CN113240310A - Method for evaluating threat of group to single target - Google Patents

Method for evaluating threat of group to single target Download PDF

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CN113240310A
CN113240310A CN202110570434.5A CN202110570434A CN113240310A CN 113240310 A CN113240310 A CN 113240310A CN 202110570434 A CN202110570434 A CN 202110570434A CN 113240310 A CN113240310 A CN 113240310A
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温卓漫
徐旺
张瑞
秦旭珩
郭宇喆
陈鸣
游屈波
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Abstract

本发明公开了一种群体对单目标的威胁评估方法，包括步骤：S1，获取群体信息和目标信息；S2，利用获取的信息构建动态贝叶斯网络；S3，对构建的动态贝叶斯网络中的各个节点状态进行离散化处理；S4，离散处理后，确定条件概率表；S5，根据节点状态、节点间关系和条件概率表，获取时间片的概率分布；S6，根据概率分布，计算群体威胁等级；S7，输出群体威胁等级计算结果等；本发明实现了群体对单目标的准确快速威胁评估，将威胁评估关注的颗粒度从单平台提升到了群体，克服了传统算法部分参数缺失时评估结果不准确、未考虑时间上的连续变化以及贝叶斯网络法在不同威胁状态概率接近时威胁等级判定结果波动的缺陷。

Figure 202110570434

The invention discloses a threat assessment method for a group to a single target, comprising the steps of: S1, acquiring group information and target information; S2, constructing a dynamic Bayesian network by using the acquired information; S3, evaluating the constructed dynamic Bayesian network The state of each node in the process is discretized; S4, after the discretization process, determine the conditional probability table; S5, obtain the probability distribution of the time slice according to the node state, the relationship between nodes and the conditional probability table; S6, according to the probability distribution, calculate the group Threat level; S7, output group threat level calculation result, etc. The present invention realizes accurate and rapid threat assessment of a group to a single target, improves the granularity of threat assessment attention from a single platform to a group, and overcomes the evaluation when some parameters of the traditional algorithm are missing The results are inaccurate, the continuous changes in time are not considered, and the defect of the Bayesian network method is that the threat level judgment results fluctuate when the probability of different threat states is close.

Figure 202110570434

Description

Method for evaluating threat of group to single target
Technical Field
The invention relates to the technical field of information fusion, in particular to a method for evaluating threats of a group to a single target.
Background
Modern war shows the trend of fast rhythm and complication, and the information content is massively increased, has greatly aggravated commander cognitive pressure. Communities have replaced a single platform and become the initiator and carrier of threat events. The real-time threat assessment by using the group as granularity is urgently needed, and the method supports the commander to quickly sense the battlefield, reduces the cognitive pressure and makes a quick and efficient decision.
The current typical threat assessment method mainly comprises: fuzzy mathematics, multi-attribute decision, evidence theory, neural network, Bayesian network, etc. However, the traditional algorithm mainly focuses on a single combat target, and a huge and complicated evaluation result is difficult to assist a commander to quickly form cognition on the current battlefield threat; the continuity and the accumulation of the change of the target threat information along with the time are not considered enough; and the evaluation accuracy is reduced when data are missing.
In addition, since the traditional bayesian network method determines the threat level according to the probability of different states, the determination result is unstable when the probabilities of the two states are relatively close.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for evaluating the threat of a group to a single target, realizes accurate and rapid threat evaluation of the group to the single target, improves the granularity concerned by the threat evaluation from a single platform to the group, and overcomes the defects that the evaluation result is inaccurate when partial parameters of the traditional algorithm are missing, the continuous change in time is not considered, and the threat level judgment result fluctuates when the probabilities of different threat states are close to each other by a Bayesian network method.
