CN111209516B - Discrete event system mode fault online diagnosis method based on Petri network diagnostor - Google Patents

Discrete event system mode fault online diagnosis method based on Petri network diagnostor Download PDF

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CN111209516B
CN111209516B CN202010011501.5A CN202010011501A CN111209516B CN 111209516 B CN111209516 B CN 111209516B CN 202010011501 A CN202010011501 A CN 202010011501A CN 111209516 B CN111209516 B CN 111209516B
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discrete event
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CN111209516A (en
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阙蔡雄
刘富春
赵锐
邓秀勤
崔洪刚
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Guangdong University of Technology
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Abstract

The utility model discloses a discrete event system mode fault on-line diagnosis method based on Petri net diagnostors, firstly, a fault identification automaton is constructed for each mode fault string according to a given mode fault set and used for identifying the mode faults, secondly, a marking state automaton is constructed according to the discrete event system and all fault identification automatons to mark the states of faults in the discrete event system, then the marking state automaton is converted into Petri net, finally, the Petri net diagnostors are constructed according to the Petri net obtained by conversion, and the mode faults of the discrete event system are diagnosed on line by using the diagnostors. The method solves the problem that the existing method is not suitable for online diagnosis of complex mode faults, and can be used for online diagnosis of S-type faults and T-type faults and also can be used for online diagnosis of traditional single fault events; the complexity is in a linear relation with the state number and the event number of the original system, so that the complexity of fault diagnosis can be reduced, and the fault diagnosis efficiency can be improved.

Description

Discrete event system mode fault online diagnosis method based on Petri network diagnostor
Technical Field
The application relates to the technical field of discrete event system fault diagnosis, in particular to a discrete event system mode fault on-line diagnosis method based on a Petri network diagnostor.
Background
The discrete event system (Discrete Event System, abbreviated as DES) is a dynamic system proposed in the 80 th century and used for modeling discrete systems as well as continuous systems after discretization, wherein the dynamic system is formed by interaction of discrete events according to a certain operation rule and causes state evolution. At present, discrete event systems have been widely used in the fields of computer integrated manufacturing systems, traffic control, computer networks, communication networks, flexible production lines, military commands, and the like. Particularly, a random discrete event system is added with a probability structure based on a classical discrete event system, so that the random characteristic of the probability of each event in an actual application system can be more accurately described, and the random discrete event system is widely applied to the fields of pattern recognition, language recognition, weather prediction, robot control and the like. At the 14 th world conference of the international automatic control union, any Chinese engineering courtyard Song Jian courtyard in the conference report classifies a discrete event system and a nonlinear system, a distributed parameter system, robust control, fault-tolerant control, self-adaption, self-correction, self-organizing system and the like as research directions with far forward; the "discrete event system" is a widely regarded research direction in the control theory world at home and abroad, and has important research significance on a series of engineering technical problems such as high-technology artificial systems (Zheng Dazhong, zhao Qianchuan. Discrete event dynamic system. Beijing: qinghai university Press, 2001).
With the rapid development of modern technology, the industrial production scale is increasingly enlarged, the automation equipment is increasingly large, the system structure is also becoming more complex, and once the system or the equipment fails, the normal operation of the system is affected, and even huge losses of life and property can be caused. Particularly in the field of industrial control, the system operation is monitored in real time, and fault detection is timely and accurately carried out on key equipment of the system so as to prevent serious or even catastrophic accidents, which is extremely important for an industrial system. Therefore, the research of fault diagnosis technology widely applied to the discrete event system of the modern industry is highly paid attention to by researchers, and is now one of research hotspots in the fields of international automation control and the like (J.Zaytoon, S.Lafortune.Overview of Fault Diagnosis Methods for Discrete Event systems.annu Reviews in Control,37 (2): 308-320, 2013).
In classical discrete event system fault diagnosis technology research, diagnosis of a single fault event is often considered, but in many applications (e.g. network intrusion detection), a series of events (called a pattern) are often required to be diagnosed. The american university of michigan' S well-known control theory expert lafortunes taught in 2006 first proposed the concept of pattern fault diagnosis (Genc S, lafortunes s.diagnosis of patterns in partially-observed discrete-event systems, proceedings of the 45th IEEE Conference on Decision and Control.IEEE,2006:422-427.) two pattern fault types, an S-pattern fault and a T-pattern fault, were studied, wherein the S-pattern fault was considered to be a fault occurring in the form of a sub-sequence in the language, and the T-pattern fault was considered to be a fault occurring in the form of a sub-string in the language. Once the mode fault diagnosis problem is proposed, the mode fault diagnosis method can be widely researched by expert scholars at home and abroad. The current mode fault diagnosis problem has been generalized to distributed discrete event systems, random discrete event systems, fuzzy discrete event systems, timed discrete event systems, etc.
