CN106027288A - Communication traffic prediction method for distribution line information monitoring service - Google Patents
Communication traffic prediction method for distribution line information monitoring service Download PDFInfo
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- H04L41/147—Network analysis or design for predicting network behaviour
Abstract
The invention provides a communication traffic prediction method for the distribution line information monitoring service. Aiming at that the existing traffic prediction method gives no consideration to the dynamic arrival characteristic of service communication data flows and causes a problem of great deviation of a traffic prediction value, the method provided by the invention gives full consideration to a dynamic arrival factor of the service data flows, and the method comprises steps: firstly, on the basis of performing traffic parameter estimation of a distribution line information monitoring service through a poisson traffic model, effectively predicting traffic of the distribution line information monitoring service according to a queuing theory model; secondly, training model parameters with original data and performing parameter optimization; and finally, performing communication traffic prediction through a poisson queuing theory traffic model. The method provided by the invention overcomes a problem that the conventional method cannot describe influence of a poisson traffic dynamic influence factor of the service on a prediction result, improves prediction accuracy, and facilitates implementation of reasonable planning and allocation of bandwidth resource of an intelligent distribution and utilization communication network.
Description
Technical field
The invention belongs to intelligence adapted technical field of telecommunications, particularly relate to a kind of distribution line information monitoring service communication method for predicting.
Background technology
Adapted telecommunication net is as a part for power communication system, it it is the carrier of adapted electrical network various information transmission, playing an important role in the business such as power distribution automation, power information collection, distributed power source access, wherein service communication volume forecasting is the basis of communication network bandwidth distribution.The method for predicting of electric power existing power distribution information monitoring business uses the computational methods that intutive forecasting combines with coefficient of elasticity.
Dynamically arrive characteristic because of do not consider distribution line information monitoring business data transmission under intelligent grid background, have that traffic prediction value is big with actually required bandwidth deviation and the problem of bandwidth resources overabsorption that causes.
Summary of the invention
It is an object of the invention to provide a kind of distribution line information monitoring service communication method for predicting, the method comprises the following steps.
Step 1: obtain distribution line information monitoring business datum time series according to the information monitoring cycle.
According to distribution line information monitoring business data transmission characteristic, monitor the data on flows cycle with time T division information；Being spaced for acquisition time with 1 second unit interval with cycle T as acquisition length, statistical information monitoring business arrives the data bit number of transformer substation communication node and constitutes data on flows sequence；M secondary data statistics is repeated and obtains M data on flows sequence as volume forecasting basic data.
Step 2: histogram method statistics obtains the probability distribution of information monitoring service traffics data sequence.
If the sequence number collection of data on flows sequence is combined into IDSet={1,2 ..., M}, using M data on flows sequence obtaining as the sample original data source of probability statistics.For sequence number i (i ∈ IDSet) data on flows sequence, with 1 second unit interval for probability statistics unit, obtain shown in the probability distribution equation below of data on flows sequence according to statistics with histogram method: Pi(N=n), wherein n arrives the information monitoring bits of traffic data number of communication node, i.e. service traffics in being 1 second unit time, and unit is bps；N is Positive Integer Set, arrives the information monitoring bits of traffic data number of communication node, the independent variable of corresponding probability distribution in 1 second representation unit time.Flow data sequence carries out probability distribution statistical successively and calculates operation, then M data on flows sequence pair answers probability distribution to constitute sequence probability distributed collection { Pi(N=n)} (i∈IDSet)。
Step 3: use the poisson arrival parameter of Least Square Method data on flows sequence.
For the data on flows sequence of sequence number i (i ∈ IDSet), corresponding sequence probability distribution Pi(N=n), method of least square parameter estimation strategy is used, according to the below equation arrival parameter lambda to flow data sequence Poisson traffic modeliEstimate.
Pi(N=n)=e- λ i(λi)n/ n!.
Wherein λiBeing that Poisson flow distributed model arrives parameter, arrive mathematic expectaion E (x) of the data bit number of the information monitoring business of communication node, i.e. average traffic flow in representing 1 second unit interval, unit is bps.
Successively each sequence is carried out above parameter estimation operation, the Poisson traffic model arrival flow parameter λ that M data on flows sequence pair is answered can be obtainedi, constitute poisson arrival parameter sets LamdaSet={ λi}。
Step 4: rejected the poisson arrival abnormal parameters value of data on flows sequence by normal distribution confidence interval.