The purpose of the invention is realized by the following scheme:
a method for threat assessment of a population to a single target, comprising the steps of:
s1, acquiring group information and target information;
s2, constructing a dynamic Bayesian network by using the acquired information;
s3, discretizing the states of all nodes in the constructed dynamic Bayesian network;
s4, determining a conditional probability table after discrete processing;
s5, obtaining probability distribution of different time slices according to the node state, the relationship between nodes and the conditional probability table;
s6, calculating a group threat level according to the probability distribution;
and S7, outputting a group threat level calculation result.
Further, in step S1, the acquired group information includes capability information and pose information of the group; the capability information comprises a group main member platform type, a main member radar type, a detection range and a striking range; the pose information comprises a central position, a main body advancing direction and an advancing speed; the acquired target information includes location information.
Further, in step S2, the dynamic bayesian network comprises proximity, capability and intention element data, the proximity element data comprising population-to-proximity point distance data, population-to-proximity point time data, target-to-proximity point distance data, proximity-to-target time data; the capability element data comprises the number data of platforms in a group, the type data of main member platforms, the radar type data of main members, the detection range data and the striking range data; the intention element data comprises group speed data, altitude data, azimuth data and radar working state data.
Further, in step S3, the following discretization processing is performed on each of the following nodes:
distance between the group and the adjacent point, setting the group to continue to move according to the current course and speed, and recording the distance between the group center point and the adjacent point as dt2pDistance node D between group and adjacent pointtpFor describing the distance of the segment, the corresponding state values are:
Figure BDA0003082394840000031
wherein DtpHAnd DtpLRespectively representing high and low threshold values of the distance between the group and the adjacent point, and selecting the threshold values according to the need of an analysis scene, wherein the value ranges are 300-500 km and 100-200 km respectively;
the time t for the group to reach the near point is set according to the current course and speedt2pObtaining the time node T of the group distance near pointtpThe corresponding state value is:
Figure BDA0003082394840000032
wherein, TtpHAnd TtpLRespectively high and low thresholds of time of a group and a near point, and the value ranges are respectively 20-40 min and 2-10 min;
the distance between the target and the adjacent point, if the group suddenly turns 90 degrees after reaching the adjacent point, the group directly advances to the target, and the distance between the adjacent point and the target is short and longp2aSetting a distance node D between a target and a near point for reflecting the height of a threatpaThe corresponding state values are:
Figure BDA0003082394840000033
wherein DpaHAnd DpaLThe distance between the target and the adjacent point is a node high threshold and a node low threshold, the value ranges are respectively 50-200 km and 5-20 km, and the distance can be adjusted according to the requirement;
the time between the adjacent point and the target point is estimated according to the current speed of the group, and the time required for reaching the target point from the adjacent point is recorded as tp2aCorresponding to the distance of the adjacent point from the target time node TpaThe state values are:
Figure BDA0003082394840000034
wherein T ispaHAnd TpaLThe time high and low thresholds of the group and the close point are respectively 2-10 min and 10-60 s;
the number of platforms in a population, the number n of the platforms in the population reflects the scale and the compiling level of the population, the population threat increases along with the increase of the number of the platforms in the population, and the state value corresponding to the node Nu of the number of the platforms in the population is as follows:
Figure BDA0003082394840000041
wherein N isHAnd NLThe value ranges of the high threshold and the low threshold of the number of the platforms in the group are respectively 2-5 and 5-10;
subject member platform type, subject members of a group representing the main operational capacity of the group, subject member platform type TppThe corresponding state values are:
Figure BDA0003082394840000042
subject member radar type reflecting scout detection capability of the group, subject member radar type node TprThe corresponding state values are:
Figure BDA0003082394840000043
a detection range, the size of the detection range of the group reflects the coverage area of the scout detection capability of the group, and the node D of the detection rangedeThe corresponding state values are:
Figure BDA0003082394840000051
wherein DdeHAnd DdeLThe value ranges of the high threshold and the low threshold of the detection range are respectively 200-500 km and 100-200 km.
