CN114124824B - Event-triggered filtering estimation method for congestion condition of people flow dense area network - Google Patents
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Abstract
The invention discloses a method for estimating congestion condition event-triggered filtering of a people stream dense area network. The invention provides a method for data acquisition and real-time estimation of data quantity received by a base station router in a traffic intensive area communication network system based on a forward switching system modeling method, an event triggering strategy and a nonlinear asynchronous filter technology. The method considers the nonlinear phenomenon of an actual system and the asynchronous phenomenon caused by the delay of system switching during modeling, and further provides a nonlinear asynchronous modeling method, which is more in line with the actual situation. The event triggering technology adopted by the method not only can avoid network congestion when the network is busy, but also can ensure the normal operation of the system under the condition of insufficient network bandwidth. Meanwhile, the event triggering technology can also reduce the cost of the proposed design and save the cost.
Description
Technical Field
The invention relates to the technical field of automation, in particular to an asynchronous event triggering filtering method for estimating network congestion conditions of a dense area of network users.
Background
With the progress of the communication technology in the information age, various intelligent communication devices are continuously updated. From almost people's requisite cell-phone to portable intelligent wearing equipment such as panel computer, intelligent wrist-watch, people's demand to intelligent equipment has reached the pole of history. The demands of people on the networks on which intelligent communication devices depend are also increasing, and network congestion is the biggest limitation affecting people's normal network usage. According to authoritative media investigation, the scale of the netizen of China is 9.89 hundred million people by 2020, and the mobile internet users are more than 16 hundred million, so that under the huge user base, even if the network technology of China is continuously developed, the basic communication facilities are continuously perfected, and the stability of network operation still faces a great challenge. Because the storage space, bandwidth capacity and processor performance of the base station are limited, network congestion and even network system breakdown are easily caused by the ultra-dense and large-data transmission in the area with very dense people stream, such as concert, tourist attractions in eleven golden weeks and the like. Therefore, it is very practical to design an event triggered filter to estimate the amount of data received by a router for a dense area communication network.
Because of the number of data packets in the communication network, the number of data packets received and transmitted by the router must be nonnegative, and the nonnegative quantity is modeled more accurately by a positive system. For an area, such as a scenic spot and a meeting place, the people flow is not always intensive, and the network communication system in the corresponding area is not always busy, and the router can be classified into idle time and busy time according to the amount of data received by the router in a short time. Meanwhile, due to different people flow gathering degrees, the busyness of different base stations in the area is different. Therefore, the area communication network can be modeled by using a switching system comprising an unstable subsystem, and the modeling by using an asynchronous control system is more in line with the actual requirements than a synchronous control system because of detection and delay on system hardware when the actual system state is switched. In addition, due to the complexity of the network system, the existing linear positive network communication system model has obvious conservation in describing the complexity of the system, and the nonlinear system has obvious advantages in modeling, so that the modeling of the network communication system by using the nonlinear positive system is more reasonable. The event triggering filtering strategy is a real-time filtering design method based on events, and when the system is busy, the event triggering strategy can be adopted to estimate the data quantity received by the base station router in real time, so that the network node can be conveniently regulated and controlled, the occurrence of network congestion is avoided, and the network information transmission efficiency is improved.
Disclosure of Invention
The invention provides a method for designing a network congestion event triggering filter under nonlinear asynchronism, which is used for estimating the number of data packets received by a data terminal of a traffic intensive area communication network in real time.
The technical scheme adopted by the invention for solving the problems comprises the following steps:
step 1, a state space model of a network communication system is established, and the specific method is as follows:
1.1, collecting input and output data quantity of a data terminal of a traffic intensive area communication network to describe an actual system;
1.2 constructing a state space model of the communication network system;
1.3, determining that the nonlinear system meets the condition;
step 2, establishing event triggering conditions of the communication network system;
step 3, establishing an event triggering asynchronous filter model;
step 4, constructing an error system under nonlinear asynchronous communication;
and 5, designing a nonlinear asynchronous event trigger filter aiming at network congestion estimation.
