitoring method based on electric power automation system - Google Patents
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Abstract
The invention discloses a flow monitoring method based on a power automation system, which comprises the following steps: acquiring parameters of historical flow data of the power automation system and flow data of a service system; calculating a comprehensive association index of historical flow data of the power automation system and flow data of the service system; and determining the relation between the traffic data of each service system according to the comprehensive association index of the historical traffic data of the power automation system and the traffic data of the service system. The method can determine the data interaction relationship among the servers, so that a manager can find the implicit relationship among different service systems conveniently, and the manager can be assisted to make correct judgment and decision.
Description
Technical Field
The invention relates to the field of electric power automation, in particular to a flow monitoring method based on an electric power automation system.
Background
In recent years, the application of a dispatching data network is rapidly developed, and the dispatching data network comprises automatic systems such as protection fault management, fault recording remote transmission, an electric quantity acquisition system and a dispatching real-time system. The data network is an important technical platform supporting a dispatching automation system, and generally requires that the data network is safe and reliable, and the real-time performance is required to be at the level of seconds or several seconds.
At present, network equipment (mainly including switches, routers and the like) applied to a dispatching automation system has a large variety and large quantity; with the increasing requirements of governments on the safety of dispatching automation systems, network safety products such as firewalls, longitudinal encryption devices, transverse isolation devices and the like are gradually increased, but software configured with the devices can only realize the configuration, maintenance and simple monitoring of devices of the same type, and cannot be associated with a service application system; for operation and maintenance personnel of the automatic system, the real-time working conditions of the operation of the service system cannot be comprehensively and systematically collected, and when the service system is abnormal, comprehensive and effective means and bases for timely positioning and troubleshooting the abnormality are lacked.
A flow monitoring system based on a power automation system is provided in CN 201110051431.7, but a specific and quantitative data interaction relationship analysis method between servers is not provided, so there is still further room for improvement.
Disclosure of Invention
The invention aims to provide a flow monitoring method based on an electric power automation system, which aims to solve the problem that the network equipment and a business application system cannot be associated in the background technology, namely, a quantitative analysis method of data interaction relation among servers is provided.
The invention provides a flow monitoring method based on a power automation system, which comprises the following steps:
step 100, acquiring parameters of historical flow data and service system flow data of the power automation system, wherein the parameters comprise total flow, maximum instantaneous flow, minimum instantaneous flow, data flow direction and duration;
step 200, analyzing the correlation degree between the total flow of the business system flow data and the total flow of the historical flow data of the power automation system;
step 300, analyzing the association degree between the maximum instantaneous flow of the traffic data of the service system and the maximum instantaneous flow of the historical traffic data of the power automation system;
step 400, analyzing the correlation degree between the instantaneous flow minimum value of the traffic system flow data and the instantaneous flow minimum value of the historical flow data of the power automation system;
step 500, analyzing the correlation degree between the data flow direction of the traffic data of the service system and the data flow direction of the historical traffic data of the power automation system;
step 600, analyzing the correlation degree between the duration of the traffic data of the service system and the duration of the historical traffic data of the power automation system;
step 700, calculating a comprehensive association index of historical flow data of the power automation system and flow data of the service system;