The online diagnosis method proposed in the prior art is only suitable for diagnosing single fault events, and is not suitable for online diagnosis of complex mode faults, such as an S-mode fault (i.e., a fault consisting of a plurality of discrete events) and a T-mode fault (i.e., a fault consisting of a plurality of continuous events).
Disclosure of Invention
The invention aims to provide an online diagnosis method for a discrete event system mode fault based on a Petri network diagnostic device, which is used for solving the problem that the existing method is not suitable for online diagnosis of a complex mode fault.
In order to achieve the above task, the present application adopts the following technical scheme:
in a first aspect, the present application provides an online diagnosis method for a discrete event system mode fault based on a Petri net diagnostor, including:
firstly constructing a fault identification automaton for each mode fault string in a given mode fault set to identify a mode fault, secondly constructing a marking state automaton according to a discrete event system and all fault identification automatons to mark the state of faults in the discrete event system, then converting the marking state automaton into a Petri network, finally constructing a Petri network diagnostic device according to the Petri network obtained by conversion, and carrying out mode fault on-line diagnosis on the discrete event system by using the diagnostic device.
Further, the constructing a fault identification automaton for each pattern fault string according to a given pattern fault set for identifying pattern faults includes:
given mode fault set K and mode fault type thereof, construction
Figure BDA0002357307510000021
Wherein X is i ＝{0，1，2，...，||k i I is H (k) i ) Is>
Figure BDA0002357307510000022
For the initial state +.>
Figure BDA0002357307510000023
{||k i The sign state, delta i Is H (k) i ) Is a transfer function of (2); for->
Figure BDA0002357307510000031
And->
Figure BDA0002357307510000032
If the mode fault type is S type, then let:
Figure BDA0002357307510000033
if the mode fault type is T-type, then let:
Figure BDA0002357307510000034
sigma in the above formula x+1 Is expressed as enabling H (k) i ) The event that transitions from current state x to state x+1, match (x, σ) is a function that matches the event string that occurs when the system reaches current state x with the σ event.
Further, the marking the state of the fault in the discrete event system according to the discrete event system and the marking state automaton of all fault identification automaton structures comprises the following steps:
order the
Figure BDA00023573075100000313
Structure of the device
G D ＝{X D ，Σ O ，δ D ，x 0，D ，X m，D ) Wherein X is D ＝X′，X m，D ＝X′ m ，x 0，D ＝UR(x′ 0 ) For the following
Figure BDA0002357307510000035
And +.>
Figure BDA0002357307510000036
The method comprises the following steps:
Figure BDA0002357307510000037
x ', delta', X 'in G' 0 、X′ m Respectively represent the finite state space, transfer function, initial state and marked state set, G D X in (2) D 、δ D 、x 0，D 、X m，D Respectively representing the finite state space, transfer function, initial state, marked state set, UR above (x' 0 ) Refers to x' 0 Is not a significant set of arrival states.
Further, the converting the tagged state automaton into a Petri net includes:
first for each state x i ∈X D Creating a corresponding library p i E P, P for each pool i E P, if the corresponding state x i With delta D (x i Sigma) +.! Then create a transition t j E T and assigning a label l (T j ) = { σ }; let Pre (p) i ，t j ) =1 for all x l ∈δ D (x i Let Post (t) j ，p l ) =1; for the following
Figure BDA00023573075100000312
If the corresponding state x i ∈x 0，D Let M 0 (p i ) =1, otherwise M 0 (p i )＝0；
A Petri net is a six-tuple
Figure BDA0002357307510000038
Wherein P is the pool, T is the transition set,>
Figure BDA0002357307510000039
is a directed arc function connecting the library and transitions,/for the transition>
Figure BDA00023573075100000310
Is a directed arc function connecting transitions and a library,>
Figure BDA00023573075100000311
is the initial identity function, l: t2 ∑ The tag is assigned a function.
Further, the construction of the Petri net diagnostic device according to the transformed Petri net includes:
s41, let P D ＝P，T D ＝T，Pre D ＝Pre，Post D ＝Post，M 0，D ＝M 0 ，l D ＝l；
S42, p for each pool i ∈P D If the corresponding state x i ∈X D Having Γ (x) i )∩∑ o ≠∑ o Then create a transition t j ∈T D The method comprises the steps of carrying out a first treatment on the surface of the Let l D (t j )＝∑ o \Г(x i )，Pre D (p i ，t j ) =1; wherein Γ (x) = { σ: delta (x, sigma) is-! -represents an event that may occur in state x;
s43, creating transition t f Libraries N and F; let l D (t f )＝{λ}，Pre D (N，t f )＝Post D (t f F) =1; lambda is an event that can occur at any time when all libraries connected to the suppression arc have no tokens, lambda occurs, t f Occurs;
s44 for all p i ∈P D If the corresponding state x i ∈X D \X m，D In is made to be D (p i ，t f ) =1, otherwise let In D (p i ，t f ) =0; let M 0，D (N)＝1，M 0，D (F)＝0；
S45, for all undefined Pres D (p, t) and Post D (t，p)(p∈P D ，t∈T D ) Let Pre D (p，t)＝0，Post D (t，p)＝0；
S46, the Petri network diagnostic device obtained by the above
Figure BDA0002357307510000041
Defined as a binary Petri network, P D 、T D 、Pre D 、Post D 、M 0，D 、l D Directed arc functions for library, transition, connection and transition, initial identification, label assignment, in D ：P D ×{t f And {0,1} is a suppression arc function.
