CN113496547A - Method, device, equipment and medium for identifying weakest path of physical protection system - Google Patents
Method, device, equipment and medium for identifying weakest path of physical protection system Download PDFInfo
- Publication number
- CN113496547A CN113496547A CN202110690073.8A CN202110690073A CN113496547A CN 113496547 A CN113496547 A CN 113496547A CN 202110690073 A CN202110690073 A CN 202110690073A CN 113496547 A CN113496547 A CN 113496547A
- Authority
- CN
- China
- Prior art keywords
- node
- path
- heuristic
- weakest
- calculation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 78
- 238000004364 calculation method Methods 0.000 claims abstract description 66
- 238000010845 search algorithm Methods 0.000 claims abstract description 46
- 238000012800 visualization Methods 0.000 claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 32
- 238000013461 design Methods 0.000 claims abstract description 20
- 230000006870 function Effects 0.000 claims description 74
- 238000001514 detection method Methods 0.000 claims description 40
- 230000004044 response Effects 0.000 claims description 27
- 238000003860 storage Methods 0.000 claims description 16
- 238000009826 distribution Methods 0.000 claims description 14
- 238000011156 evaluation Methods 0.000 claims description 9
- 230000000007 visual effect Effects 0.000 claims description 7
- 230000009471 action Effects 0.000 claims description 4
- 230000003321 amplification Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 2
- 238000004220 aggregation Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 19
- 230000008569 process Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000009545 invasion Effects 0.000 description 6
- 208000027066 STING-associated vasculopathy with onset in infancy Diseases 0.000 description 5
- 238000004590 computer program Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000001681 protective effect Effects 0.000 description 2
- 230000002285 radioactive effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 241000321453 Paranthias colonus Species 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000032823 cell division Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000265 homogenisation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000013146 percutaneous coronary intervention Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000005316 response function Methods 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012916 structural analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
- G06T17/205—Re-meshing
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device, equipment and a medium for identifying the weakest path of a real object protection system, wherein the method comprises the following steps: performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model; based on a system structure parameter visualization model, taking system interception probability as a cost value, and designing a graph theory-based reverse path heuristic search algorithm; the heuristic search algorithm of the reverse path based on the graph theory, the device, the equipment and the medium comprises a calculation method of the remaining time TR required by the enemy task under different reverse path plans and an optimized heuristic function; and identifying the weakest path of the physical protection system by a graph theory-based reverse path heuristic search algorithm. The algorithm provided by the invention solves the problem that the global optimal solution cannot be obtained, and the acceptability and completeness of the validity analysis result of the physical protection system are better by combining the optimized heuristic function design.
Description
Technical Field
The invention belongs to the field of nuclear security, and particularly relates to a method, a device, equipment and a medium for identifying the weakest path of a real object protection system.
Background
The design and effectiveness evaluation of the early real object protection system are only limited to a one-dimensional single preset attack path model, and a representative analysis method is an EASI method and mainly aims at the problem of nuclear security. In the 1980 s, the national laboratory of sandia in the united states proposed an SAVI method based on an Adversary intrusion path Sequence Diagram (ASD) for multi-path validity analysis of a physical protection system. During the same time period, a series of related methods are proposed, such as the ASSESS method, the MA physical protection system method, etc., but most of the methods are improvements to the EASI and SAVI methods. The EASI, SAVI, ASSESSS methods can only be used for simplifying structural analysis of one-dimensional physical protection systems, and many imprecise assumptions are made, such as the SAVI method and the ASSESS method, which both use a multi-path model known as the enemy intrusion sequence diagram. The ASD model cannot delineate where the adversary passes through the barrier and considers that the area distance that the adversary needs to pass is the same regardless of which sequence of paths.
In the later stage, korean nuclear nonproliferation and control research 2008 proposes an SAPE method based on a two-dimensional facility map. The SAPE method can expand the layout of the protection facilities into a two-dimensional grid map for solving the uncertainty related to the distance, but the SAPE method does not consider the uncertainty unrelated to the distance, and the rasterization division often introduces the problem of computational complexity due to further refinement of model elements. The SAPE method computes the system interception probability (P) using the EASI model rather than the just-in-time detection conceptI) And a heuristic A-x algorithm (best first search algorithm) is combined for searching the weak path. Although the heuristic algorithm in the SAPE method does not need to traverse all possible situations, the H function in the heuristic algorithm is only used for estimating the remaining time required for the enemy task to complete, the provided analysis result is only applicable in some situations, and the weak path identified by the SAPE in the practical case may not be a real weak path.