A hitting range, the size of which reflects the ability area of the target to hit fire, and the node (hereinafter referred to as D)fr) The corresponding state values are:
Figure BDA0003082394840000052
wherein DfrHAnd DfrLThe values of the high threshold and the low threshold of the striking range are respectively 200-400 km and 50-200 km;
the group speed is a measure of the moving speed of the current whole group, the faster the group speed is, the more difficult the target is to prevent the moving speed, the higher the threat degree is, and the state value corresponding to the group speed node V is as follows:
Figure BDA0003082394840000053
wherein VHAnd VLThe values of high and low thresholds of a striking range are respectively Mach 1.0-2.0 and Mach 0.5-1.0;
the group height reflects the current behavior intention of the group, the lower the height is, the larger the threat is, and the state value corresponding to the group height node H is as follows:
Figure BDA0003082394840000054
wherein HVH、HH、HL、HVLThe height threshold value is respectively 10-20 km, 5-10 km, 1-5 km and 0.1-1 km;
the group orientation, the size theta of an included angle between the group advancing direction and a group central target connecting line is in the range of [ -180 degrees, 180 degrees ], the right deviation is positive by taking the group advancing direction as a reference, the group tactical intention can be reflected, and the state value corresponding to the group orientation node A is as follows:
Figure BDA0003082394840000061
wherein, γH、γLThe angle is respectively the high and low threshold values, the value ranges are respectively 100-140 degrees and 40-80 degrees;
radar operating condition, radar operating condition reflects the intention more directly perceivedly, and the state value that this radar operating condition node Ws corresponds is:
Figure BDA0003082394840000062
further, discretization processing of secondary nodes is included, wherein the secondary nodes comprise proximity nodes, capability nodes and intention nodes; wherein, the state value corresponding to the proximity node Px is:
Figure BDA0003082394840000063
the state values corresponding to the capability node Cp are:
Figure BDA0003082394840000064
the state value corresponding to the intention node It is:
Figure BDA0003082394840000071
further, discretization processing of a three-level node is included, wherein the three-level node comprises a top-level output node group threat; wherein, the state value corresponding to the top-level output node group threat Tr is:
Figure BDA0003082394840000072
further, in step S4, if the observation node has no parent node, assigning a value to the node condition probability table by using an expert knowledge method; and if the hidden node has a father node, adopting an EM (effective noise) method or a gradient descent method to learn the parameters of the Bayesian network, and acquiring a node condition probability table.
Further, in step S5, according to the observed node states, the inter-node relationships and the conditional probability table of different time slices, a forward-backward algorithm is used to derive the probability distribution of the hidden node under the observed value when the observed node is in a specific combination state:
Figure BDA0003082394840000073
wherein n is the number of hidden nodes, m is the number of observation nodes, and T is the number of time slices; subscripts i, j, k respectively represent an index value of a current hidden node, an index value of a current observation node, and an index value of a current time slice; x represents a hidden node and y represents an observed node; x is the number ofki，ykjThe 1 st subscript of (1) indicates the kth time slice, and the 2 nd subscript indicates the ith hidden node and the jth observation node under the time slice respectively; pa (x)ki)，pa(ykj) Respectively represent xki，ykjA set of parent nodes; p (y)kj＝ykjo) represents the j observation node y in the k time slicekjProbability of being in state o; p (A | B) represents the probability of an event A occurring if another event B has occurred, P (x)ki|Pa(xki) Represents the probability of x occurring if all the parents of the ith hidden node x of the kth time slice occur.
Further, in step S6, according to the result of step S5, the probability of the top-level node Tr in three states of high, medium, and low per time slice can be obtained; weight distribution is carried out on the high state, the medium state and the low state, a rose diagram is drawn according to the probability distribution and the weight ratio of the three states, and the central angle of the rose petal corresponding to each state is largeSmall is omegai：
Figure BDA0003082394840000081
Wherein wiIs the weight of the ith state and guarantees wHeight of>wIn>wIs low in(ii) a The radius length of the petal is Ri：
Ri＝Pi
Wherein, PiCalculating the total length L of the circle arc of the rose diagram for the probability of the ith state:
Figure BDA0003082394840000082
judging the threat level TL of the current time slice group according to the total length L of the arcs:
Figure BDA0003082394840000083
further, in step S7, the probability distribution and threat level calculation results for each time slice are output and presented.
