CN114021314B - System electromagnetic vulnerability assessment method based on dynamic Bayesian network - Google Patents
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Abstract
The invention discloses a system electromagnetic vulnerability assessment method based on a dynamic Bayesian network, and belongs to the field of electromagnetic safety. 1. Establishing an interaction sequence diagram of electromagnetic pulses and an electronic system according to an external electromagnetic pulse environment of the electronic information system; 2. constructing a hierarchical static Bayesian network; 3. t time nodes are selected according to the time interval for electromagnetic vulnerability assessment, and under the influence of electromagnetic pulse, the failure probability of the bottom node is analyzed on each time node and used as the observation state of the bottom node; 4. constructing a discrete dynamic Bayesian network according to the time sequence of the T time nodes; 5. the discrete dynamic Bayesian network is split into a plurality of dynamic Bayesian networks with two-layer structures, a forward operator and a backward operator are defined according to the observed states of the bottom nodes, the states of the top hidden nodes are inferred, the failure probability of the system is obtained, and the electromagnetic vulnerability of dynamically describing the whole or part of the electronic information system at different time is realized.
Description
Technical Field
The invention relates to the field of electromagnetic safety, in particular to a system electromagnetic vulnerability assessment method based on a dynamic Bayesian network.
Background
Electromagnetic pulses have a great impact on electronic information systems, including man-made electromagnetic pulse interference and electromagnetic pulses present in natural environments such as lightning, which can cause damage to electronic information systems to varying degrees, e.g., electromagnetic pulses generated by nuclear bomb explosions can produce violent impacts on electronic devices and systems in the thousands of kilometers range around. In addition to High-altitude nuclear explosion electromagnetic pulses (High-altitude Electromagnetic Pulse, HEMP), high-power microwaves (High Power Microwave, HPM), ultra-Wideband electromagnetic pulses (UWB), lightning electromagnetic pulses (Light Electromagnetic Pulse, LEMP) and the like can cause great damage to electronic information systems, and particularly in military opposition, electromagnetic pulses can greatly reduce the stability and reliability of the system. Therefore, it is necessary to evaluate the electromagnetic vulnerability of electronic information systems.
The existing electromagnetic vulnerability assessment method based on the static Bayesian network can only describe the electromagnetic vulnerability of the electronic information system on one static time node, and cannot dynamically describe the electromagnetic vulnerability of the electronic information system in whole or part exposed to the external environment at different times.
Disclosure of Invention
In order to realize electromagnetic vulnerability assessment of the electronic information system on different time nodes, the invention extends the time of the traditional static Bayesian network and is used for dynamically describing the electromagnetic vulnerability of the electronic information system in whole or part exposed to the external environment at different times.
The invention adopts the following technical scheme:
A system electromagnetic vulnerability assessment method based on a dynamic Bayesian network comprises the following steps:
Step 1: establishing an interaction sequence diagram of electromagnetic pulse and the electronic system according to the external electromagnetic pulse environment of the electronic information system, and determining current or voltage excitation of all subsystems and unit components in the electronic information system under the action of the electromagnetic pulse;
step 2: constructing a hierarchical static Bayesian network from the bottom layer unit to the whole system for the subsystem and the unit components excited by current or voltage; in a hierarchical static bayesian network, the conditional probability that each node passes to its downstream nodes constitutes a conditional probability table;
Step 3: acquiring a time interval in which electromagnetic vulnerability assessment is required, selecting T time nodes in the time interval, wherein each time node corresponds to a layered static Bayesian network with the same structure; under the influence of electromagnetic pulse, performing electromagnetic vulnerability analysis on the bottom nodes of the layered static Bayesian network on each time node to obtain failure probability of the bottom nodes, and taking the failure probability as an observation state of the bottom nodes;
Step 4: according to the time sequence of the T time nodes, taking the hierarchical static Bayesian network corresponding to each time node as a time slice, and connecting the former time slice with the corresponding layer node of the latter time slice to form a discrete dynamic Bayesian network;
step 5: splitting the discrete dynamic Bayesian network into a plurality of dynamic Bayesian networks with two-layer structures;
In each split time slice, taking a bottom layer node as an observation node, obtaining the state of the bottom layer node through observation, taking a top layer node as a hidden node, judging the state of the top layer node through the state of the observation node and a conditional probability table, wherein a plurality of observation nodes exist in each split time slice and only one hidden node exists; and obtaining the joint distribution probability of the hidden node corresponding to the split time slice in a certain combination state according to the state of the observation node on the split time slice.
The invention promotes electromagnetic vulnerability assessment modeling from static to dynamic, and realizes dynamic description of electromagnetic vulnerability of the whole or part of the electronic information system exposed to external environment at different times.