With parameter lambda in arrival parameter sets LamdaSet of data on flows sequenceiFor independent variable, structure normal distribution curve Norm (μ, δ), wherein the average of normal distribution and variance parameter are respectively according to μ=E (λi) and δ=var (λi) calculate.Arranging confidence interval is [μ-2* δ, μ+2* δ], is removed by the arrival parameter value outside confidence interval from LamdaSet gathers, it is achieved arrives the rejecting operation of flow parameter exceptional value, obtains optimum poisson arrival parameter set to be selected validSet={ λj, wherein λj∈[μ-2*δ,μ+2*δ]。
Step 5: use median-of-three sort method to select the optimum poisson arrival parameter of data on flows sequence.
To the element λ in optimum poisson arrival parameter set to be selected validSetj(j=1 ..., q) carry out ascending sort operation, obtain λ1<λ2<…<λmid<…<λq-1 <λq, wherein λmidFor intermediate value；Choose λmidOptimum poisson arrival parameter lambda for information monitoring service traffics data sequenceopt。
Step 6: calculate distribution line information monitoring service traffics according to queue theory model.
With optimum poisson arrival parameter lambdaoptAs the arrival rate of the queue theory model of traffic prediction, the maximum delay of given distribution line information monitoring business requires TDelaySecond, service traffics B meeting traffic delay requirement are calculated according to following queue theory model formula.
B=λopt+ 1/TDelay
。
Using B as distribution line information monitoring traffic prediction value.When communication node service traffics band width configuration is B, it is possible to meet the Delay Service prescription of business.
First These steps carries out arrival parameter estimation based on Poisson traffic model and preferred operations to distribution line information monitoring service traffics；Secondly using arrival parameter as important input, effective traffic prediction is realized in conjunction with poisson arrival queue theory model；Because taking into full account that business datum arrives characteristic, can effectively reduce the prediction deviation that existing static prediction method is brought.
Described step 1 divides cycle T according to distribution line information monitoring business datum characteristic；Being spaced for acquisition time with 1 second unit interval with cycle T as acquisition length, the time series that the data bit flow that M some distribution line information monitoring business arrival transformer substation communication node of statistics acquisition is formed is constituted is as volume forecasting basic data.
With M the data on flows Sequence composition training sample obtained in described step 2, statistics with histogram method is used to obtain the probability distribution that data on flows sequence pair is answered.
To each sequence probability distribution P in sequence probability distributed collection in described step 3i
(N=n), use method of least square parameter estimation strategy, carry out poisson arrival parameter lambda according to Poisson traffic modeliEstimation operation；Obtain M and arrive flow parameter λi, constitute poisson arrival parameter sets LamdaSet={ λi}。
With parameter lambda in arrival parameter sets LamdaSet of data on flows sequence in described step 4iFor independent variable, structure normal distribution curve Norm (μ, δ), reject poisson arrival parameter lambda by arranging confidence intervaliIn exceptional value, obtain optimum poisson arrival parameter set to be selected validSet={ λj, wherein λj ∈[μ-2*δ,μ+2*δ]。
To the element λ in optimum poisson arrival parameter set to be selected validSet in described step 5jCarry out ascending sort operation, use the optimum poisson arrival parameter that intermediate value is information monitoring service traffics data sequence of median-of-three sort method selected and sorted.
With optimum poisson arrival parameter lambda in described step 6optAs the arrival rate of the queue theory model of traffic prediction, the maximum delay of given distribution line information monitoring business requires TDelay, calculate service traffics B meeting traffic delay requirement, using B as distribution line information monitoring traffic prediction value.
Compared with general technology, distribution line information monitoring service communication method for predicting of the present invention, do not consider that service communication data flowable state arrives characteristic and causes the volume forecasting big problem of value deviation for existing method for predicting, take into full account the dynamic arrival factor of business data flow, on the basis of Poisson traffic model carries out the flow parameter estimation of distribution line information monitoring business, according to queue theory model, distribution line information monitoring service traffics are carried out effective prediction.Use initial data training pattern parameter and parameter preferred, then carry out Poisson queueing theory discharge model and communicate volume forecasting, overcome traditional method and cannot describe the Poisson flow dynamic effect factor of the business problem on the impact that predicts the outcome, improve precision of prediction, be advantageously implemented making rational planning for and configured bandwidth resource of intelligence adapted telecommunication network.