The beneficial effects of the invention include:
the invention establishes an empty threat analysis element by taking a population as granularity, constructs a dynamic Bayesian network and draws a rose graph and the like based on the Bayesian network, realizes accurate and rapid threat assessment of the population on a single target, improves the granularity concerned by the threat assessment from a single platform to the population, and overcomes the defects of inaccurate assessment result, continuous change in time and fluctuation of threat level judgment result when the probability of different threat states approaches in the traditional algorithm is not considered when partial parameters of the traditional algorithm are missing.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a threat assessment method of the present invention;
FIG. 2 is a dynamic Bayesian threat assessment network of the present invention;
FIG. 3 is a diagram illustrating the physical definition of proximity calculation-related variables in the present invention;
FIG. 4 is a diagram illustrating the physical definition of the group orientation calculation-related variables in the present invention;
FIG. 5 is a graph of a calculated rose of threat levels for a population in accordance with the present invention;
FIG. 6 is a graph of group threat dynamic Bayesian network assessment results over a first timeslice;
FIG. 7 is a result of evaluating the proximity element of FIG. 6;
FIG. 8 is a result of evaluation of the energy components of FIG. 6;
FIG. 9 is a result of evaluation of the schematic element of FIG. 6;
FIG. 10 is a graph of group threat dynamic Bayesian network assessment results over a second timeslice;
FIG. 11 is a result of evaluating the proximity element of FIG. 10;
FIG. 12 is a result of evaluation of the energy components of FIG. 10;
FIG. 13 is a result of evaluation of the schematic element of FIG. 10;
FIG. 14 is a graph of group threat dynamic Bayesian network assessment results over a third timeslice;
FIG. 15 is a result of evaluation of the proximity element of FIG. 14;
FIG. 16 is a result of evaluation of the energy components of FIG. 14;
FIG. 17 is a result of evaluation of the schematic element of FIG. 14;
FIG. 18 is a graph of calculated roses of threat levels for a group in a first time slice;
FIG. 19 is a graph of calculated roses of threat levels for the group over a second time slice;
FIG. 20 is a graph of calculated roses of threat levels for the population in the third time slice.
Detailed Description
All features disclosed in all embodiments in this specification, or all methods or process steps implicitly disclosed, may be combined and/or expanded, or substituted, in any way, except for mutually exclusive features and/or steps.
The meaning of the approach point: and the position point which is closest to the target in the flight path formed by the group under the current attitude driving condition.
As shown in fig. 1 to 20, a method for evaluating a threat of a group to a single target, in a specific embodiment, as shown in fig. 1, includes the steps of:
(1) population and target information acquisition
The group capability information is input as follows: the number of platforms in the group is 4, the type of the main member platform is a fighter, the detection range is 250km, and the attack range is 160 km. Group pose information is as in table 1:
TABLE 1 discretization of the state of proximity-type observation nodes
Figure BDA0003082394840000111
The center position of the colony is (east longitude 119.1 degrees, north latitude 23.9 degrees, height 300m), the traveling direction of the main body is 137.5 degrees north, and the traveling speed is Mach 1.5.
The target position information is as follows: east longitude 120 degrees, north latitude 23 degrees, height 0 m.
(2) Dynamic bayesian network construction
Referring to fig. 2, a dynamic bayesian threat analysis network of a population to a single target is constructed.
(3) Node state discretization
Referring to the discretization formula of the state of each node, the selected threshold is shown in table 2, and the state of each node in the network is discretized.