Drawings
FIG. 1 is a static hierarchical Bayesian network structure in accordance with an embodiment of the present invention;
FIG. 2 is a dynamic Bayesian network structure in accordance with an embodiment of the present invention;
FIG. 3 is a diagram illustrating a dynamic Bayesian network structure split into two layers according to an embodiment of the present invention;
FIG. 4 is a flow chart of a forward operator and backward operator calculation process shown in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an electromechanical transmission system stimulated by current or voltage according to an embodiment of the present invention;
FIG. 6 is a static Bayesian network corresponding to the system of FIG. 6 in accordance with an embodiment of the present invention;
FIG. 7 is a dynamic Bayesian network corresponding to the system of FIG. 6 in accordance with an embodiment of the present invention;
FIG. 8 is a plot of fault rate function for the entire electromechanical transmission system of FIG. 6, as shown in an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings.
The invention extends the traditional static Bayesian network in time and is used for dynamically evaluating the electromagnetic vulnerability of the electronic information system.
1. Firstly, selecting an operation time interval of an electronic information system needing electromagnetic vulnerability assessment, and selecting T time nodes in the interval for next analysis; in this embodiment, T time nodes are obtained in an equipartition manner, that is, T time nodes are obtained from an equal interval in the running time interval.
2. And respectively establishing a hierarchical static Bayesian network for the selected T time nodes.
Before a static bayesian network is established, functional logic relationships between all unit components, each level of subsystems, of the electronic information system need to be clarified. In this embodiment, an interaction sequence diagram of electromagnetic pulses and the electronic system is established according to an external electromagnetic pulse environment of the electronic information system, where the interaction sequence diagram represents a functional logic relationship. The current or voltage Excitation (EMS) of each subsystem and unit component in the electronic information system under the action of electromagnetic pulse is further determined through experimental measurement or theoretical calculation.
Taking a single time node as an example, a hierarchical static Bayesian network from the underlying unit to the whole system is constructed for the current or voltage stimulated subsystem and unit components. As shown in fig. 1, wherein the first layer X (1, 1) node represents the entire system, the second layer X (2, i) node represents the ith subsystem, and the third layer X (3, j) node represents the jth unit cell in the system; performing directed loop-free connection between nodes according to a functional logic relationship, wherein each X (3, j) node is connected with an X (2, i) node to which the X (3, j) node belongs, and the jth unit component in the system belongs to an ith subsystem; each X (2, i) node is connected with an X (1, 1) node, and the X (2, i) nodes with functional relations are connected, and the X (3, j) nodes with functional relations belonging to the same subsystem are connected.
Wherein X (2, i) ∈ { X (2, 1), X (2, 2), …, X (2, i), …, X (2, m) }, m represents the number of current or voltage activated subsystems in the system; x (3, j) e { X (3, 1), X (3, 2), …, X (3, j), …, X (3, n) }, n representing the number of all cell units in the system that are energized by a current or voltage, one for each subsystem.
The hierarchical static bayesian network structure established above does not change over time, so the same network structure can be used on all time nodes (T time nodes in the above). In a static bayesian network, the elements, subsystems and systems are all abstracted as network nodes, a directed loop-free delivery network is formed according to their functional logic relationships and composition relationships, and the conditional probabilities of a node delivering to its downstream nodes form a Conditional Probability Table (CPT).
3. Under the influence of a certain electromagnetic pulse, electromagnetic vulnerability analysis is carried out on bottom component units in the system on each time node, and the failure probability of the bottom node is obtained and is used as the observation state of the bottom node.
In this embodiment, the failure probability may be estimated according to a conventional technical means in the art by combining technologies such as electromagnetic coupling analysis and electromagnetic damage analysis technologies, and the failure probability, that is, an observation state, of the bottom root node X (3,j) t of the hierarchical static bayesian network in different time nodes under the influence of a certain electromagnetic pulse; wherein X (3,j) t represents a jth unit component corresponding to a t-th time node.
4. The static Bayesian network is generalized to the dynamic Bayesian network.
And according to the time sequence of the T time nodes, taking the hierarchical static Bayesian network corresponding to each time node as a time slice, and connecting the former time slice with the bottom node of the latter time slice to form the discrete dynamic Bayesian network shown in figure 2.
The discrete dynamic bayesian network conforms to the markov assumption, and the nodes in each time slice are only related to the time slice and the nodes in the previous time slice, and are not related to the nodes in other time slices. Because the root node on each time slice has a dependency relationship with the corresponding root node of the previous time slice, the conditional probability table of the node changes although the network structure of each time slice does not change.
