CN104361088A - Congestion data processing method based on real-time weight analysis in SCADA (supervisory control and data acquisition) system - Google Patents

Abstract

The invention discloses a congestion data processing method based on real-time weight analysis in an SCADA (supervisory control and data acquisition) system, and relates to a congestion control technology in a mass data acquisition and transmission process. The method is characterized by comprising the following steps that (1) data is collected and preprocessed; (2) the collected data is subjected to real-time weight analysis, and the weight coefficient occupied by each index of the data is calculated according to the data set features; (3) the performance integral evaluation value of the real-time data is calculated; (4) the collected data is subjected to intelligent processing according to the data performance integral evaluation value, and the uploading priority of the data is determined; (5) a data packet with the higher priority is selected to be uploaded to an SCADA interface system. The congestion data processing method has the advantages that by aiming at a great amount of congestion data occurring under the emergency conditions in the SCADA system, the real-time weight analysis is carried out, the processing can be carried out in a shorter time, emergent and important data is selected to be preferentially uploaded, and the critical judging basis is provided for decision makers.

Description

The congestion data disposal route that SCADA system is analyzed based on real time weight

Technical field

The present invention relates to snowslide data congestion processing technology field, be specifically related to the congestion data disposal route that a kind of SCADA system is analyzed based on real time weight.

Background technology

SCADA (Supervisory Control And Data Acquisition) system, i.e. data acquisition and supervisor control.SCADA system is the production run control and scheduling automated system based on computing machine, can carry out monitoring and controlling to the operational outfit at scene, to realize the functions such as data acquisition, equipment control, measurement, parameter adjustment and accident alarming.Its application is very wide, can be applied to data acquisition and the numerous areas such as Monitor and Control and process control in the fields such as electric power, metallurgy, oil, chemical industry, combustion gas, railway.Application SCADA technology, can ensure the information completely of system, and the correct running status grasping large scale system equipment, helps rapid diagnosis system fault, enhance productivity.

The data acquisition of SCADA system often relates to that device category is many, data volume large, and transmission mode is complicated, relates at most tens0000 to millions of data.Various different data are according to change to be sent or master station calls the mode of getting together and transmits.When the visual plant generation emergency situations of layer subsystem under system, as power subsystem have a power failure time, various real time data will as tidewater on deliver to SCADA system, form data snowslide, and data congestion, cause a large amount of warning appears in SCADA man-machine interface in the short time, thus cause the situation that operation monitoring personnel are in a rush.

Summary of the invention

For the deficiencies in the prior art, in order to solve the problem of the snowslide data congestion produced under emergency in SCADA system, the invention provides a kind of real-time analysis, being easy to realize, the congestion data disposal route of high-speed decision.

In order to solve the problem, of the present invention taked technical scheme is:

A kind of SCADA system comprises the following steps based on the congestion data disposal route of real time weight analysis:

Step one, data acquisition and pre-service;

Step 2, real time weight analysis is carried out to image data: according to the weight coefficient shared by the feature calculation data transmission performance index of data acquisition; The feature of data acquisition refers to collected data and data transmission performance evaluation index value, and data transmission performance index comprises right of priority, real-time, time delay, expected residual transmission time, out-of-limit grade, ANOMALOUS VARIATIONS grade;

Step 3, use weigthed sums approach calculate the data performance comprehensive evaluation value (linear weighted function mean value) of packet in data stream buffer district;

Step 4, carry out intelligent processing method according to data performance comprehensive evaluation value to image data, that determines data uploads priority; Specifically comprise: according to the performance synthesis evaluation of estimate of the collected data that step 3 calculates, the packet of data buffer is sorted, determine packet send priority, select the high packet priority of priority delivers to SCADA interface system.

Step 5, the packet that selection priority is high are uploaded to SCADA interface system.

In step one, data prediction is carry out pre-service according to the quantity of data newly-increased in the unit interval, specifically comprise: if newly-increased data volume is 0.2 ~ 0.5 times (data volume in the unit interval is 1.2 ~ 1.5 times of normal data amount) of normal data amount in the unit interval, then think and data congestion does not occur, directly send in data; If newly-increased data volume is normal data amount 0.5 ~ 1.5 times (data volume in the unit interval is 1.5 ~ 2.5 times of normal data amount) in the unit interval, then thinks that data congestion is not serious, read general weight coefficient, then perform step 3; If newly-increased data volume is higher than more than 1.5 (data volume in the unit interval is more than 2.5 times of normal data amount) of normal data amount in the unit interval, then thinks and serious data congestion occurs, need to perform the real time weight analysis in step 2.