Generally speaking, the effectiveness design and analysis of the physical protection system is gradually advanced from a one-dimensional specific single path evaluation method to a two-dimensional plane model/three-dimensional model and a multi-way path search analysis method based on graph theory, but most methods still adopt EASI and SAVI deterministic specific attack route calculation theory and model, and the path planning is determined according to the forward intrusion task view angle of an adversary from a starting point to a target protection node. When the forward search strategy is used, the weakest path search algorithm with the system interception probability as the cost value can firstly obtain the path with the minimum cost value from the source point to the current node system interception probability, the selection usually only focuses on the current cost value, the dependency relationship between the cost value of the subsequent path and the previous node detection probability is not considered, and the weakest path corresponding to the minimum interception probability of the whole system can not be identified. In addition, even if the system effectiveness metric parameter index is calculated for the same path in different path directions, the obtained values of the system effectiveness metric parameter index are different.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a method, a device, equipment and a medium for identifying the weakest path of a physical protection system, wherein the method for identifying the weakest path comprises a plurality of calculation methods of residual time TR required for completing enemy tasks under different reverse path plans, the problems that the TR and cost values are not available due to the fact that subsequent invasion paths of related nodes are searched in the forward direction are unknown and the system interception probability analysis theory in the EASI (early event analysis) process is not applicable any more are solved, the designed heuristic function can effectively give out the global optimal solution of the weakest path of the system, and the effective correction of the non-heuristic weakest path deviation result is realized.
The invention aims to provide a method for identifying the weakest path of a real object protection system.
A second object of the present invention is to provide a weakest path recognition apparatus for a physical protection system.
It is a third object of the invention to provide a computer apparatus.
It is a fourth object of the present invention to provide a storage medium.
The first purpose of the invention can be achieved by adopting the following technical scheme:
a method of identifying a weakest path of a physical protection system, the method comprising:
performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model;
based on a system structure parameter visualization model, taking system interception probability as a cost value, and designing a graph theory-based reverse path heuristic search algorithm; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and identifying the weakest path of the physical protection system by a graph theory-based reverse path heuristic search algorithm.
Furthermore, each grid in the system structure parameter visualization model corresponds to a node, a target protection node is used as a starting point, and a starting position point corresponding to enemy attack is a terminal point;
the method for designing the graph theory-based reverse path heuristic search algorithm based on the system structure parameter visualization model by taking the system interception probability as a cost value specifically comprises the following steps:
adding the starting point into an OpenList, taking the system interception probability as the cost value of the nodes, and initializing the cost value of each node to be 0;
judging whether the OpenList is empty or not, if so, failing to search, and ending the algorithm search; if not, then:
selecting the node with the minimum replacement value from the OpenList as the node N to be processed currentlya；
Judging the current node NaWhether it is the termination point NeIf so, reconstructing a reverse path according to the nodes in the CloseList and the parent node index information of the nodes to obtain the weakest path of the system, and finishing the algorithm search; if not, then:
the current node NaFrom OpenLisDeleting the sequence in the sequence and adding the sequence into CloseList;
obtaining the current node N based on graph theory data structure searchaAll adjacent nodes N ofnAnd access the current node N one by one according to the access ruleaAdjacent node N ofn；
Judging adjacent node NnIf not, accessing next adjacent node and using the next adjacent node as the current adjacent node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation; if yes, then:
judging adjacent node NnIf the current neighbor node is in the CloseList, if so, accessing a next neighbor node, and taking the next neighbor node as the current neighbor node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation; if not, then:
judging adjacent node NnWhether in OpenList:
if not, the current node N is determinedaSet as its adjacent node NnParent node N ofpCalculating the neighbor node NnAnd will be adjacent to node NnAdding OpenList, and continuously accessing the next adjacent node; taking the next adjacent node as the current adjacent node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation;
if so, judging that the current node N is passed throughaTo the adjacent node NnIf the actual cost value is smaller, the current node N is determined to be the node NaSet as adjacent node NnParent node N ofpAnd recalculating and updating the neighbor node NnReturning to judge whether the OpenList is empty or not, and continuously executing subsequent operations; if not, returning to judge whether the OpenList is empty or not, and continuing to execute subsequent operations.