Further, the on-line diagnosis of the mode fault of the discrete event system by using the diagnostor comprises the following steps:
first, a diagnostic device is initialized when a discrete event system starts to run
Figure BDA0002357307510000042
Then recording the events occurring during operation of the discrete event system; after each observation of the occurrence of a new event, the corresponding transition meeting the condition occurs, the diagnostician reaches a new mark, and a judgment result M is output u (F) The method comprises the steps of carrying out a first treatment on the surface of the When M u (F) When=1, it is determined that the discrete event system has failed.
In a second aspect, the present application provides an online diagnostic device for a discrete event system mode fault based on a Petri net diagnostic, including:
the fault identification automaton construction module is used for constructing a fault identification automaton for each mode fault string according to a given mode fault set K so as to identify mode faults;
the marking state automaton construction module is used for marking the state of the fault in the discrete event system according to the discrete event system and the marking state automaton of all fault identification automaton construction;
the Petri network conversion module is used for converting the marking state automaton into a Petri network;
a diagnostician construction module for constructing a Petri net diagnostician from the converted Petri net;
and the online diagnosis module is used for carrying out online diagnosis on the mode faults of the discrete event system by utilizing the diagnostor.
In a third aspect, the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the discrete event system mode fault online diagnosis method based on the Petri net diagnostor of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the discrete event system mode fault online diagnosis method based on a Petri net diagnostor of the foregoing first aspect.
The application has the following technical characteristics:
1. the method solves the problem that the existing method is not suitable for online diagnosis of complex mode faults, and can be used for online diagnosis of S-type faults and T-type faults and can also be used for online diagnosis of traditional single fault events.
2. The method has the advantages of being simple in structure and the like, can reduce the complexity of fault diagnosis and improve the fault diagnosis efficiency.
Drawings
FIG. 1 is a schematic flow chart of a discrete event system mode fault online diagnosis method based on a Petri network diagnostor provided in an embodiment of the present application;
FIG. 2 is an automaton model constructed for a file network system in one embodiment of the present application;
FIG. 3 is a schematic diagram of H (up) constructed for a file network system;
FIG. 4 is a labeled state automaton G for file network system construction D Schematic of (2);
FIG. 5 is a state automaton G to be marked D A schematic diagram converted into a Petri net;
FIG. 6 is a diagram of a network according to Petri
Figure BDA0002357307510000051
Construction of Petri network diagnostic device>
Figure BDA0002357307510000052
Is a schematic diagram of (a).
FIG. 7 is a schematic structural diagram of a discrete event system mode fault online diagnostic device based on a Petri network diagnostic device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The application discloses a discrete event system mode fault on-line diagnosis method based on a Petri network diagnostor, wherein in the application, a discrete event system is used for determining a finite automaton G= (X, sigma, delta, X) 0 ，X m ) The representation, where X is the finite state space of the discrete event system, Σ is the set of all events occurring in the discrete event system, δ is the transfer function, X 0 Is the initial state of the discrete event system. The event set Σ can be divided into two disjoint sets Σ o Sum sigma uo Wherein is sigma o Representing a set of observable events, Σ uo Representing an unobservable set of events.
Figure BDA00023573075100000610
Is a set of marking states. An event string ω is a sequence of a finite number of events in the event set Σ, a pattern failure refers to a string of events that causes the system to deviate from normal behavior (i.e. fail), typically with k i And (3) representing. There are two types of mode faults-S-mode faults and T-mode faults, where events in an S-mode fault can beIntermittent occurs, while events in a T-mode fault must occur continuously. All mode faults k i The set of constituents is denoted K.
The technical scheme of the application is as follows: first for each pattern failure string K therein according to a given pattern failure set K i E, K constructing a fault identification automaton H (K) i ) For identifying pattern faults, and secondly identifying automata H (k) based on discrete event system G and all faults i ) Construction of a marking State automaton G D To mark the state of a fault in a discrete event system, and then to mark the state automaton G D Conversion to a Petri network, and finally constructing a Petri network diagnostic device according to the converted Petri network
Figure BDA0002357307510000061
By means of a diagnostic device->
Figure BDA0002357307510000062
And carrying out mode fault on-line diagnosis on the discrete event system. The method comprises the following specific steps:
s1, according to a given mode fault set K, each mode fault string K i E, K constructing a fault identification automaton H (K) i ) For identifying pattern faults.