TABLE 2 state discretization threshold selection
Figure BDA0003082394840000112
At 10s intervals, 3 time slices were analyzed. The state of the capability class observation node does not change with time, and the value is shown in table 3:
TABLE 3 discretization of node State for capability class Observation
Figure BDA0003082394840000113
The state values of the proximity class observation nodes and the intention class observation nodes are obtained to change along with time, and the state values of the proximity class corresponding to 3 time slices are shown in a table 4:
TABLE 4 discretization of the state of the observation nodes of the proximity class
Figure BDA0003082394840000121
The corresponding intention class status values for the 3 time slices are shown in table 5:
TABLE 5 discretization of node states for intent observation
Figure BDA0003082394840000122
(4) Conditional probability table determination
Acquiring a primary node condition probability table by using an expert knowledge method; and (3) learning the dynamic Bayesian network parameters by using an EM (Expectation Maximization) method or a gradient descent method to obtain the conditional probability table of other nodes.
(5) Bayesian network inference
The bayesian network inference is performed based on the state values of the observation nodes of different time slices, the inference relationship between the nodes and the conditional probability table by using a forward and backward reasoning method, and the evaluation results of the three time slices are obtained as shown in fig. 6 to 17.
It can be seen that, in the first time slice, the probabilities of the group threat state values being high, medium, and low are: 95.5%, 4.43% and 0.051%; in the second time slice, the corresponding probabilities are respectively: 96.6%, 3.40%, 0.030%; in the third time slice, the corresponding probabilities are respectively: 98.2%, 1.84% and 0.011%. The evaluation results were in agreement with the expectations.
(6) Group threat level calculation
The weights of the high, medium and low threat states are 4/7, 2/7 and 1/7 respectively. The rose graphs of the three time slices are drawn as shown in fig. 18, 19 and 20, the total lengths L of the arcs of the corresponding rose graphs are 3.5129, 3.5320 and 3.5598 respectively, and the corresponding threat levels of the groups are high, high and high respectively.
(7) Result output
And outputting and displaying the group threat assessment result of each time slice.
In conclusion, the method can integrate three major multidimensional factors of proximity, capability and intention and accurately evaluate the threat of a group to a single target. The method not only promotes the granularity concerned by threat analysis from a single platform to a group; meanwhile, by using the dynamic Bayesian network, the continuous change of the threat in time is effectively embodied, and the stability of the evaluation result can be ensured when the input parameter is partially lost; in addition, the threat level is calculated by adopting the rose diagram, so that the condition that the threat level output by the traditional Bayesian network method is unstable when the probabilities of different threat states are close is avoided.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Other embodiments than the above examples may be devised by those skilled in the art based on the foregoing disclosure, or by adapting and using knowledge or techniques of the relevant art, and features of various embodiments may be interchanged or substituted and such modifications and variations that may be made by those skilled in the art without departing from the spirit and scope of the present invention are intended to be within the scope of the following claims.
The functionality of the present invention, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium, and all or part of the steps of the method according to the embodiments of the present invention are executed in a computer device (which may be a personal computer, a server, or a network device) and corresponding software. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, or an optical disk, exist in a read-only Memory (RAM), a Random Access Memory (RAM), and the like, for performing a test or actual data in a program implementation.

Claims (10)

1. A method for threat assessment of a population to a single target, comprising the steps of:
s1, acquiring group information and target information;
s2, constructing a dynamic Bayesian network by using the acquired information;
s3, discretizing the states of all nodes in the constructed dynamic Bayesian network;
s4, determining a conditional probability table after discrete processing;
s5, obtaining probability distribution of different time slices according to the node state, the relationship between nodes and the conditional probability table;
s6, calculating a group threat level according to the probability distribution;
and S7, outputting a group threat level calculation result.
2. The method according to claim 1, wherein in step S1, the acquired group information includes ability information and pose information of the group; the capability information comprises a group main member platform type, a main member radar type, a detection range and a striking range; the pose information comprises a central position, a main body advancing direction and an advancing speed; the acquired target information includes location information.