Step 5: the discrete dynamic Bayesian network is split into a plurality of dynamic Bayesian networks with two-layer structures.
In order to facilitate the reasoning calculation, the discrete dynamic Bayesian network is split into a plurality of dynamic Bayesian networks with two-layer structures, so as to form the dynamic Bayesian network shown in fig. 3.
Because the discrete dynamic Bayesian network shown in FIG. 2 is of a three-layer structure, when in splitting, the second layer node is used as the top layer node, the third layer node is used as the bottom layer node, and the observation state of the top layer node is obtained according to the observation state of the bottom layer node (namely, the observation state of the second layer node is obtained); and then taking the second layer node as a bottom layer node, taking the first layer node as a top layer node, and obtaining the observation state of the top layer node according to the observation state of the bottom layer node (namely, obtaining the observation state of the first layer node).
Specifically, in each split time slice, the bottom layer node is used as an observation node, the state of the bottom layer node can be obtained through observation, the top layer node is used as a hidden node, the state of the top layer node is required to be judged through the state and the conditional probability table of the observation node, and a plurality of observation nodes and only one hidden node exist in each split time slice; and obtaining the joint distribution probability of the hidden node corresponding to the split time slice in a certain combination state according to the state of the observation node on the split time slice.
In this embodiment, the calculation of the joint distribution probability that the hidden node is in a certain combination state is implemented by defining a forward operator and a backward operator. The method comprises the following steps:
defining a forward operator alpha t (i), which is the conditional probability that the hidden node on the t time slice is in the i state under the condition of the state combination observed by the observation nodes on the 1 st to t time slices;
Defining a backward operator beta t (i), which is the conditional probability that the hidden node on the T-th time slice is in a state combination observed from the t+1th to the observation node on the T-th time slice under the i-state condition;
The reasoning result is marked as gamma t (i) and represents the conditional probability that the hidden node on the T time slice is in the i state under the condition of the state combination observed by the observation nodes on the 1 st to the T time slice;
The forward operator, the backward operator and the reasoning result are respectively expressed as follows:
wherein, P (-) represents a conditional probability, Representing hidden nodes on the t-th time slice,/>Representing observation nodes on time slice 1,/>Representing observation nodes on the t-th time slice,/>Representing the observation node on the T-th time slice, wherein eta is a normalization operator.
As shown in fig. 4, the forward operator and the backward operator are initialized and iterated: network initialization, the first previous operator is iterated by the forward operator alpha t (i) of the first time slice forward operator alpha 1 (i) interval slice, and the iteration is ended when t=t; then the backward operator beta t (i) of the t-th time slice is calculated by the backward operator, the reasoning result gamma t (i) can be calculated by the forward operator and the backward operator, and the process iterates until the first time slice is returned.
In one implementation of the present invention, a static Bayesian network is established for an electro-mechanical transmission system that is energized by a current or voltage as shown in FIG. 5. The electromechanical transmission system is divided into three stages according to the structure of the electromechanical transmission system, wherein the first layer is the whole system structure, and the second layer is divided into two subsystems: the third layer is divided into five unit components: a power supply, a logic power supply, a DC motor, an H bridge and a pulse width modulation controller.
A static bayesian network is established as shown in fig. 6, wherein X (3,1) represents a logic power supply, X (3,2) represents a power supply, X (3,3) represents a DC motor, X (3,4) represents an H-bridge, X (3,5) represents a pulse width modulation controller, X (2,1) represents a power supply subsystem, X (2,2) represents a servo subsystem, and X (1,1) represents the entire electromechanical transmission system.
The static bayesian network is generalized to the dynamic bayesian network as shown in fig. 7. And carrying out reasoning calculation on the dynamic Bayesian network to obtain a fault rate function curve of the whole electromechanical transmission system, as shown in figure 8.
The foregoing list is only illustrative of specific embodiments of the invention. Obviously, the invention is not limited to the above embodiments, but many variations are possible. All modifications directly derived or suggested to one skilled in the art from the present disclosure should be considered as being within the scope of the present invention.