The analysis of step 2 real time weight carries out tax power by VC Method to data transmission performance indicators, namely the weight coefficient shared by data transmission performance index is calculated, the weight coefficient shared by corresponding index of problem data is increased, thus make the weighted value of problem data larger, priority is higher, delivers to SCADA system man-machine interface on preferential.

Specifically comprising the following steps of real time weight analysis:

(1) suppose there is the individual collected sample data a of m _iwherein i=1,2 ... m, collected data have n number according to transmission performance indicators f _j, j=1,2 ... n;

(2) the transmission performance data of collected data are collected, by the decision matrix X=(x that transmission performance deposit data is formed in m sample object n index _ij) _{m × n}in; VC Method is adopted to carry out tax power to data transmission performance indicators:

$\stackrel{\‾}{x_j}=\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}$

$S_j={\left[\frac{1}{m-1}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-\stackrel{\‾}{x_j})}^2\right]}^{1/2}$

Then x _jthe coefficient of variation

X _ijrepresent a jth achievement data of i-th sample object, represent the average of jth column data in decision matrix, namely jth number is according to the average of transmission performance indicators; s _jrepresent the standard deviation of jth column data in decision matrix;

(3) the weight coefficient w of a jth index _jfor:

$w_j=\frac{b_j}{{\mathrm{\Σ}}_{j=1}^n{b}_j};$

This kind is composed power method and is given prominence to each index degree of variation, b _jthe change of larger expression jth item index on different evaluation object is larger, and the ability of difference object is strong, should attach the importance.

Step 3 adopts linear weighting method to try to achieve the performance synthesis evaluation of estimate P of collected data _i, specifically comprise the following steps:

$P_i=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{w}_j{x}_{\mathrm{ij}},1\≤i\≤m$

Wherein, P _irepresent the comprehensive evaluation value of collected data, P _iless, represent that corresponding data is more inessential; P _ilarger, represent corresponding data more promptly, more important.

The beneficial effect of this method is: the present invention adopts real time weight analytical approach to carry out intelligent decision and process to a large amount of congestion data.This intelligent processing method is first according to the quantity increasing data in data buffer in the unit interval, weight coefficient shared by every data transmission performance index (right of priority, real-time, time delay, expected residual transmission time, out-of-limit grade, ANOMALOUS VARIATIONS grade etc.) is calculated, then weigthed sums approach is used to calculate the linear weighted function mean value of each packet in data stream buffer district, finally according to the transmission sequence of the size setting blocking data of this linear weighted function mean value, the packet that weighted mean value is less than setting threshold value will make discard processing.The present invention adopts the method for the collected data performance index weights coefficient of adjustment in real time, weight neatly shared by a certain performance index of actual state adjustment data of system, thus more effectively distinguish the light, heavy, slow, anxious of data, send on carrying out according to the most important batch data of actual conditions prioritizing selection.The method is compared with traditional " timeout datum abandons ", " arrive first and first send out " data processing method, have higher intelligent, can when there is data snowslide in emergency situations, effectively select significant data preferential on give, provide rational decision-making foundation to monitoring operations staff.

Accompanying drawing explanation

Fig. 1 is the congestion data process flow figure that a kind of SCADA system of the present invention is analyzed based on real time weight.

Fig. 2 is the theory diagram that the present invention is applied in typical SCADA system.

Fig. 3 is the monitoring of embodiment of the present invention congestion data and handling procedure process flow diagram.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described.

Below in conjunction with the drawings and specific embodiments, the present invention will be further described.

The present invention adopts real time weight analytical approach to carry out intelligent decision and process to a large amount of congestion data.This intelligent processing method is first according to the quantity increasing data in data buffer in the unit interval, weight coefficient shared by every data transmission performance index (as right of priority, real-time, time delay, expected residual transmission time, out-of-limit grade, ANOMALOUS VARIATIONS grade etc.) is calculated, then weigthed sums approach is used to calculate the linear weighted function mean value of each packet in data stream buffer district, finally according to the transmission sequence of the size setting blocking data of this linear weighted function mean value, the packet that weighted mean value is less than setting threshold value will make discard processing.