Further, the adjacent node NnThe cost value F is calculated by a cost function F (n), and the expression of F (n) is as follows:
F(n)＝G(n)+H(n)
wherein n is a current node to be detected, g (n) represents an actual cost value consumed for reaching the current node to be detected from an initial position along a generated path, h (n) represents an estimated cost value consumed for reaching a terminal point from the current node to be detected, and h (n) is called a heuristic function;
when the algorithm is used for the heuristic search design of the weakest path of the physical protection system, the conditions which must be met include:
for any node x, y, all paths from the starting point to the node y after passing through the node x and then to the end point and paths from the starting point to the end point directly through the node x satisfy the following inequality:
G(x)+H(x)≤G(y)+H(y)
suppose G*(n) represents the actual cost value from the starting point to the current node, H*(n) represents the actual cost value from the current node to the endpoint, then:
F*(n)＝G*(n)+H*(n)
satisfies the following conditions:
H(n)≤H*(n)。
further, the TR calculation method includes calculation based on the TR of the weakest path and calculation based on the TR of the minimum delay path, and specifically includes:
the calculation expression of TR for the ith node is as follows:
wherein, TRi-1The remaining time required for the enemy task of node i-1 to complete; l is the distance between the ith and the (i-1) th nodes; t is tdiDelay brought by the protection element of the delay function at the ith node, if there is no delay element at the ith node, tdi＝0；
The calculation of TR based on the weakest path is based on the determined path with the minimum system interception probability from the terminal point to the parent node of the current node as l and tdiIs calculated according to；
The calculation of the TR based on the minimum time delay path is that the path with the minimum time delay from the current node to the terminal point is used as l and tdiThe path of the minimum time delay is obtained by a calculation structure of traversing the end point layer by layer outwards through a priority search algorithm.
Further, the graph theory-based reverse path heuristic search algorithm further comprises a calculation method of system response time RFT, wherein the calculation method of RFT specifically comprises the following steps:
assuming that the delay function of the system does not work during the travel in response to the force, the calculation expression of the RFT is as follows:
RFT＝temp+ttr
wherein, tempTime required for aggregation; t is ttrDetermining the path travel time according to the shortest distance from the response force to the target point;
the shortest distance path is realized by an A-algorithm by taking the Euclidean distance from the current node to the target node as a heuristic function.
Further, the optimized heuristic function h (n) specifically includes:
constructing a heuristic function of the time left for an adversary to complete a task:
wherein v is the average speed of the enemy; TR (transmitter-receiver)iThe time remaining for the enemy to complete the task from the current node to the target protection terminal; lambda [ alpha ]1And λ2Are each TRiAnd the amplification factor of l/v by modifying lambda1And λ2The value of (c), the adjustment of the degree of action of the heuristic function is achieved;
calculating the obtained RFT and HTRSubstituting the following formula to obtain a mean value X:
X＝HTR-RFT
respectively reacting obtained HTR30% of RFT and 30% of RFTAs the respective variance and X, the following is substituted:
h _ P to be obtained(R|A)Substituting the following formula to obtain the optimized heuristic function:
H(n)＝k·H_P(R|A)
where k is the influencing factor of the heuristic function pre-evaluation, H _ P(R|A)To realize the conversion variable of the shortest path distance to the probability evaluation index.
Further, performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model specifically includes:
inputting design information of a physical protection system, and determining relevant parameters of a target protection facility;
carrying out grid division on the whole protection area of the target protection facility to generate a system two-dimensional grid planar model;
deploying protection function elements of the physical protection system to complete the construction of a system detection probability field and a time parameter distribution field;
and superposing related attribute parameters and distribution fields of the protection function elements of the physical protection system to a two-dimensional gridding planar model of the system to generate a system structure parameter visualization model.
The second purpose of the invention can be achieved by adopting the following technical scheme:
an apparatus for identifying a weakest path of a physical protection system, the apparatus comprising:
the generation module is used for performing two-dimensional structure parameter visual modeling on the physical protection system to generate a system structure parameter visual model;
the design module is used for designing a reverse path heuristic search algorithm based on a graph theory by taking the system interception probability as a cost value based on a system structure parameter visualization model; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and the identification module is used for identifying the weakest path of the physical protection system through a reverse path heuristic search algorithm based on graph theory.
The third purpose of the invention can be achieved by adopting the following technical scheme:
a computer device comprising a processor and a memory for storing a processor executable program, the processor implementing the weakest path identification method when executing the program stored in the memory.
The fourth purpose of the invention can be achieved by adopting the following technical scheme:
a storage medium stores a program which, when executed by a processor, implements the weakest path identification method described above.