Claims (5)
1. The system electromagnetic vulnerability assessment method based on the dynamic Bayesian network is characterized by comprising the following steps of:
Step 1: establishing an interaction sequence diagram of electromagnetic pulse and the electronic system according to the external electromagnetic pulse environment of the electronic information system, and determining current or voltage excitation of all subsystems and unit components in the electronic information system under the action of the electromagnetic pulse;
step 2: constructing a hierarchical static Bayesian network from the bottom layer unit to the whole system for the subsystem and the unit components excited by current or voltage; in a hierarchical static bayesian network, the conditional probability that each node passes to its downstream nodes constitutes a conditional probability table;
Step 3: acquiring a time interval in which electromagnetic vulnerability assessment is required, selecting T time nodes in the time interval, wherein each time node corresponds to a layered static Bayesian network with the same structure; under the influence of electromagnetic pulse, performing electromagnetic vulnerability analysis on the bottom nodes of the layered static Bayesian network on each time node to obtain failure probability of the bottom nodes, and taking the failure probability as an observation state of the bottom nodes;
step 4: according to the time sequence of the T time nodes, taking the hierarchical static Bayesian network corresponding to each time node as a time slice, and connecting the nodes of the former time slice and the latter time slice to form a discrete dynamic Bayesian network;
step 5: splitting the discrete dynamic Bayesian network into a plurality of dynamic Bayesian networks with two-layer structures;
In each split time slice, taking a bottom layer node as an observation node, obtaining the state of the bottom layer node through observation, taking a top layer node as a hidden node, judging the state of the top layer node through the state of the observation node and a conditional probability table, wherein a plurality of observation nodes exist in each split time slice and only one hidden node exists; and obtaining the joint distribution probability of the hidden node corresponding to the split time slice in a certain combination state according to the state of the observation node on the split time slice.
2. The method for evaluating electromagnetic vulnerability of a system based on a dynamic bayesian network according to claim 1, wherein the method for constructing the hierarchical static bayesian network in step 2 is as follows:
In a hierarchical static Bayesian network, a first layer X (1, 1) node represents the entire system, a second layer X (2, i) node represents an ith subsystem, and a third layer X (3, j) node represents a jth unit component in the system; performing directed loop-free connection between nodes according to a functional logic relationship, wherein each X (3, j) node is connected with an X (2, i) node to which the X (3, j) node belongs, and the jth unit component in the system belongs to an ith subsystem; each X (2, i) node is connected with an X (1, 1) node, and the X (2, i) nodes with functional relation are connected;
X (2, i) ε { X (2, 1), X (2, 2), …, X (2, i), …, X (2, m) }, m represents the number of current or voltage activated subsystems in the system; x (3, j) e { X (3, 1), X (3, 2), …, X (3, j), …, X (3, n) }, n representing the number of all cell units in the system that are energized by a current or voltage, one for each subsystem.
3. The method for electromagnetic vulnerability assessment of system based on dynamic Bayesian network of claim 1, wherein the discrete dynamic Bayesian network conforms to Markov assumption, and the nodes in each time slice are related to the nodes in the time slice and the previous time slice only, and are not related to the nodes in other time slices.
4. A method for evaluating electromagnetic vulnerability of a system based on a dynamic bayesian network according to claim 3, wherein the joint distribution probability calculation process of the hidden node in a certain combination state in step 5 is as follows:
defining a forward operator alpha t (i), which is the conditional probability that the hidden node on the t time slice is in the i state under the condition of the state combination observed by the observation nodes on the 1 st to t time slices;
Defining a backward operator beta t (i), which is the conditional probability that the hidden node on the T-th time slice is in a state combination observed from the t+1th to the observation node on the T-th time slice under the i-state condition;
The reasoning result is marked as gamma t (i) and represents the conditional probability that the hidden node on the T time slice is in the i state under the condition of the state combination observed by the observation nodes on the 1 st to the T time slice;
The forward operator, the backward operator and the reasoning result are respectively expressed as follows:
wherein, P (-) represents a conditional probability, Representing hidden nodes on the t-th time slice,/>Representing observation nodes on time slice 1,/>Representing observation nodes on the t-th time slice,/>Representing an observation node on a T-th time slice, wherein eta is a normalization operator;
the forward operator and the backward operator are initialized and iterated:
network initialization, the first previous operator calculates the forward operator alpha t (i) of the T-th time slice from the forward operator alpha t (1) of the first time slice, and the iteration is ended when t=t; then the backward operator beta t (i) of the t-th time slice is calculated by the backward operator, the reasoning result gamma t (i) can be calculated by the forward operator and the backward operator, and the process iterates until the first time slice is returned.
5. The method for evaluating electromagnetic vulnerability of system based on dynamic bayesian network according to claim 3, wherein the method for splitting dynamic bayesian network of multiple two-layer structure in step 5 comprises the steps of: in each time slice, the second layer node is firstly used as a top layer node, the third layer node is used as a bottom layer node, the second layer node is split into m networks with two-layer structures, and m represents the number of subsystems excited by current or voltage in the system; and then, taking the second layer node as a bottom layer node, taking the first layer node as a top layer node, and splitting the first layer node into 1 network with a two-layer structure.
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