Further, the nonlinear system determined in step 1.3 satisfies the following conditions:
wherein,0＜ε 1 ≤ε 2 0＜ε 3 ≤ε 4 ,0＜ε 5 ≤ε 6 ,f i (0)＝0。
3. the method for estimating congestion status event-triggered filtering of a people stream dense area network according to claim 2, wherein the event-triggered condition for establishing the communication network system in step 2 is constructed as follows:
||e y (k)|| 1 ＞β||y(k)|| 1 ，
where β is a given constant and satisfies β ε [0, 1), e y (k) Is a sampling error and satisfiesk∈[k l ,k l+1 )，/>y(k l ) Indicating when a communication network system is triggered by an eventEtching k l Output value of (2); l epsilon N + ，‖·‖ 1 Representing the 1-norm of the vector, i.e. the sum of the absolute values of all elements in the vector.
Further, the building event triggering asynchronous filter model in step 3 has the following structural form:
wherein x is f (k) Representing the state signal, z, of the filter f (k) Representing an estimate of the output signal z (k), A fi ,B fi ,E fi ,F fi Is a filter matrix to be designed.
Further, the error system under nonlinear asynchronous communication is constructed in the step 4, which is obtained by:
when k is E [ k r ,k r +Δ r ) Sometimes there is
When k is E [ k r +Δ r ,k r +1), when there is
Wherein,
further, the step 5 designs a nonlinear asynchronous event triggered filter for network congestion estimation, wherein a state space model of the communication system is:
x(k+1)＝A σ(k) f(x(k))+B σ(k) g(ω(k))
y(k)＝C σ(k) h(x(k))+D σ(k) l(ω(k))
z(k)＝E σ(k) p(x(k))+F σ(k) q(ω(k))
wherein x (k) = [ x ] 1 (k),x 2 (k),...,x n (k)] T ∈R n The data transmission quantity of the communication network in the region at the moment k, wherein n represents the system capacity number of the base station in the region;is an external disturbance in the operation of the communication network; y (k) ∈R m For the actual output of the data quantity received by the data terminal acquired by the sensor at the moment k, m represents the number of measuring output sensors; z (k) ∈R s The method comprises the steps of estimating and outputting the received data quantity of a base station data terminal at the moment k; nonlinear function f (x) ∈R n ,g(x)∈R m ,h(x)∈R n ,l(x)∈R m ,p(x)∈R n ,q(x)∈R m The function σ (k) is a switching signal, representing [0, ++]Mapping to a finite set s= {1,2, …, n+ }; let σ (k) =i, i e S, then the system matrix can be denoted as a i ,B i ,C i ,D i ,E i ,F i The method comprises the steps of carrying out a first treatment on the surface of the Matrix A i ≥0,B i ≥0,C i ≥0,D i ≥0,E i ≥0；R n ,/>N + ,R n+n Respectively representing an n-dimensional vector, an n-dimensional non-negative vector, a positive integer and an n x n-dimensional euclidean matrix space.