As shown in Figure 1, the congestion data disposal route that a kind of SCADA system is analyzed based on real time weight, comprises the following steps:

Step one, data acquisition and pre-service; Data prediction refers to carry out pre-service according to the quantity of data newly-increased in the unit interval, specifically comprise: if newly-increased data volume is 0.2 ~ 0.5 times (data volume in the unit interval is 1.2 ~ 1.5 times of normal data amount) of normal data amount in the unit interval, then think and data congestion does not occur, directly send in data; If newly-increased data volume is normal data amount 0.5 ~ 1.5 times (data volume in the unit interval is 1.5 ~ 2.5 times of normal data amount) in the unit interval, then thinks that data congestion is not serious, read general weight coefficient, then perform step 3; If newly-increased data volume is higher than more than 1.5 (data volume in the unit interval is more than 2.5 times of normal data amount) of normal data amount in the unit interval, then thinks and serious data congestion occurs, need to perform the real time weight analysis in step 2.

Step 2, real time weight analysis is carried out to image data: according to the weight coefficient shared by the feature calculation data transmission performance index of data acquisition; The feature of data acquisition refers to collected data and data transmission performance evaluation index value, and data transmission performance index comprises right of priority, real-time, time delay, expected residual transmission time, out-of-limit grade, ANOMALOUS VARIATIONS grade;

Real time weight analysis carries out tax power by VC Method to data transmission performance indicators, namely the weight coefficient shared by data transmission performance index is calculated, the weight coefficient shared by corresponding index of problem data is increased, thus make the weighted value of problem data larger, priority is higher, delivers to SCADA system man-machine interface on preferential.

Specifically comprising the following steps of real time weight analysis:

(1) suppose there is the individual collected sample data a of m _iwherein i=1,2 ... m, collected data have n number according to transmission performance indicators f _j, j=1,2 ... n;

(2) the transmission performance data of collected data are collected, by the decision matrix X=(x that transmission performance deposit data is formed in m sample object n index _ij) _{m × n}in; VC Method is adopted to carry out tax power to data transmission performance indicators:

$\stackrel{\‾}{x_j}=\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}$

$S_j={\left[\frac{1}{m-1}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-\stackrel{\‾}{x_j})}^2\right]}^{1/2}$

Then x _jthe coefficient of variation

X _ijrepresent a jth achievement data of i-th sample object, represent the average of jth column data in decision matrix, namely jth number is according to the average of transmission performance indicators; s _jrepresent the standard deviation of jth column data in decision matrix;

(3) the weight coefficient w of a jth index _jfor:

$w_j=\frac{b_j}{{\mathrm{\Σ}}_{j=1}^n{b}_j};$

This kind is composed power method and is given prominence to each index degree of variation, b _jthe change of larger expression jth item index on different evaluation object is larger, and the ability of difference object is strong, should attach the importance.

Step 3, use weigthed sums approach calculate the data performance comprehensive evaluation value (linear weighted function mean value) of packet in data stream buffer district;

Linear weighting method is adopted to try to achieve the performance synthesis evaluation of estimate P of collected data _i, specifically comprise the following steps:

$P_i=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{w}_j{x}_{\mathrm{ij}},1\≤i\≤m$

Wherein, P _irepresent the comprehensive evaluation value of collected data, P _iless, represent that corresponding data is more inessential; P _ilarger, represent corresponding data more promptly, more important.

Step 4, carry out intelligent processing method according to data performance comprehensive evaluation value to image data, that determines data uploads priority; Specifically comprise: according to the performance synthesis evaluation of estimate of the collected data that step 3 calculates, the packet of data buffer is sorted, determine packet send priority, select the high packet priority of priority delivers to SCADA interface system.

Step 5, the packet that selection priority is high are uploaded to SCADA interface system.

Fig. 2 illustrates a typical unit SCADA system.System by harvester 3 ..., N respectively from infrastructure devices 3-1,3-2 ..., 3-m and equipment N-1, N-2 ..., gather real time data, by Ethernet being delivered to SCADA server 1 in N-m.Run congestion data monitoring described in the invention and handling procedure in the server 1, and determine whether start real time weight analysis subroutine according to the real time status of collected data.The data of filtering with handling procedure through congestion data monitoring by deliver to SCADA system man-machine interface, workstation 2 shows in real time, for operations staff's reference decision-making.