Given mode fault set K and mode fault type thereof, construction
Figure BDA0002357307510000063
Wherein X is i ＝{0，1，2，...，||k i I is H (k) i ) Sigma is the set of all events occurring in the discrete event system, +.>
Figure BDA0002357307510000064
For the initial state +.>
Figure BDA0002357307510000065
For marking status (||k) i I represents a pattern failure k i Length of (k) i Number of events contained in) δ i Is H (k) i ) Is a transfer function of (a).For->
Figure BDA0002357307510000066
And->
Figure BDA0002357307510000067
If the mode fault type is S type, then let:
Figure BDA0002357307510000068
if the mode fault type is T-type, then let:
Figure BDA0002357307510000069
sigma in the above formula x+1 Is expressed as enabling H (k) i ) The event that transitions from current state x to state x+1, match (x, σ) is a function that matches the event string that occurs when the system reaches current state x with the σ event, defined as follows:
match(x，σ)＝{i：σ 1 …σ i ＝σ x-i+2 …σ x σ}
from this step, it can be seen that H (k i ) The markup language includes pattern faults k i Is a language of (c).
S2, according to the discrete event system G and all H (k i ) Construction of a marking State automaton G D To flag the status of a failure in the discrete event system.
Order the
Figure BDA00023573075100000714
Structure of the device
G D ＝(X D ，∑ o ，δ D ，x 0，D ，X m，D ) Wherein X is D ＝X′，∑ o Representing a set of observable events, X m，D ＝X′ m ，x 0，D ＝UR(x′ 0 ) For the following
Figure BDA0002357307510000071
And +.>
Figure BDA0002357307510000072
The method comprises the following steps:
Figure BDA0002357307510000073
like G, X ', delta', X 'in G' 0 、X′ m Respectively represent the finite state space, transfer function, initial state and marked state set, G D X in (2) D 、δ D 、x 0，D 、X m，D Respectively representing the finite state space, transfer function, initial state, marked state set, UR above (x' 0 ) Refers to x' 0 The set of unobservable arrival states UR (x) is defined as:
Figure BDA0002357307510000074
intuitively, the set of unobservable states UR (x) represents all the sets of states reached by state x through an unobservable path.
S3, marking the state automaton G D Conversion to Petri net
Figure BDA0002357307510000075
First for each state x i ∈X D Creating a corresponding library p i e.P. For each pool p i E P, if the corresponding state x i With delta D (x i ，σ)！(δ D (x i Sigma) +.! Representing transfer delta D (x i Sigma) at G D Defined in the specification), a transition t is created j E T and assigning a label l (T j ) = { σ }. Let Pre (p) i ，t j ) =1. For all x l ∈δ D (x i Let Post (t) j ，p l )＝1. For the following
Figure BDA0002357307510000076
If the corresponding state x i ∈x 0，D Let M 0 (p i ) =1, otherwise M 0 (p i )＝0。
In this step, a Petri net is a six-tuple
Figure BDA0002357307510000077
Wherein P is the pool, T is the transition set,>
Figure BDA0002357307510000078
is a directed arc function connecting the library and transitions,/for the transition>
Figure BDA0002357307510000079
Is a directed arc function connecting transitions and a library,>
Figure BDA00023573075100000710
is the initial identity function, l: t2 ∑ The tag is assigned a function.
S4, constructing a Petri network diagnostic device according to the converted Petri network
Figure BDA00023573075100000711
In this step, with a Petri net
Figure BDA00023573075100000712
Similarly, is->
Figure BDA00023573075100000713
P in (3) D 、T D 、Pre D 、Post D 、M 0，D 、l D The method comprises the steps of respectively obtaining a directed arc function of a library set, a transition set, a connection library and transition, a directed arc function of a connection transition and library, an initial identification function and a label distribution function. In (In) D ：P D ×{t f }→{0,1} is a suppression arc function.
The method specifically comprises the following steps:
s41, let P D ＝P，T D ＝T，Pre D ＝Pre，Post D ＝Post，M 0，D ＝M 0 ，l D ＝l
S42, p for each pool i ∈P D If the corresponding state x i ∈X D Having Γ (x) i )∩∑ o ≠∑ o Then create a transition t j ∈T D . Let l D (t j )＝∑ o \Γ(x i )，Pre D (p i ，t j ) =1. Wherein Γ (x) = { σ: delta (x, sigma) is-! And represents an event that may occur in state x.