Further, the specific steps of step 5 are as follows:
5.1 event triggered asynchronous filter system matrix designed as follows:
wherein,is R n Vector (S)>Is R m Vector; 1 n N-dimensional vector representing all elements 1, < ->An n-dimensional vector representing the mu-th element as 1 and the remaining elements as 0;
5.2 design constant
0＜ε 1 ≤ε 2 ,0＜ε 3 ≤ε 4 ,
0＜ε 5 ≤ε 6 ,γ＞0,γ＞1,0≤β＜1,0＜μ 1 <1,μ 2 >1, if R is present n (Vector)
ξ i ≥0,/>ξ j ≥0,/>R m (Vector)
δ i ≥0,δ j ≥0,/>The following inequality is caused:
ζ i ≤λζ (i，j) ，ζ i ≤λζ (j，i) ，ζ (i，j) ≤λζ i ，ζ (j，i) ≤λζ i ，
for any i, j e S, i +.j,the constructed error system is stable in L1 gain under the filter gain matrix and the switching rate;
5.3 designing the switching rate of the state space model of the system to be switched as follows:
wherein the method comprises the steps of
Wherein Γ is - (k 0 ，k)，Γ + (k 0 K) represents the total time of synchronous and asynchronous operation of the filter and the switching system, respectivelyM, τ a Indicating the residence time, delta, of the switching subsystem max Representing the maximum asynchronous time of the filter and the corresponding subsystem;
5.4 according to the positive constraint conditions of step 2, step 4.2, step 5.1 and step 5.2, the condition that the error system is positive is obtained:
when k is E [ k r ，k r +Δ r ) Time of day
When k is E [ k r +Δ r ，k r +1) time
Wherein,
5.5 obtaining the guaranteed error System L according to step 2 and step 4.2 1 Conditions for gain stabilization:
when k is E [ k r ，k r +Δ r ) Time of day
When k is E [ k r +Δ r ，k r +1) time
Wherein,
5.6 constructing a redundant positive Lyapunov function:
wherein,combining the steps 5.5 to obtain:
wherein,
from the conditions in step 5.2 and step 5.3, it is possible to obtain:
description of the communication network error System being L under the designed event triggered Filter according to step 5.6 1 The gain is stable.
The invention provides a filter design method for monitoring congestion conditions of a traffic-intensive area communication network in real time. The invention provides a method for monitoring network communication efficiency in real time aiming at data acquisition of application amount accessed by users in a communication network in an area based on a forward switching system model, an event triggering strategy and a nonlinear asynchronous filter, so that data transmission can be regulated and controlled on a network node, network collapse is avoided, and data transmission efficiency is improved. Meanwhile, the characteristics of non-linearity, asynchronous switching and the like of the established model are better suitable for the system state in the actual application scene, and the method is more generally applicable to various areas where network congestion is likely to occur.
Drawings
FIG. 1 is a schematic diagram of a communication network system according to the present invention
Fig. 2 is a block diagram of an event triggered filter in a communication network system
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, a dynamic model of a network communication transmission system is built by taking a certain network communication transmission system as a research object, taking the communication application data quantity entering a base station router as input, and taking the data quantity actually and completely sent by the router as output.
And step 1, combining a network communication system to establish a state space model.
1.1, collecting input and output data quantity of a data terminal of a traffic intensive area communication network to describe an actual system:
considering the flow of user network communication in an area, a communication network system mainly comprises a user end, a switch, a router and a server end, and the structure diagram of a certain communication network system in fig. 1 is shown (see the drawing in the specification). Fig. 1 shows the association among a network card, a base station switch, a router and a server in an intra-area network communication flow. Wherein, the red arrow indicates the process that the network communication application of the user arrives at the switch, and the blue arrow indicates the process that the switch successfully receives and processes the application and then forwards the application to the corresponding server side in the Internet by the router. Since in a resource sharing network without any negotiation and request permission mechanism in advance, several user IP packet data arriving at the router at the same time may be expected to be forwarded via the same output port, but they must not be processed at the same time, the buffer on the intermediate node may provide a certain protection for the data waiting to be served, but since the base station memory space, bandwidth capacity and processor performance are limited, the router only discards the data when the buffer space is exhausted. In such a continuously overloaded condition, network congestion may occur, causing the network performance to drop or collapse dramatically. At this time, an event-triggered filtering estimation method is very needed to monitor and estimate the data transmission amount of the communication network in the area in real time so as to ensure that the area network operates normally. Considering that the nonlinear situation in the real scene is more universal, components and parts are limited by conditions such as mechanical physics and the like, delay is necessarily generated during switching, so that the system enters an asynchronous state, and modeling is performed by adopting a nonlinear asynchronous system during modeling, so that the system is more fit with an actual system. The structure of the designed event-triggered filtering method is shown in fig. 2.