Wherein, congestion data monitoring and handling procedure are to the processing procedure of data as shown in Figure 3.Collected data are admitted to data buffer, and program judges whether data congestion occurs according to the quantity of data newly-increased in the unit interval: if there is not data congestion, then directly data sent without in any process packing; If there occurs data congestion, then whether exceed threshold decision the need of startup weight analysis subroutine according to the data bulk in the unit interval: if do not exceed threshold value, then adopt general weight coefficient to calculate aggregation of data performance evaluation value; If exceed threshold value, then start weight analysis subroutine, calculate data performance comprehensive evaluation value according to new data volume and data characteristics; Then according to data performance comprehensive evaluation value determination data priority, and data assemblies identical for priority is packed, above deliver to SCADA system interface display.

Below be only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (7)

1. the congestion data disposal route analyzed based on real time weight of SCADA system, is characterized in that, comprise the following steps,

Step one, data acquisition and pre-service;

Step 2, real time weight analysis is carried out to image data: according to the weight coefficient shared by the feature calculation data transmission performance index of data acquisition;

Step 3, use weigthed sums approach calculate the data performance comprehensive evaluation value of packet in data stream buffer district;

Step 4, carry out intelligent processing method according to data performance comprehensive evaluation value to image data, that determines data uploads priority;

Step 5, the packet that selection priority is high are uploaded to SCADA interface system.

2. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, data prediction described in step one is carry out pre-service according to the quantity of data newly-increased in the unit interval, specifically comprise: if newly-increased data volume is 0.2 ~ 0.5 times of normal data amount in the unit interval, then think and data congestion does not occur, directly send in data; If newly-increased data volume is normal data amount 0.5 ~ 1.5 times in the unit interval, then thinks that data congestion is not serious, read general weight coefficient, then perform step 3; If newly-increased data volume is higher than more than 1.5 times of normal data amount in the unit interval, then thinks and serious data congestion occurs, need to perform the real time weight analysis in step 2.

3. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, is characterized in that: the feature of data acquisition described in step 2 is collected data and data transmission performance evaluation index value.

4. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, is characterized in that: the index of data transmission performance described in step 2 comprises right of priority, real-time, time delay, expected residual transmission time, out-of-limit grade, ANOMALOUS VARIATIONS grade.

5. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, is characterized in that: real time weight analysis described in step 2 carries out tax power by VC Method to data transmission performance indicators,

Described real time weight analysis specifically comprises the following steps:

(1) suppose there is the individual collected sample data a of m _iwherein i=1,2 ... m, collected data have n number according to transmission performance indicators f _j, wherein j=1,2 ... n;

(2) the transmission performance data of collected data are collected, by the decision matrix X=(x that described transmission performance deposit data is formed in a described m sample object n index _ij) _{m × n}in; VC Method is adopted to carry out tax power to data transmission performance indicators:

$\stackrel{\‾}{x_J}=\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}$

$S_j={\left[\frac{1}{m-1}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-\stackrel{\‾}{x_J})}^2\right]}^{1/2}$

Then x _jthe coefficient of variation

X _jrepresent that jth number is according to transmission performance indicators f _jcollection obtain achievement data, x _ijrepresent a jth transmission performance indicators data of i-th sample object, represent the average of jth column data in decision matrix, namely jth number is according to the average of transmission performance indicators; s _jrepresent the standard deviation of jth column data in decision matrix;

(3) the weight coefficient w of a jth index _jfor:

$w_j=\frac{b_j}{{\mathrm{\Σ}}_{j=1}^n{b}_j};$

B _jthe change of larger expression jth item index on different evaluation object is larger, and the ability of difference object is strong.

6. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, is characterized in that: step 3 adopts linear weighting method to try to achieve the performance synthesis evaluation of estimate P of collected data _i, specifically comprise the following steps:

$P_i=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{w}_j{x}_{\mathrm{ij}},1\≤i\≤m$

Wherein, P _irepresent the comprehensive evaluation value of collected data, P _iless, represent that corresponding data is more inessential; P _ilarger, represent corresponding data more promptly, more important.

7. the congestion data disposal route analyzed based on real time weight of SCADA system according to claim 1, it is characterized in that: in step 4, according to the performance synthesis evaluation of estimate of the collected data that step 3 calculates, the packet of data buffer is sorted, determine packet send priority, select the high packet priority of priority delivers to SCADA interface system.