Compared with the prior art, the invention has the following beneficial effects:
1. the heuristic function designed by the invention considers the estimated cost from the current node to the end point, the given path search result is a global optimal solution, the effective correction of the non-heuristic weakest path deviation result can be realized, and the local optimization problem of the non-heuristic search algorithm is solved. Moreover, the number of nodes traversed by the designed heuristic function in the searching process is obviously less than that of a non-heuristic algorithm, and the execution efficiency of the algorithm is higher.
2. The two methods for calculating the TR of the remaining time required by the enemy to finish the task can analyze different enemy intrusion strategies, the method for calculating the TR based on the weakest path is closer to the application of an actual intrusion scene, and the method for calculating the TR based on the minimum time delay path is more suitable for solving criterions of internal and external threat sources or solving situations that the enemy completely grasps the advancing route of a protection facility, and supports the effectiveness analysis of a faulty physical protection system in various scenes and threat sources.
3. The backward heuristic path search algorithm provided by the invention solves the problems that TR and system interception probability are irreconcilable due to uncertainty of a subsequent intrusion path of a forward heuristic search adversary, and a global optimal solution cannot be obtained, and has better acceptability and completeness of system effectiveness analysis results by combining with an optimized heuristic function design.
Drawings
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, 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 the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of a graph-theory-based reverse path heuristic search algorithm according to embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of a node path in embodiment 1 of the present invention.
Fig. 3 is a distribution diagram of the detection points on the reverse search path according to embodiment 1 of the present invention.
Fig. 4 is a schematic diagram of a two-dimensional structural parameter visualization model of the system according to embodiment 2 of the present invention.
Fig. 5 is a schematic diagram of the weakest path of the system under Dijkstra algorithm search in embodiment 2 of the present invention.
Fig. 6 is a schematic diagram of a backward heuristic path search based on a method for computing a TR of a weakest path according to embodiment 2 of the present invention.
Fig. 7 is a schematic diagram of a backward heuristic path search based on a minimum delay path TR calculation method according to embodiment 2 of the present invention.
Fig. 8 is a block diagram of a computer device according to embodiment 4 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Example 1:
the embodiment provides a method for identifying the weakest path of a physical protection system, which comprises two parts of modeling and analyzing, and specifically comprises the following steps:
s101, performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model.
The two-dimensional structure parameter visualization modeling process of the physical protection system specifically comprises the following steps:
(1) determining related parameters of target protection facilities, such as the division of protection areas, the number of buildings in the protection areas of different floors, the physical structure size and the like, based on the input of system design data;
(2) carrying out reasonable plane rasterization on a protection area and protection facilities according to the characteristics of system partition and an adversary attack strategy to generate a system two-dimensional gridding plane model;
(3) arranging and designing system protection elements of the physical protection system to complete the construction of a system detection probability field and a time parameter distribution field;
(4) and superposing the related attribute parameters and the distribution field of the protection elements of the physical protection system to a physical structure model of the system to generate a visual model of the structural parameters of the system.
S102, designing a graph theory-based reverse path heuristic search algorithm based on a system structure parameter visualization model and taking the system interception probability as a cost value.
Further, step S102 specifically includes:
s1021, as shown in fig. 1, the graph theory-based reverse path heuristic search algorithm specifically includes:
(1) adding the target protection node as a starting point into an OpenList, assigning the initial cost value of the starting point to be 0, and taking the system interception probability as the cost value of the node.
(2) Judging whether the OpenList is empty or not, if so, failing to search, namely, being incapable of generating a wayFinishing the algorithm search; if the nodes exist in the OpenList, the node with the minimum replacement value is selected from the OpenList to be used as the node (N) to be processed currentlya)。
(3) Further judging the current node NaWhether it is the termination point Ne(initial position point corresponding to enemy attack), if yes, then according to the nodes in CloseList and its father node index information to make reverse path reconstruction, starting from the end point to generate enemy invasion path along father node, returning result, namely the weakest path of system, and finishing algorithm search; if the current node NaIs not terminal point NeThen the current node N is setaDeleted from OpenList and added to CloseList.