3. The method of claim 1, wherein in step S2, the dynamic bayesian network comprises proximity, capability and intention element data, the proximity element data comprising group-to-proximity point distance data, group-to-proximity point time data, object-to-proximity point distance data, proximity-to-object time data; the capability element data comprises the number data of platforms in a group, the type data of main member platforms, the radar type data of main members, the detection range data and the striking range data; the intention element data comprises group speed data, altitude data, azimuth data and radar working state data.
4. The method according to claim 1, wherein in step S3, the following nodes are discretized as follows:
distance between the group and the adjacent point, setting the group to continue to move according to the current course and speed, and recording the distance between the group center point and the adjacent point as dt2pDistance node D between group and adjacent pointtpFor describing the distance of the segment, the corresponding state values are:
Figure FDA0003082394830000021
wherein DtpHAnd DtpLRespectively representing high and low threshold values of the distance between the group and the adjacent point, and selecting the threshold values according to the need of an analysis scene, wherein the value ranges are 300-500 km and 100-200 km respectively;
the time t for the group to reach the near point is set according to the current course and speedt2pObtaining the time node T of the group distance near pointtpThe corresponding state value is:
Figure FDA0003082394830000022
wherein, TtpHAnd TtpLRespectively high and low thresholds of time of a group and a near point, and the value ranges are respectively 20-40 min and 2-10 min;
the distance between the target and the adjacent point, if the group suddenly turns 90 degrees after reaching the adjacent point, the group directly advances to the target, and the distance between the adjacent point and the target is short and longp2aSetting a distance node D between a target and a near point for reflecting the height of a threatpaThe corresponding state values are:
Figure FDA0003082394830000023
wherein DpaHAnd DpaLThe distance between the target and the adjacent point is a node high threshold and a node low threshold, the value ranges are respectively 50-200 km and 5-20 km, and the distance can be adjusted according to the requirement;
the time between the adjacent point and the target point is estimated according to the current speed of the group, and the time required for reaching the target point from the adjacent point is recorded as tp2aCorresponding to the distance of the adjacent point from the target time node TpaThe state values are:
Figure FDA0003082394830000031
wherein T ispaHAnd TpaLThe time high and low thresholds of the group and the close point are respectively 2-10 min and 10-60 s;
the number of platforms in a population, the number n of the platforms in the population reflects the scale and the compiling level of the population, the population threat increases along with the increase of the number of the platforms in the population, and the state value corresponding to the node Nu of the number of the platforms in the population is as follows:
Figure FDA0003082394830000032
wherein N isHAnd NLThe value ranges of the high threshold and the low threshold of the number of the platforms in the group are respectively 2-5 and 5-10;
subject member platform type, subject members of a group representing the main operational capacity of the group, subject member platform type TppThe corresponding state values are:
Figure FDA0003082394830000033
subject member radar type reflecting scout detection capability of the group, subject member radar type node TprThe corresponding state values are:
Figure FDA0003082394830000034
a detection range, the size of the detection range of the group reflects the coverage area of the scout detection capability of the group, and the node D of the detection rangedeThe corresponding state values are:
Figure FDA0003082394830000041
wherein DdeHAnd DdeLThe value ranges of the high threshold and the low threshold of the detection range are respectively 200-500 km and 100-200 km.