1.2, data acquisition is carried out on communication traffic of an intra-area network, and a state space model of the communication traffic in the communication network system is established by utilizing the data, wherein the form is as follows:
x(k+1)＝A σ(k) f(x(k))+B σ(k) g(ω(k))
y(k)＝C σ(k) h(x(k))+D σ(k) l(ω(k))
z(k)＝E σ(k) p(x(k))+F σ(k) q(ω(k))
wherein x (k) = [ x ] 1 (k)，x 2 (k)，...，x n (k)] T ∈R n And n represents the number of the system capacity of the base station in the region for the data transmission quantity of the communication network in the region at the moment k.Is an external disturbance in the operation of the communication network (such as a sudden increase in the transmission data volume applied by a user in a short time, a communication failure of the base station equipment, etc.). y (k) ∈R m For the actual output of the data quantity received by the data terminal acquired by the sensor at the moment k, m represents the number of measuring output sensors; z (k) ∈R s The method is to estimate and output the data quantity received by the base station data terminal at the moment k. Nonlinear function f (x) ∈R n ，g(x)∈R m ，h(x)∈R n ，l(x)∈R m ，p(x)∈R n ，q(x)∈R m The function σ (k) is a switching signal, representing [0, ++]To a finite set s= {1,2, …, N + Mapping of. Let σ (k) =i, i e S, then the system matrix can be denoted as a i ，B i ，C i ，D i ，E i ，F i . Matrix A i ≥0，B i ≥0，，C i ≥0，D i ≥o，E i ≥o。R n ，/>N + ，R n×n Respectively representing an n-dimensional vector, an n-dimensional non-negative vector, a positive integer and an n x n-dimensional euclidean matrix space.
1.3 nonlinear functions f (x), g (x), h (x), l (x), p (x), q (x), for anyi=1, 2, …, n, iota=1, 2, …, m, all satisfy the following sector areas:
wherein,0＜ε 1 ≤ε 2 0＜ε 3 ≤ε 4 ，0＜ε 5 ≤ε 6 ，f i (0)＝0。
step 2, constructing event trigger control conditions of communication network congestion:
||e y (k)|| 1 ＞β||y(k)|| 1 ，
where β is a given constant and satisfies β ε [0, 1), e y (k) Is a sampling error and satisfiesk∈[k l ，k l+1 )，/>y(k l ) Indicating the communication network system at event trigger time k l Output value of l epsilon N + ，||.|| 1 Representing the 1-norm of the vector, i.e. the sum of the absolute values of all elements in the vector.
Step 3, establishing an event triggering asynchronous filter model, wherein the structure form is as follows:
wherein x is f (k) Representing the state signal, z, of the filter f (k) Representing an estimate of the output signal z (k) by the filter, A fi ，B fi ，E fi ，F fi Is a filter matrix to be designed.
And 4, constructing an error system under nonlinear asynchronization, which comprises the following steps:
4.1 according to step 1.2 and step 3.2, lete(k)＝z f (k) -z (k) constructing an error system under nonlinear asynchrony:
e(k)＝E fj p(x f (k))+F fj C i h(x(k))-E i p(x(k))+F fj D i l(ω(k))-F i q(ω(k))+F fj e y (k)，
a kind of electronic device with high-pressure air-conditioning system
e(k)＝E fi p(x f (k))+F fi C i h(x(k))-E i p(x(k))+F fi D i l(ω(k))-F i q(ω(k))+F fi e y (k)，
Wherein x is T (k) Representing the transpose of the vector x (k), the system is described in [ k ] r ，k r +1) the intra-time switching signal sigma (k) has been switched to ith, while the filter is at [ k ] r ，k r +Δ r ) The system and the filter are in an asynchronous state when the system is still in the j state after not being switched in time; [ k ] r +Δ r ，k r +1) the filter switches to the i-th state in the period of time, which is the synchronous state. Delta r Is the filter lag time, where delta 0 ＝0，Δ r ＜k r+1 -k r ，r＝1，2，…。k r The system switching time is the system switching time.