S43, creating transition t f Libraries N and F. Let l D (t f )＝{λ}，Pre D (N，t f )＝Post D (t f F) =1; lambda is an event that can occur at any time when all libraries connected to the suppression arc have no tokens, lambda occurs, t f Which occurs.
S44 for all p i ∈P D If the corresponding state x i ∈X D \X m，D In is made to be D (p i ，t f ) =1, otherwise let In D (p i ，t f ) =0. Let M 0，D (N)＝1，M 0，D (F)＝0。
S45, for all undefined Pres D (p, t) and Post D (t，p)(p∈P D ，t∈T D ) Let Pre D (p，t)＝0，Post D (t，p)＝0。
S46, the Petri network diagnostic device obtained by the above
Figure BDA0002357307510000081
Defined as a binary Petri net.
Given a binary Petri network, the current mark is set as
Figure BDA0002357307510000082
When transition t i After this, the Petri network reaches a new identification +.>
Figure BDA0002357307510000083
The calculation formula is as follows:
Figure BDA0002357307510000084
above mentioned
Figure BDA0002357307510000085
Identification (marking) called Petri net,>
Figure BDA0002357307510000086
similarly.
S5, utilizing Petri network diagnostic device
Figure BDA0002357307510000087
And carrying out mode fault on-line diagnosis on the discrete event system.
First, a diagnostic device is initialized when a discrete event system starts to run
Figure BDA0002357307510000088
Then recording the events occurring during operation of the discrete event system; after each observation of the occurrence of a new event, a corresponding conditional transition (i.e. the transition that contains the event in the tag and is allowed) occurs, diagnostician->
Figure BDA0002357307510000089
A new mark is reached, and a judging result M is output u (F) The method comprises the steps of carrying out a first treatment on the surface of the When M u (F) When=1, it can be determined that the discrete event system has failed, specifically, see algorithm 1 below.
Algorithm 1 (Petri based network diagnostic device)
Figure BDA00023573075100000810
Mode fault on-line diagnosis algorithm of (2)
Input:
Figure BDA00023573075100000811
/>
and (3) outputting: m is M u (F)
Step 1, initializing the system to let u=σ=σ' =ε, P In ＝{p i ∈P D ：In D (p i ，t f ) =1 } (above u records the discrete event system history event string, σ records the last observed event, σ' records the last event, all of which are null events epsilon, P at the beginning of diagnosis In All the inhibition of arc and t are contained in the collection f Connected houses
Step 2, recording the event sigma of the system, and jumping to step 3 when sigma is equal to sigma'.
Step 3, for
Figure BDA0002357307510000091
If->
Figure BDA0002357307510000092
Then t i Which occurs. (when a new event is observed, i.e., σ+.σ', then the event (σ ε l) is included in the tag D (t i ) Is allowed (enabled)
Figure BDA0002357307510000093
The new identification of the Petri network diagnostic device after the transition occurs (desired) is calculated in step 4, and the calculation formula is the formula in S46
Step 4, let u=uσ, for
Figure BDA0002357307510000094
Calculate M u (p i ) (the above input warehouse set I (t) i )＝{p i ∈P D ：Pre D (p i ，t i ) > 0}, output pool O (t i )＝{p i ∈P D ：Post D (t i ，p i )＞0})
Step 5, if for
Figure BDA0002357307510000095
Are all provided with M u (p i ) =0, then M u (F) =1, otherwise M u (F)＝0。
Step 6, let σ' =σ, if M u (F) =1, then the algorithm ends, otherwise it jumps to step 2.
Complexity analysis:
given a discrete event system g= (X, Σ, δ, X) 0 ，X m ) One mode failure set k= { K 1 ，k 2 ，...，k r Let |x|=n, |Σ|=m, |k | i ||＝l i (1≤i≤r)，
Figure BDA0002357307510000096
The number of states of G' is in the worst case:
Figure BDA0002357307510000097
the number of transitions is in the worst case:
Figure BDA0002357307510000098
G D the number of states and transitions of (2) is the same as G'. In the worst case, petri network diagnostic device
Figure BDA0002357307510000099
The number of the library is the same as the number of the inhibition arcs, and is as follows: p D I= |x' |+2=n (λ+r) +2. The transition number is: i T D |=nm (λ+r) +n (λ+r) +1=n (m+1) (λ+r) +1. The directional arc number is as follows: n (2m+1) (λ+r) +2.
From the above analysis, structure G D Is of the complexity O (lambda nm), G will be D Complexity of conversion to Petri net and construction G D Is of the same complexity, constructs a diagnostic device
Figure BDA0002357307510000101
The complexity of (a) is O (lambda nm) and has a linear relation with the state number and event number of the original system G.