step 800, determining a relationship between the traffic data of each service system according to the comprehensive association index of the historical traffic data of the power automation system and the traffic data of the service system.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a quantitative analysis method for the data interaction relationship between the servers, which can quantitatively calculate the correlation index of the historical flow data of the power automation system and the flow data of the service system, can accurately determine the relationship between the historical flow data of the power automation system and the flow data of the service system according to the correlation index, and can determine the data interaction relationship between the servers by taking the correlation index as a reference, thereby facilitating a manager to find the implicit relationship between different service systems and assisting the manager to make correct judgment and decision.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
The present invention provides an embodiment:
a flow monitoring method based on a power automation system comprises the following steps:
step 100, acquiring parameters of historical flow data and service system flow data of the power automation system, wherein the parameters comprise total flow, maximum instantaneous flow, minimum instantaneous flow, data flow direction and duration;
step 200, analyzing the correlation degree between the total flow of the business system flow data and the total flow of the historical flow data of the power automation system;
recording total flow set of historical flow data of the power automation system as { DTOT}，{DTOT}＝{DTOT(t1),DTOT(t2),...,DTOT(tu) Total flow set of historical flow data, { t }1,t2L tuIs a sequence of time points at fixed time intervals, denoted Δ t, the number of time points u, tiAt the ith time point, i ∈ [1, u]，{DTOT(ti) The total flow of the historical flow data at the ith time point is set as { B }TOT}，{BTOT}＝{BTOT(t1),BTOT(t2),...,BTOT(tu) Calculating the correlation degree of the total flow of the historical flow data and the total flow of the service system flow data at the ith time point, and recording the correlation degree as
Wherein Δ eTOTiIs the difference value of the total flow of the service system flow data and the historical flow data at the ith time point, delta minTOTIs the minimum value of the difference value between the total flow of the traffic system flow data and the total flow of the historical flow data, delta maxTOTη is the maximum value of the difference between the total flow of the traffic system traffic data and the total flow of the historical traffic dataTOTTo total flow resolution factor, ηTOT∈[0,1]Preferably 0.67;
calculating the correlation degree l of the total flow of the traffic data of the service system and the total flow of the historical traffic dataTOT，
Particularly, in CN 201110051431.7, data aggregation of 10 time granularities, such as 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, 1 day, 1 week, 1 month, 1 quarter, 1 year, etc., is supported for differences of data time granularity requirements of each application of the system, and based on 1 minute aggregation data in the database, 5 minutes of data are aggregated from 1 minute of data, 10 minutes of data are aggregated from 5 minutes of data, and so on, iterative aggregation is performed on different time granularity flow data, and meanwhile, performance of real-time flow collection is prevented from being affected. The concept of time interval and time point sequence of the present invention is the same as the data time granularity in CN 201110051431.7, the time interval is preferably 1 minute, and the time point sequence is preferably 1-120 minutes.
Step 300, analyzing the association degree between the maximum instantaneous flow of the traffic data of the service system and the maximum instantaneous flow of the historical traffic data of the power automation system;
the maximum value set of the instantaneous flow of the historical flow data of the power automation system is recorded as { Dmax}，{Dmax}＝{Dmax(t1),Dmax(t2),...,Dmax(tu) Set of instantaneous flow maximums for historical flow data, { Dmax(ti) The maximum value of the instantaneous flow of the historical flow data at the ith time point and the instantaneous flow of the traffic data of the service systemThe maximum value of the flow is set as { Bmax}，{Bmax(ti) Is the instantaneous flow maximum of the historical flow data at the ith time point, { Bmax}＝{Bmax(t1),Bmax(t2),...,Bmax(tu) Calculating the association degree of the maximum value of the instantaneous flow of the historical flow data and the maximum value of the instantaneous flow of the service system flow data at the ith time point, and recording the association degree as
Calculating the association degree l of the instantaneous flow maximum value of the historical flow data and the instantaneous flow maximum value of the traffic system flow datamax，
Generally, the instantaneous traffic maximum of the historical traffic data should be greater than the instantaneous traffic maximum of the traffic data of the traffic system, somax∈[0,1]；
Step 400, analyzing the correlation degree between the instantaneous flow minimum value of the traffic system flow data and the instantaneous flow minimum value of the historical flow data of the power automation system;
the minimum value set of the instantaneous flow of the historical flow data of the power automation system is recorded as { Dmin}，