4.2 the error system in the asynchronous time period and the synchronous time period can be respectively obtained by using the step 1.3 to meet the following conditions:
when k is E [ k r ，k r +Δ r ) Sometimes there is
When k is E [ k r +Δ r ，k r +1), when there is
Wherein,
and 5, designing a nonlinear asynchronous event trigger filter aiming at network congestion estimation.
5.1 event triggered filter system matrices designed as follows:
wherein,is R n Vector (S)>Is R m Vector. 1 n N-dimensional vector representing all elements 1, < ->An n-dimensional vector representing the mu-th element as 1 and the remaining elements as 0.
5.2 design constant0＜ε 1 ≤ε 2 ，0＜ε 3 ≤ε 4 ，0＜ε 5 ≤ε 6 ，γ＞0，λ＞1，0≤β＜1，0＜μ 1 ＜1，μ 2 > 1, R is present n (Vector)R m Vector delta i ≥0，/>δ j ≥0，/>The following inequality is caused:
ζ i ≤λζ (i，j) ，ζ i ≤λζ (j，i) ，ζ (i，j) ≤λζ i ，ζ (j，i) ≤λζ i ，
for any i, j e S, i+.j, i=1, 2, …, n, j=1, 2, …, S, the error system constructed is L at the filter gain matrix and the switching rate 1 The gain is stable.
5.3 designing the switching rate of the state space model of the system to be switched as follows:
wherein the method comprises the steps of
Wherein Γ is - (k 0 ，k)，Γ + (k 0 K) represents the total time of synchronous and asynchronous operation of the filter and the switching system, τ, respectively a Indicating the residence time, delta, of the switching subsystem max Representing the maximum asynchronous time of the filter with the corresponding subsystem.
5.4 according to the positive constraint conditions of step 2, step 4.2, step 5.1 and step 5.2, the condition that the error system is positive is obtained:
when k is E [ k r ，k r +Δ r ) Time of day
When k is E [ k r +Δ r ，k r +1) time
Wherein,
5.5 obtaining the guaranteed error System L according to step 2 and step 4.2 1 Conditions for gain stabilization:
when k is E [ k r ，k r +Δ r ) Time of day
When k is E [ k r +Δ r ，k r +1) time
Wherein,
5.6 constructing a piecewise redundant positive Lyapunov function:
wherein,combining the steps 5.5 to obtain:
wherein,
from the conditions in step 5.2 and step 5.3, it is possible to obtain:
description of the communication network error System being L under the designed event triggered Filter according to step 5.6 1 The gain is stable.
Claims (1)
1. A method for estimating congestion condition event triggering filtering of a people stream dense area network is characterized by comprising the following steps:
step 1, a state space model of a network communication system is established, and the specific method is as follows:
1.1, collecting input and output data quantity of a data terminal of a traffic intensive area communication network to describe an actual system;
1.2 constructing a state space model of the communication network system;
1.3, determining that the nonlinear system meets the condition;
step 2, establishing event triggering conditions of the communication network system;
step 3, establishing an event triggering asynchronous filter model;
step 4, constructing an error system under nonlinear asynchronous communication;
step 5, designing a nonlinear asynchronous event trigger filter aiming at network congestion estimation;
the nonlinear system determined in the step 1.3 meets the conditions, and is specifically as follows:
wherein,0＜ε 1 ≤ε 2 0＜ε 3 ≤ε 4 ,0＜ε 5 ≤ε 6 ,f i (0)＝0；
the event triggering condition for establishing the communication network system in the step 2 is constructed as follows:
||e y (k)|| 1 ＞β||y(k)|| 1 ，
where beta is a given constant and satisfies beta.epsilon.0, 1),e y (k) Is a sampling error and satisfiesk∈[k l ,k l+1 )，/>y(k l ) Indicating the communication network system at event trigger time k l Output value of (2); l epsilon N + ，‖·‖ 1 Representing the 1-norm of the vector, i.e., the sum of the absolute values of all elements in the vector;