Accompanying drawing explanation
Fig. 1 is the inventive method overall flow figure.
Fig. 2 is distribution line information monitoring business typical scene schematic diagram.
Fig. 3 is that certain province's information monitoring business arrives 35kV transformer station Business Stream spirogram.
Fig. 4 is that the inventive method normal distribution Estimating Confidence Interval parameter rejects operation figure.
Detailed description of the invention
Below in conjunction with the accompanying drawings, preferred embodiment is elaborated.It is emphasized that it is that the description below is merely exemplary rather than in order to limit the scope of the present invention and application thereof.As a example by a distribution line information monitoring typical services scene, this method implementation process is described.Business scenario parameter is as shown in table 1.By 32 outlets of each transformer station, every distribution line on-pole switch 15, Switching Station 2, ring main unit 8, box-type substation 30, pole transformer 50 calculating, then the distribution automation business net flow of 32 outlets is (12 × 15+93 × 2+32 × 8+23 × 30+13 × 50) × 32 × 8=502272bit/s ≈ 502kbps, the poisson arrival parameter lambda of Poisson traffic model in corresponding this method.Stipulations extra byte is by the 10% of business net flow simultaneously, bandwidth allowance is by 50% consideration of business net flow, according to above-mentioned power distribution network scale, use coefficient of elasticity intuitive forecasting technique can show that the traffic prediction value of power distribution automation business backbone's access network is about 502 × 1.6=803kbps.
Table 1 distribution line information monitoring typical services parameter list.
。
A kind of distribution line information monitoring service communication method for predicting, method flow is as it is shown in figure 1, the method comprises the steps.
Step 1: obtain distribution line information monitoring business datum time series according to the information monitoring cycle.
According to distribution line information monitoring business data transmission characteristic, monitor the data on flows cycle with time T division information；Being spaced for acquisition time with 1 second unit interval with cycle T as acquisition length, statistical information monitoring business arrives the data bit number of transformer substation communication node and constitutes data on flows sequence；M secondary data statistics is repeated and obtains M data on flows sequence as volume forecasting basic data.
Distribution line information monitoring typical services scene is as in figure 2 it is shown, by adding up certain data bit number composition data on flows sequence saving information monitoring business arrival 35kV transformer substation communication node in example, take cycle T=600 second, M=10；10 secondary data statistics are repeated and obtain 10 data on flows sequences as volume forecasting basic data, as shown in Figure 3.
Step 2: histogram method statistics obtains the probability distribution of information monitoring service traffics data sequence.
If the sequence number collection of data on flows sequence is combined into IDSet={1,2 ..., M}, using M data on flows sequence obtaining as the sample original data source of probability statistics.For sequence number i (i ∈ IDSet) data on flows sequence, with 1 second unit interval for probability statistics unit, obtain shown in the probability distribution equation below of data on flows sequence according to statistics with histogram method: Pi(N=n), wherein n arrives the information monitoring bits of traffic data number of communication node, i.e. service traffics in being 1 second unit time, and unit is bps；N∈Z+( Z+For Positive Integer Set) it is the information monitoring bits of traffic data number arriving communication node in 1 second unit time, the independent variable of corresponding probability distribution.Flow data sequence carries out probability distribution statistical successively and calculates operation, then M data on flows sequence pair answers probability distribution to constitute sequence probability distributed collection { Pi(N=n)} (i∈IDSet)。
Step 3: use the poisson arrival parameter of Least Square Method data on flows sequence.
For the data on flows sequence of sequence number i (i ∈ IDSet), corresponding sequence probability distribution Pi(N=n), method of least square parameter estimation strategy is used, according to the below equation arrival parameter lambda to flow data sequence Poisson traffic modeliEstimate.
Pi(N=n)=e- λ i(λi)n/ n!.
Wherein λiBeing that Poisson flow distributed model arrives parameter, arrive mathematic expectaion E (x) of the data bit number of the information monitoring business of communication node, i.e. average traffic flow in representing 1 second unit interval, unit is bps.