{Dmin}＝{Dmin(t1),Dmin(t2),...,Dmin(tu) Is a set of instantaneous flow minimum values for historical flow data, { Dmin(ti) The flow data of the service system is set as { B } which is the minimum value of the instantaneous flow of the historical flow data at the ith time pointmin}，{Bmin(ti) Is the instantaneous flow minimum value of the historical flow data at the ith time point, { Bmin}＝{Bmin(t1),Bmin(t2),...,Bmin(tu) Calculating the association degree of the minimum value of the instantaneous flow of the historical flow data and the minimum value of the instantaneous flow of the traffic system flow data at the ith time point, and recording the association degree as
Calculating the association degree l of the instantaneous flow minimum value of the historical flow data and the instantaneous flow minimum value of the traffic system flow datamin，
Generally, the instantaneous flow minimum of the historical flow data should be less than the instantaneous flow maximum of the traffic data of the traffic system, somin∈[0,1]；
Step 500, analyzing the correlation degree between the data flow direction of the traffic data of the service system and the data flow direction of the historical traffic data of the power automation system;
recording the data flow direction of the historical flow data of the power automation system as DdirThe data flow direction set of the traffic data of the service system is Bdir，
Calculating the relevance l of the data flow direction of the historical flow data and the data flow direction of the traffic system flow datadir，
Since the data flow only has two directions of input and output, and the data flow is independent of the time metric, ldirEither 1 or 0;
step 600, analyzing the correlation degree between the duration of the traffic data of the service system and the duration of the historical traffic data of the power automation system;
recording the duration set of historical flow data of the power automation system as { DT}，{DT(ti) Duration of historical flow data at the ith time point, { D }T}＝{DT(t1),DT(t2),...,DT(tu) The duration time set of the historical flow data is shown as { B }, and the duration time set of the service system flow data is shown as { B }T}，{BT(ti) The duration of the traffic data of the service system at the ith time point, { B }T}＝{BT(t1),BT(t2),...,BT(tu) Calculating the association degree between the duration of the historical flow data and the duration of the service system flow data at the ith time point, and recording the association degree as
Wherein Δ eTiIs the difference value of the duration of the traffic data of the service system at the ith time point and the duration of the historical traffic data, delta minTIs the minimum value of the difference between the duration of the traffic data of the service system and the duration of the historical traffic data, Δ maxTη is the maximum value of the difference between the duration of the traffic data of the service system and the duration of the historical traffic dataTTo a time-of-duration resolution factor, ηT∈[0,1]Preferably 0.38;
calculating the relevance l of the duration of the traffic data of the service system and the duration of the historical traffic dataT，
Step 700, calculating a comprehensive association index of historical flow data of the power automation system and flow data of the service system;
a method for calculating the comprehensive correlation index l comprises the following steps:
step 800, determining a relationship between the traffic data of each service system according to the comprehensive association index of the historical traffic data of the power automation system and the traffic data of the service system.
Calculating similarity sigma between jth and kth service system flow datajkThe method comprises the following steps:
wherein lTOT(j) And lTOT(k) Respectively the correlation degrees of the total flow of the jth and kth service systems and the total flow of the historical flow data; lmax(j) And lmax(k) Respectively representing the association degrees of the instantaneous flow maximum value of the jth and kth service system flow data and the instantaneous flow maximum value of the historical flow data; lmin(j) And lmin(k) Respectively relating the association degree of the instantaneous flow minimum value of the jth and kth service system flow data and the instantaneous flow minimum value of the historical flow data; lT(j) And lT(k) Respectively, the correlation degree between the duration of the jth and kth service system traffic data and the duration of the historical traffic data.
The advantage of using this method to quantitatively analyze the data interaction relationship between the servers is that: the method can quantitatively calculate the correlation index of the historical flow data of the power automation system and the flow data of the service system, can accurately determine the relationship between the historical flow data of the power automation system and the flow data of the service system according to the correlation index, and can determine the data interaction relationship between the servers by taking the relationship as a reference, so that a manager can find the implicit relationship between different service systems conveniently and assist the manager in making correct judgment and decision.