and 3, establishing an event triggering asynchronous filter model, wherein the structure form of the event triggering asynchronous filter model is as follows:
wherein x is f (k) Representing the state signal, z, of the filter f (k) Representing an estimate of the output signal z (k), A fi ,B fi ,E fi ,F fi Is a filter matrix to be designed;
and 4, constructing an error system under nonlinear asynchronous communication, and obtaining:
when k is E [ k r ,k r +Δ r ) Sometimes there is
When k is E [ k r +Δ r ,k r +1), when there is
Wherein,
the method of step 5, wherein the nonlinear asynchronous event trigger filter for network congestion estimation is designed, and the state space model of the communication system is:
x(k+1)＝A σ(k) f(x(k))+B σ(k) g(ω(k))
y(k)＝C σ(k) h(x(k))+D σ(k) l(ω(k))
z(k)＝E σ(k) p(x(k))+F σ(k) q(ω(k))
wherein x (k) = [ x ] 1 (k),x 2 (k),…,x n (k)] T ∈R n The data transmission quantity of the communication network in the region at the moment k, wherein n represents the system capacity number of the base station in the region;is an external disturbance in the operation of the communication network; y (k) ∈R m For the actual output of the data quantity received by the data terminal acquired by the sensor at the moment k, m represents the number of measuring output sensors; z (k) ∈R s The method comprises the steps of estimating and outputting the received data quantity of a base station data terminal at the moment k; nonlinear function f (x) ∈R n ,g(x)∈R m ,h(x)∈R n ,l(x)∈R m ,p(x)∈R n ,q(x)∈R m The function σ (k) is a switching signal, representing [0, ++]To a finite set s= {1,2, [ N ] N + Mapping; let σ (k) =i, i e S, then the system matrix can be denoted as a i ,B i ,C i ,D i ,E i ,F i The method comprises the steps of carrying out a first treatment on the surface of the Matrix array Respectively representing n-dimensional vectors, n-dimensional non-negative vectors, positive integers and n multiplied by n-dimensional Euclidean matrix spaces;
the specific steps of the step 5 are as follows:
5.1 event triggered asynchronous filter system matrix designed as follows:
wherein,is R n Vector (S)>Is R m Vector; 1 n N-dimensional vector representing all elements 1, < ->An n-dimensional vector representing the mu-th element as 1 and the remaining elements as 0;
5.2 design constant
0＜ε 5 ≤ε 6 ,γ＞0,λ＞1,0≤β＜1,0＜μ 1 ＜1,μ 2 > 1, if R is present n (Vector)Vector->The following inequality is caused:
for any oneThe error system is constructed to be L under the filter gain matrix and the switching rate 1 Gain stabilization;
5.3 designing the switching rate of the state space model of the system to be switched as follows:
wherein the method comprises the steps of
Wherein Γ is - (k 0 ,k),Γ + (k 0 K) represents the total time of synchronous and asynchronous operation of the filter and the switching system, τ, respectively a Indicating the residence time, delta, of the switching subsystem max Representing the maximum asynchronous time of the filter and the corresponding subsystem;
5.4 according to the positive constraint conditions of step 2, step 4.2, step 5.1 and step 5.2, the condition that the error system is positive is obtained:
when k is E [ k r ,k r +Δ r ) Time of day
When k is E [ k r +Δ r ,k r +1) time
Wherein,
5.5 obtaining the guaranteed error System L according to step 2 and step 4.2 1 Conditions for gain stabilization:
when k is E [ k r ,k r +Δ r ) Time of day
When k is E [ k r +Δ r ,k r +1) time
Wherein,
5.6 constructing a redundant positive Lyapunov function:
wherein,combining the steps 5.5 to obtain:
wherein,
from the conditions in step 5.2 and step 5.3, it is possible to obtain:
description of the communication network error System being L under the designed event triggered Filter according to step 5.6 1 The gain is stable.
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