A hitting range, the size of which reflects the ability area of the target to hit fire, and the node (hereinafter referred to as D)fr) The corresponding state values are:
Figure FDA0003082394830000042
wherein DfrHAnd DfrLThe values of the high threshold and the low threshold of the striking range are respectively 200-400 km and 50-200 km;
the group speed is a measure of the moving speed of the current whole group, the faster the group speed is, the more difficult the target is to prevent the moving speed, the higher the threat degree is, and the state value corresponding to the group speed node V is as follows:
Figure FDA0003082394830000043
wherein VHAnd VLThe values of high and low thresholds of a striking range are respectively Mach 1.0-2.0 and Mach 0.5-1.0;
the group height reflects the current behavior intention of the group, the lower the height is, the larger the threat is, and the state value corresponding to the group height node H is as follows:
Figure FDA0003082394830000044
wherein HVH、HH、HL、HVLThe height threshold value is respectively 10-20 km, 5-10 km, 1-5 km and 0.1-1 km;
the group orientation, the size theta of an included angle between the group advancing direction and a group central target connecting line is in the range of [ -180 degrees, 180 degrees ], the right deviation is positive by taking the group advancing direction as a reference, the group tactical intention can be reflected, and the state value corresponding to the group orientation node A is as follows:
Figure FDA0003082394830000051
wherein, γH、γLThe angle is respectively the high and low threshold values, the value ranges are respectively 100-140 degrees and 40-80 degrees;
radar operating condition, radar operating condition reflects the intention more directly perceivedly, and the state value that this radar operating condition node Ws corresponds is:
Figure FDA0003082394830000052
5. the method of claim 4, comprising discretizing a secondary node comprising a proximity node, a capability node, and an intent node; wherein, the state value corresponding to the proximity node Px is:
Figure FDA0003082394830000053
the state values corresponding to the capability node Cp are:
Figure FDA0003082394830000054
the state value corresponding to the intention node It is:
Figure FDA0003082394830000061
6. the method of claim 5, comprising discretizing a three-level node, the three-level node comprising top-level output node group threats; wherein, the state value corresponding to the top-level output node group threat Tr is:
Figure FDA0003082394830000062
7. the method according to claim 1, wherein in step S4, if there is no father node in the observation node, the node conditional probability table is assigned by using expert knowledge; and if the hidden node has a father node, adopting an EM (effective noise) method or a gradient descent method to learn the parameters of the Bayesian network, and acquiring a node condition probability table.
8. The method of claim 1, wherein in step S5, according to the observed node states, the inter-node relationships and the conditional probability table of different time slices, a forward-backward algorithm is used to derive the probability distribution of hidden nodes under the observation values when the observed nodes are in a specific combination state:
Figure FDA0003082394830000063
wherein n is the number of hidden nodes, m is the number of observation nodes, and T is the number of time slices; subscripts i, j, k respectively represent an index value of a current hidden node, an index value of a current observation node, and an index value of a current time slice; x represents a hidden node and y represents an observed node; x is the number ofki，ykjThe 1 st subscript of (1) indicates the kth time slice, and the 2 nd subscript indicates the ith hidden node and the jth observation node under the time slice respectively; pa (x)ki)，pa(ykj) Respectively represent xki，ykjA set of parent nodes; p (y)kj＝ykjo) represents the j observation node y in the k time slicekjProbability of being in state o; p (A | B) represents the probability of an event A occurring if another event B has occurred, P (x)ki|Pa(xki) Represents the probability of x occurring if all the parents of the ith hidden node x of the kth time slice occur.
9. The method of claim 8, wherein in step S6, according to the result of step S5, the probability of each time slice of the top node Tr in three states of high, medium and low can be obtained; carrying out weight distribution on the high state, the medium state and the low state, drawing a rose diagram according to the probability distribution and the weight ratio of the three states, wherein the central angle of the rose petal corresponding to each state is omegai：
Figure FDA0003082394830000071
Wherein wiIs the weight of the ith state and guarantees wHeight of>wIn>wIs low in(ii) a The radius length of the petal is Ri：
Ri＝Pi
Wherein, PiCalculating the total length L of the circle arc of the rose diagram for the probability of the ith state:
Figure FDA0003082394830000072
judging the threat level TL of the current time slice group according to the total length L of the arcs:
Figure FDA0003082394830000081
10. the method of claim 9, wherein the probability distribution and threat level calculation results for each time slice are output and presented in step S7.
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