The table below shows the accuracy (percentage) of the correlation between the service systems when the time intervals and the time series lengths are different, and it can be seen from the data in the table that the smaller the time granularity is, the higher the accuracy is, and the longer the time series is, the higher the accuracy is. Therefore, the flow monitoring method based on the power automation system achieves unexpected effects, and has remarkable progress compared with the prior art.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (7)
1. A flow monitoring method based on a power automation system comprises the following steps:
step 100, acquiring parameters of historical flow data and service system flow data of the power automation system, wherein the parameters comprise total flow, maximum instantaneous flow, minimum instantaneous flow, data flow direction and duration;
step 200, analyzing the correlation degree between the total flow of the business system flow data and the total flow of the historical flow data of the power automation system;
step 300, analyzing the association degree between the maximum instantaneous flow of the traffic data of the service system and the maximum instantaneous flow of the historical traffic data of the power automation system;
step 400, analyzing the correlation degree between the instantaneous flow minimum value of the traffic system flow data and the instantaneous flow minimum value of the historical flow data of the power automation system;
step 500, analyzing data flow direction of traffic data of the service system and history of the power automation systemDegree of correlation between data flow directions of traffic data
Wherein DdirData flow direction for historical flow data of power automation systems, BdirThe data flow direction of the traffic data of the service system;
step 600, analyzing the correlation degree between the duration of the traffic data of the service system and the duration of the historical traffic data of the power automation system;
step 700, calculating a comprehensive association index of historical flow data of the power automation system and flow data of the service system;
step 800, determining the relation between the traffic data of each service system according to the comprehensive association index of the historical traffic data of the power automation system and the traffic data of the service systems, and determining the similarity between the traffic data of the jth service system and the traffic data of the kth service system
Wherein lTOT(j) And lTOT(k) Respectively the correlation degrees of the total flow of the jth and kth service systems and the total flow of the historical flow data; lmax(j) And lmax(k) Respectively representing the association degrees of the instantaneous flow maximum value of the jth and kth service system flow data and the instantaneous flow maximum value of the historical flow data; lmin(j) And lmin(k) Respectively relating the association degree of the instantaneous flow minimum value of the jth and kth service system flow data and the instantaneous flow minimum value of the historical flow data; lT(j) And lT(k) Respectively, the correlation degree between the duration of the jth and kth service system traffic data and the duration of the historical traffic data.
2. The flow monitoring method based on the electric power automation system as claimed in claim 1, characterized in that:
step 200 further comprises:
recording power automationTotal flow set of System historical flow data is { DTOT}，{DTOT}＝{DTOT(t1),DTOT(t2),...,DTOT(tu) Total flow set of historical flow data, { t }1,t2…tuIs a sequence of time points at fixed time intervals, denoted Δ t, the number of time points u, tiAt the ith time point, i ∈ [1, u]，{DTOT(ti) The total flow of the historical flow data at the ith time point is set as { B }TOT}，{BTOT}＝{BTOT(t1),BTOT(t2),...,BTOT(tu) Calculating the correlation degree of the total flow of the historical flow data and the total flow of the service system flow data at the ith time point, and recording the correlation degree as
Wherein Δ eTOTiIs the difference value of the total flow of the service system flow data and the historical flow data at the ith time point, delta minTOTIs the minimum value of the difference value between the total flow of the traffic system flow data and the total flow of the historical flow data, delta maxTOTη is the maximum value of the difference between the total flow of the traffic system traffic data and the total flow of the historical traffic dataTOTTo total flow resolution factor, ηTOT∈[0,1]Preferably 0.67;
calculating the correlation degree l of the total flow of the traffic data of the service system and the total flow of the historical traffic dataTOT，