Successively each sequence is carried out above parameter estimation operation, the Poisson traffic model arrival flow parameter λ that M data on flows sequence pair is answered can be obtainedi, constitute poisson arrival parameter sets LamdaSet={ λi}。
Fundamentals of forecasting data in Fig. 3 carrying out in example Poisson traffic model arrival flow parameter λ estimate, unit is kbps；Obtain poisson arrival parameter sets LamdaSet, comprise 10 estimated values, as shown in table 2.
Table 2 Poisson traffic model flow arrives estimates of parameters table.
| Sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Parameter | 654.76 | 592.50 | 605.87 | 401.95 | 659.33 | 483.94 | 447.57 | 588.02 | 510.17 | 676.46 |
Step 4: rejected the poisson arrival abnormal parameters value of data on flows sequence by normal distribution confidence interval.
With parameter lambda in arrival parameter sets LamdaSet of data on flows sequenceiFor independent variable, structure normal distribution curve Norm (μ, δ), wherein the average of normal distribution and variance parameter are respectively according to μ=E (λi) and δ=var (λi) calculate.If confidence interval is [μ-2* δ, μ+2* δ], the arrival parameter value outside confidence interval is removed from LamdaSet gathers, it is achieved arrive the rejecting operation of flow parameter exceptional value, obtain optimum poisson arrival parameter set to be selected validSet={ λj, wherein λj∈[μ-2*δ,μ+2*δ]。
Example can be obtained μ=E (λ by data in table 2i)=518.57, δ=var (λi)=65.90；Confidence interval is [386.77,650.38], as it is shown on figure 3, three values (654.76,659.33,676.46) are because being rejected outside confidence interval in estimates of parameters；Obtain optimum poisson arrival parameter set to be selected validSet, comprise 7 optional estimated values, as shown in table 3.
The optimum poisson arrival estimates of parameters table of table 3.
| Sequence number | 2 | 3 | 4 | 6 | 7 | 8 | 9 |
| Parameter | 592.50 | 605.87 | 401.95 | 483.94 | 447.57 | 588.02 | 510.17 |
Step 5: use median-of-three sort method to select the optimum poisson arrival parameter of data on flows sequence.
To the element λ in optimum poisson arrival parameter set to be selected validSetj(j=1 ..., q) carry out ascending sort operation, obtain λ1<λ2<…<λmid<…<λq-1 <λq, wherein λmidFor intermediate value；Choose λmidOptimum poisson arrival parameter lambda for information monitoring service traffics data sequenceopt.In example, optional estimated value as shown in table 3 is ranked up operation, 401.95 < 447.57 < 483.94 < λmid=510.17 < 588.02 < 592.50 < 605.87, it is thus achieved that optimum poisson arrival parameter lambdaopt=510.17, the average discharge i.e. predicted is 510.17kbps.
Step 6: calculate distribution line information monitoring service traffics according to queue theory model.
With optimum poisson arrival parameter lambdaoptAs the arrival rate of the queue theory model of traffic prediction, the maximum delay of given distribution line information monitoring business requires TDelaySecond, service traffics B meeting traffic delay requirement are calculated according to following queue theory model formula.
B=λopt+ 1/TDelay
。
Using B as distribution line information monitoring traffic prediction value.When communication node service traffics band width configuration is B, it is possible to meet the Delay Service prescription of business.
In example, distribution line information monitoring business maximum delay requires TDelay=0.1s, the average discharge λ of this method predictionopt=510.17kbps, therefore can calculate B=520.17kbps.The predicted flow rate of electric power existing coefficient of elasticity intuitive forecasting technique is 803kbps, and therefore this method is compared with coefficient of elasticity intuitive forecasting technique, closer to business net flow 502kbps.
The above; being only the present invention preferably detailed description of the invention, but protection scope of the present invention is not limited thereto, any those familiar with the art is in the technical scope that the invention discloses; the change that can readily occur in or replacement, all should contain within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with scope of the claims.
Claims (7)
1. a distribution line information monitoring service communication method for predicting, it is characterised in that comprise the following steps:
Step 1: obtain distribution line information monitoring business datum time series according to the information monitoring cycle；
Step 2: histogram method statistics obtains the probability distribution of information monitoring service traffics data sequence；
Step 3: use the poisson arrival parameter of Least Square Method data on flows sequence；
Step 4: rejected the poisson arrival abnormal parameters value of data on flows sequence by normal distribution confidence interval；
Step 5: use median-of-three sort method to select the optimum poisson arrival parameter of data on flows sequence；
Step 6: calculate distribution line information monitoring service traffics according to queue theory model.