In a file network system, a user may perform basic operations such as logging in, reading and writing files, deleting files, executing programs, and the like. Modeling the system, an automaton was constructed as shown in fig. 2, in which there were 12 states q= {0,1, …,11}, and the event set Σ= { u, a, r, w, e, p }, in which the specific meaning of each event is shown in table 1.
Table 1 events and their symbolic representations
Event(s) (symbol)
Ordinary user login u
Administrator login a
Reading a list of files r
Writing files w
Execution file e
Obtaining super user rights p
In general, operations such as obtaining the super-user authority and executing the program can be completed only by an administrator, but in some cases, such as network intrusion, when an intruder obtains the super-user authority through an illegal way, the above operations may be illegally utilized by the intruder, and thus a diagnosis needs to be made on the system to determine whether the illegal operation is suffered. As can be seen from fig. 7, the mode fault in the system is an S-mode fault, and defines k= { up } (i.e. the super user permission is obtained after the normal user logs in), Σ o ＝{u，a，r，e}，∑ uo = { p, w }, H (up) is first constructed as shown in fig. 3 according to the above procedure, then G is constructed D As shown in fig. 4. Will G D Conversion to Petri net
Figure BDA0002357307510000102
As shown in fig. 5. Finally according to Petri net->
Figure BDA0002357307510000103
Construction of Petri network diagnostic device>
Figure BDA0002357307510000104
As shown in fig. 6.
Assuming at some point that the system is suffering a network intrusion, the system diagnostic procedure is shown in Table 2, with the recorded system operational event string being urr.
Table 2 network system operation log and diagnosis result
Figure BDA0002357307510000105
Figure BDA0002357307510000111
As can be seen from the above table, when the system records that the r event occurs for the second time, the diagnostic device has diagnosed that the system is suffering from network intrusion, i.e. has failed, and the system administrator can take further measures according to the diagnosis result to prevent the system from suffering greater damage.
According to another aspect of the present application, there is provided an online diagnosis device 1 for discrete event system mode fault based on Petri net diagnostor, as shown in fig. 7, comprising:
a fault identification automaton construction module 11, configured to construct a fault identification automaton for each mode fault string according to a given mode fault set K to identify mode faults;
a marking state automaton construction module 12 for marking a state of a fault in the discrete event system according to the discrete event system and all fault identification automaton construction marking state automata;
the Petri net conversion module 13 is used for converting the marking state automaton into a Petri net;
a diagnostician construction module 14 for constructing a Petri net diagnostician from the converted Petri net;
an on-line diagnostic module 15 for on-line diagnosing the mode fault of the discrete event system by using the diagnostor.
It should be noted that the specific implementation process and the related explanation of each of the above modules 11 to 15 correspond to S1 to S5 in the foregoing method embodiment, and are not repeated herein.
Referring to fig. 8, the embodiment of the present application further provides a terminal device 2, where the terminal device 2 may be a computer or a server; comprising a memory 22, a processor 21 and a computer program 23 stored in the memory 22 and executable on the processor, the processor 21, when executing the computer program 23, implements the steps of the above-described Petri network diagnostor based discrete event system mode fault on-line diagnosis method, e.g. S1 to S5 as shown in FIG. 1.
The computer program 23 may also be split into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to complete the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions describing the execution of the computer program 23 in the terminal device 2, for example, the computer program 23 may be divided into a fault identification automaton construction module, a marker state automaton construction module, a Petri net conversion module, a diagnostor construction module, and an on-line diagnosis module, and the functions of the respective modules are referred to in the foregoing apparatuses and will not be described again.