3. The flow monitoring method based on the electric power automation system as claimed in claim 2, characterized in that:
step 300 further comprises:
the maximum value set of the instantaneous flow of the historical flow data of the power automation system is recorded as { Dmax}，{Dmax}＝{Dmax(t1),Dmax(t2),...,Dmax(tu) Set of instantaneous flow maximums for historical flow data, { Dmax(ti) The maximum value of the instantaneous flow of the historical flow data at the ith time point is set as { B }max}，{Bmax(ti) Is the instantaneous flow maximum of the historical flow data at the ith time point, { Bmax}＝{Bmax(t1),Bmax(t2),…,Bmax(tu) Calculating the association degree of the maximum value of the instantaneous flow of the historical flow data and the maximum value of the instantaneous flow of the service system flow data at the ith time point, and recording the association degree as
Calculating the association degree l of the instantaneous flow maximum value of the historical flow data and the instantaneous flow maximum value of the traffic system flow datamax，
4. The flow monitoring method based on the electric power automation system as claimed in claim 3, characterized in that:
step 400 further comprises:
the minimum value set of the instantaneous flow of the historical flow data of the power automation system is recorded as { Dmin}，{Dmin}＝{Dmin(t1),Dmin(t2),...,Dmin(tu) Is a set of instantaneous flow minimum values for historical flow data, { Dmin(ti) The flow data of the service system is set as { B } which is the minimum value of the instantaneous flow of the historical flow data at the ith time pointmin}，{Bmin(ti) Is the instantaneous flow minimum value of the historical flow data at the ith time point, { Bmin}＝{Bmin(t1),Bmin(t2),...,Bmin(tu) Calculating the association degree of the minimum value of the instantaneous flow of the historical flow data and the minimum value of the instantaneous flow of the traffic system flow data at the ith time point, and recording the association degree as
Calculating the association degree l of the instantaneous flow minimum value of the historical flow data and the instantaneous flow minimum value of the traffic system flow datamin，
5. The flow monitoring method based on the electric power automation system as claimed in claim 4, characterized in that:
step 600 further comprises:
recording the duration set of historical flow data of the power automation system as { DT}，{DT(ti) Duration of historical flow data at the ith time point, { D }T}＝{DT(t1),DT(t2),...,DT(tu) The duration time set of the historical flow data is shown as { B }, and the duration time set of the service system flow data is shown as { B }T}，{BT(ti) That is traffic data of the service system is on the firstDuration of i time points, { BT}＝{BT(t1),BT(t2),...,BT(tu) Calculating the association degree between the duration of the historical flow data and the duration of the service system flow data at the ith time point, and recording the association degree as
Wherein Δ eTiIs the difference value of the duration of the traffic data of the service system at the ith time point and the duration of the historical traffic data, delta minTIs the minimum value of the difference between the duration of the traffic data of the service system and the duration of the historical traffic data, Δ maxTη is the maximum value of the difference between the duration of the traffic data of the service system and the duration of the historical traffic dataTTo a time-of-duration resolution factor, ηT∈[0,1]；
Calculating the relevance l of the duration of the traffic data of the service system and the duration of the historical traffic dataT，
6. The flow monitoring method based on the electric power automation system as claimed in claim 5, characterized in that:
step 700 further comprises:
the method for calculating the comprehensive correlation index l comprises the following steps:
7. the flow monitoring method based on the electric power automation system as claimed in claim 6, characterized in that:
the time interval is 1 minute, and the time point sequence is 1-120 minutes.
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| CN102158401A (en) * | 2011-03-03 | 2011-08-17 | 江苏方天电力技术有限公司 | Flow monitoring model based on electric automation system |
| CN102355425A (en) * | 2011-10-26 | 2012-02-15 | 深信服网络科技（深圳）有限公司 | Network traffic control method and device |
| CN105117803A (en) * | 2015-09-10 | 2015-12-02 | 国家电网公司 | Base line prediction and optimization method based on non-demand response factor |
| CN106372799A (en) * | 2016-08-31 | 2017-02-01 | 全球能源互联网研究院 | Power grid security risk prediction method |
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