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterized in that, described according in information monitoring cycle acquisition distribution line information monitoring business datum seasonal effect in time series step, according to distribution line information monitoring business data transmission characteristic, monitor the data on flows cycle with time T division information；Being spaced for acquisition time with 1 second unit interval with cycle T as acquisition length, statistical information monitoring business arrives the data bit number of transformer substation communication node and constitutes data on flows sequence；M secondary data statistics is repeated and obtains M data on flows sequence as volume forecasting basic data.
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterized in that, in the step of the probability distribution that described histogram method statistics obtains information monitoring service traffics data sequence, if the sequence number collection of data on flows sequence is combined into IDSet={1,2, ..., M}, using M data on flows sequence obtaining as the sample original data source of probability statistics；For sequence number i (i ∈ IDSet) data on flows sequence, with 1 second unit interval for probability statistics unit, obtain shown in the probability distribution equation below of data on flows sequence according to statistics with histogram method: Pi(N=n), wherein n arrives the information monitoring bits of traffic data number of communication node, i.e. service traffics in being 1 second unit time, and unit is bps, N ∈ Z+( Z+For Positive Integer Set) it is the information monitoring bits of traffic data number arriving communication node in 1 second unit time, the independent variable of corresponding probability distribution；Flow data sequence carries out probability distribution statistical successively and calculates operation, then M data on flows sequence pair answers probability distribution to constitute sequence probability distributed collection { Pi(N=n)} (i∈IDSet)。
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterized in that, in the step of the poisson arrival parameter of described employing Least Square Method data on flows sequence, for the data on flows sequence of sequence number i (i ∈ IDSet), corresponding sequence probability distribution Pi(N=n), method of least square parameter estimation strategy is used, according to the below equation arrival parameter lambda to flow data sequence Poisson traffic modeliEstimate:
Pi(N=n)=e- λi(λi)n/ n!
Wherein λiBeing that Poisson flow distributed model arrives parameter, arrive mathematic expectaion E (x) of the data bit number of the information monitoring business of communication node, i.e. average traffic flow in representing 1 second unit interval, unit is bps；
Successively each sequence is carried out above parameter estimation operation, the Poisson traffic model arrival flow parameter λ that M data on flows sequence pair is answered can be obtainedi, constitute poisson arrival parameter sets LamdaSet={ λi}。
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterized in that, in the step of the described poisson arrival abnormal parameters value being rejected data on flows sequence by normal distribution confidence interval, with parameter lambda in arrival parameter sets LamdaSet of data on flows sequenceiFor independent variable, structure normal distribution curve Norm (μ, δ), wherein the average of normal distribution and variance parameter are respectively according to μ=E (λi) and δ=var (λi) calculate；Arranging confidence interval is [μ-2* δ, μ+2* δ], is removed by the arrival parameter value outside confidence interval from LamdaSet gathers, it is achieved arrives the rejecting operation of flow parameter exceptional value, obtains optimum poisson arrival parameter set to be selected validSet={ λj, wherein λj∈[μ-2*δ,μ+2*δ]。
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterized in that, described employing median-of-three sort method selects in the step of the optimum poisson arrival parameter of data on flows sequence, to the element λ in optimum poisson arrival parameter set to be selected validSetj(j=1 ..., q) carry out ascending sort operation, obtain λ1<λ2<…<λmid<…<λq-1 <λq, wherein λmidFor intermediate value；Choose λmidOptimum poisson arrival parameter lambda for information monitoring service traffics data sequenceopt。
Distribution line information monitoring service communication method for predicting the most according to claim 1, it is characterised in that in the described step calculating distribution line information monitoring service traffics according to queue theory model, with optimum poisson arrival parameter lambdaoptAs the arrival rate of the queue theory model of traffic prediction, the maximum delay of given distribution line information monitoring business requires TDelaySecond, calculate according to following queue theory model formula and meet service traffics B of traffic delay requirement:
B=λopt+ 1/TDelay；
Using B as distribution line information monitoring traffic prediction value, when communication node service traffics band width configuration is B, it is possible to meet the Delay Service prescription of business.
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