Implementations of the present application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described Petri net diagnostician-based discrete event system mode fault online diagnosis method, e.g., S1 to S5 shown in fig. 1.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (5)

1. The discrete event system mode fault on-line diagnosis method based on the Petri network diagnostor is characterized by comprising the following steps of:
firstly constructing a fault identification automaton for each mode fault string in a given mode fault set to identify a mode fault, secondly constructing a marking state automaton according to a discrete event system and all fault identification automatons to mark the state of faults in the discrete event system, then converting the marking state automaton into a Petri network, finally constructing a Petri network diagnostic device according to the Petri network obtained by conversion, and carrying out mode fault on-line diagnosis on the discrete event system by using the diagnostic device;
the constructing a fault identification automaton for each pattern fault string according to a given pattern fault set for identifying pattern faults includes:
given mode fault set K and mode fault type thereof, construction
Figure FDA0004088833020000011
Wherein X is i ＝{0，1，2，...，||k i I is H (k) i ) Is>
Figure FDA0004088833020000012
For the initial state +.>
Figure FDA0004088833020000013
For marking state delta i Is H (k) i ) Is a transfer function of (2); for->
Figure FDA0004088833020000014
And->
Figure FDA0004088833020000015
If the mode fault type is S type, then let:
Figure FDA0004088833020000016
if the mode fault type is T-type, then let:
Figure FDA0004088833020000017
sigma in the above formula x+1 Is expressed as enabling H (k) i ) An event transitioning from current state x to state x+1, match (x, σ) is a function used to match the event string that occurs when the system reaches current state x with the σ event;
the marking the state of the fault in the discrete event system according to the discrete event system and the marking state automaton of all fault identification automaton structures comprises the following steps:
order the
Figure FDA0004088833020000018
Structure G D ＝(X D ，∑ o ，δ D ，x 0，D ，X m，D ) Wherein X is D ＝X′，X m，D ＝X′ m ，x 0，D ＝UR(x′ 0 ) For->
Figure FDA0004088833020000019
And +.>
Figure FDA00040888330200000110
The method comprises the following steps:
Figure FDA00040888330200000111
x ', delta', X 'in G' 0 、X′ m Respectively represent the finite state space, transfer function, initial state and marked state set, G D X in (2) D 、δ D 、x 0，D 、X m，D Respectively representing the finite state space, transfer function, initial state, marked state set, UR above (x' 0 ) Refers to x' 0 Is not a significant set of arrival states;
the conversion of the tagged state automaton into a Petri net comprises:
first for each state x i ∈X D Creating a corresponding library p i E P, P for each pool i E P, if the corresponding state x i With delta D (x i Sigma) +.! Then create a transition t j E T and assigning a label l (T j ) = { σ }; let Pre (p) i ，t j ) =1 for all x l ∈δ D (x i Let Post (t) j ，p l ) =1; for the following
Figure FDA0004088833020000021
If the corresponding state x i ∈x 0，D Let M 0 (p i ) =1, otherwise M 0 (p i )＝0；
A Petri net is a six-tuple
Figure FDA0004088833020000022
Where P is the library set, T is the transition set, pre: />
Figure FDA0004088833020000023
Is a directed arc function connecting libraries and transitions, post: />
Figure FDA0004088833020000024
Is a directed arc function connecting the transition and the library, M 0 ：/>
Figure FDA0004088833020000025
Is the initial identity function, l: t2 ∑ Assigning a function to the tag; />
The construction of the Petri network diagnostic device according to the converted Petri network comprises the following steps:
s41, let P D ＝P，T D ＝T，Pre D ＝Pre，Post D ＝Post，M 0，D ＝M 0 ，l D ＝l；
S42, p for each pool i ∈P D If the corresponding state x i ∈X D Having Γ (x) i )∩∑ o ≠∑ o Then create a transition t j ∈T D The method comprises the steps of carrying out a first treatment on the surface of the Let l D (t j )＝∑ o /Γ(x i )，Pre D (p i ，t j ) =1; wherein Γ (x) = { σ: delta (x, sigma) is-! -represents an event that may occur in state x;
s43, creating transition t f Libraries N and F; let l D (t f )＝{λ}，Pre D (N，t f )＝Post D (t f F) =1; lambda is an event that can occur at any time when all libraries connected to the suppression arc have no tokens, lambda occurs, t f Occurs;
s44 for all p i ∈P D If the corresponding state x i ∈X D /X m，D In is made to be D (p i ，t f ) =1, otherwise let In D (p i ，t f ) =0; let M 0，D (N)＝1，M 0，D (F)＝0；
S45, for all undefined Pres D (p, t) and Post D (t，p)(p∈P D ，t∈T D ) Let Pre D (p，t)＝0，Post D (t，p)＝0；
S46, the Petri network diagnostic device obtained by the above
Figure FDA0004088833020000026
Defined as a binary Petri network, P D 、T D 、Pre D 、Post D 、M 0，D 、l D Directed arc functions for library, transition, connection and transition, initial identification, label assignment, in D ：P D ×{t f And {0,1} is a suppression arc function.
2. The on-line diagnosis method for the mode fault of the discrete event system based on the Petri net diagnostor according to claim 1, wherein the on-line diagnosis for the mode fault of the discrete event system by using the diagnostor comprises the following steps:
first, a diagnostic device is initialized when a discrete event system starts to run
Figure FDA0004088833020000027
Then recording the events occurring during operation of the discrete event system; after each observation of the occurrence of a new event, the corresponding transition meeting the condition occurs, the diagnostician reaches a new mark, and a judgment result M is output u (F) The method comprises the steps of carrying out a first treatment on the surface of the When M u (F) When=1, it is determined that the discrete event system has failed.
3. An on-line diagnosis device for a discrete event system mode fault based on a Petri net diagnostor, which is characterized by comprising:
the fault identification automaton construction module is used for constructing a fault identification automaton for each mode fault string according to a given mode fault set K so as to identify mode faults;
the marking state automaton construction module is used for marking the state of the fault in the discrete event system according to the discrete event system and the marking state automaton of all fault identification automaton construction;
the Petri network conversion module is used for converting the marking state automaton into a Petri network;
a diagnostician construction module for constructing a Petri net diagnostician from the converted Petri net;
the on-line diagnosis module is used for carrying out on-line diagnosis of mode faults on the discrete event system by utilizing the diagnostor;
the constructing a fault identification automaton for each pattern fault string according to a given pattern fault set for identifying pattern faults includes:
given mode fault set K and mode fault type thereof, construction
Figure FDA0004088833020000031
Wherein X is i ＝{0，1，2，...，||k i I is H (k) i ) Is>
Figure FDA0004088833020000032
For the initial state +.>
Figure FDA0004088833020000033
For marking state delta i Is H (k) i ) Is a transfer function of (2); for->
Figure FDA0004088833020000034
And->
Figure FDA0004088833020000035
If the mode fault type is S type, then let:
Figure FDA0004088833020000036
if the mode fault type is T-type, then let:
Figure FDA0004088833020000037
sigma in the above formula x+1 Is expressed as enabling H (k) i ) An event transitioning from current state x to state x+1, match (x, σ) is a function used to match the event string that occurs when the system reaches current state x with the σ event;
the marking the state of the fault in the discrete event system according to the discrete event system and the marking state automaton of all fault identification automaton structures comprises the following steps:
order the
Figure FDA0004088833020000038
Structure G D ＝(X D ，∑ o ，δ D ，x 0，D ，X m，D ) Wherein X is D ＝X′，X m，D ＝X′ m ，x 0，D ＝UR(x′ 0 ) For->
Figure FDA0004088833020000039
And +.>
Figure FDA00040888330200000310
The method comprises the following steps:
Figure FDA00040888330200000311
x ', delta', X 'in G' 0 、X′ m Respectively represent the finite state space, transfer function, initial state and marked state set, G D X in (2) D 、δ D 、x 0，D 、X m，D Respectively representing the finite state space, transfer function, initial state, marked state set, UR above (x' 0 ) Refers to x' 0 Is not a significant set of arrival states;
the conversion of the tagged state automaton into a Petri net comprises:
first for each state x i ∈X D Creating a corresponding library p i E P, P for each pool i E P, if the corresponding state x i With delta D (x i Sigma) +.! Then create a transition t j E T and assigning a label l (T j ) = { σ }; let Pre (p) i ，t j ) =1 for all x l ∈δ D (x i Let Post (t) j ，p l ) =1; for the following
Figure FDA0004088833020000041
If the corresponding state x i ∈x 0，D Let M 0 (p i ) =1, otherwise M 0 (p i )＝0；
A Petri net is a six-tuple
Figure FDA0004088833020000042
Where P is the library set, T is the transition set, pre: />
Figure FDA0004088833020000043
Is a directed arc function connecting libraries and transitions, post: />
Figure FDA0004088833020000044
Is a directed arc function connecting the transition and the library, M 0 ：/>
Figure FDA0004088833020000045
Is the initial identity function, l: t2 ∑ Assigning a function to the tag;
the construction of the Petri network diagnostic device according to the converted Petri network comprises the following steps:
s41, let P D ＝P，T D ＝T，Pre D ＝Pre，Post D ＝Post，M 0，D ＝M 0 ，l D ＝l；
S42, p for each pool i ∈P D If the corresponding state x i ∈X D Having Γ (x) i )∩∑ o ≠∑ o Then create a transition t j ∈T D The method comprises the steps of carrying out a first treatment on the surface of the Let l D (t j )＝∑ o /Γ(x i )，Pre D (p i ，t j ) =1; wherein Γ (x) = { σ: delta (x, sigma) is-! -represents an event that may occur in state x;
s43, creating transition t f Library N andf, performing the process; let l D (tf)＝{λ}，Pre D (N，t f )＝Post D (t f F) =1; lambda is an event that can occur at any time when all libraries connected to the suppression arc have no tokens, lambda occurs, t f Occurs;
s44 for all p i ∈P D If the corresponding state x i ∈X D /X m，D In is made to be D (p i ，t f ) =1, otherwise let In D (p i ，t f ) =0; let M 0，D (N)＝1，M 0，D (F)＝0；
S45, for all undefined Pres D (p, t) and Past D (t，p)(p∈P D ，t∈T D ) Let Pre D (p，t)＝0，Past D (t，p)＝0；
S46, the Petri network diagnostic device obtained by the above
Figure FDA0004088833020000046
Defined as a binary Petri network, P D 、T D 、Pre D 、Post D 、M 0，D 、l D Directed arc functions for library, transition, connection and transition, initial identification, label assignment, in D ：P D ×{t f And {0,1} is a suppression arc function.
4. Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 2 when the computer program is executed.
5. A computer-readable storage medium, in which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 2.
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