(4) Obtaining the current node N based on graph theory data structure searchaAll adjacent nodes N ofnAnd access the current node N one by one according to a certain access rule (first-in first-out storage and access mechanism)aAdjacent node N ofn。
(5) Judging adjacent node NnWhether the node is reachable or not is judged, if so, whether the adjacent node is in the closeList is further judged; if adjacent node NnIf not, accessing the next adjacent node; taking the next adjacent node as the current adjacent node NnAnd returning to the step (5) to continue executing;
(6) judging adjacent node NnWhether it already exists in CloseList, if neighbor node NnAlready in CloseList, then access the next neighbor node; taking the next adjacent node as the current adjacent node NnAnd returning to the step (5) to continue executing;
if adjacent node NnIf not in CloseList, further determine the neighboring node NnWhether in an OpenList.
(7) Judging adjacent node NnIf the node N is not already in the OpenList, the node N is not in the OpenList listnIf not in OpenList, then the current node N is setaSet as its adjacent node NnParent node N ofpCalculating the neighbor node NnAnd will be adjacent to node NnAdding OA penList; continuing to access the next adjacent node; taking the next adjacent node as the current adjacent node NnAnd returning to the step (5) to continue executing;
(8) if adjacent node NnIn OpenList, it is determined that the current node N passesaTo the adjacent node NnIf the actual cost value G (N) of the path is smaller, if so, the current node N is determinedaSet as its adjacent node NnParent node N ofpAnd recalculating and updating the neighbor node NnAnd returning to the step (2) to continue executing; if not, returning to the step (2) and continuing to execute.
And S1022, cost value of the node.
The adjacent node NnThe cost value F is calculated by a cost function F (n), and the expression of F (n) is as follows:
F(n)＝G(n)+H(n) (1)
in the formula, f (n) represents an evaluation function, which includes two parts, one part is an actual cost value g (n) consumed for reaching the current node to be detected from an initial position along a generated path, and the actual cost value g (n) is represented by a system interception probability of the current node to be detected; the other part is an estimated value H (n) of the cost to be consumed from the current node to be detected to the end point. H (n), also called heuristic function, is considered a heuristic, related to the actual problem and the specific scenario.
It should be noted that, when the a-x algorithm is used for the weakest path heuristic search design of the physical protection system, the selection of the g (n) function and the h (n) function in the above formula is not arbitrary, but must satisfy a certain condition. For example, for any node x, y in fig. 2, all the paths from the starting point to the node y after passing through the node x and then to the end point and the paths from the starting point to the end point directly through the node x need to satisfy the following triangle inequality:
G(x)+H(x)≤G(y)+H(y) (2)
suppose G*(n) represents the actual cost value from the starting point to the current node, H*(n) represents the actual cost value from the current node to the endpoint, then:
F*(n)＝G*(n)+H*(n) (3)
when the A-algorithm is used for calculating the value of the valuation function F (n) in the process of searching the weakest path of the physical protection system, the requirement that the estimation cost value from the starting point to the current node is not lower than the actual cost value of the estimation cost value is met, namely G (n) is not less than G*(n) of (a). In general, most of the cost values from the starting point to a node are calculable, so that G (n) ═ G is easily satisfied*(N), so the algorithm is judging to pass the current node NaTo the adjacent node NnThe smaller the cost value of G (n) is, the more optimal the path is represented by using the value of G (n) as a reference. And H (n) is less than or equal to H when the heuristic function is designed*(n) that the actual cost value from the current node to the endpoint is satisfied is not less than the estimated cost value.
S1023, the actual cost value G (n).
In order to realize effective calculation of the actual cost value g (n) in the reverse path search process, the following calculation model is proposed in the patent.
Considering the example of the path shown in fig. 3, assuming that there are n detection points between the start point (the enemy attack start position point) and the end point (the protection target position point) of the path, the number is 1, 2, … …, n in order from the end point.
Based on the theory of the EASI method, the actual cost value G (1) from the node where the detection point 1 is located to the end point is calculated as follows:
G(1)＝PD1·PC·PR|A1 (4)
in the formula, PD1Indicates the probability of detection of the detection point 1, PCIndicating the probability, P, of successful communication with the responsive force after detection of the adversaryR|A1Indicating the probability of the response power successfully interrupting the enemy intrusion process after the enemy is detected and communication is successfully achieved with the response power.
The actual cost value G (2) from the node where the probe point 2 is located to the end point can be calculated by the following formula:
similarly, the actual value G (i-1) starting from the node where the detection point i-1 is located to the end point is calculated as follows:
generalizing, a general calculation expression of the actual cost value g (i) from the node where the detection point i is located to the end point is as follows:
the above equation is a generalized recursive calculation of the actual cost value g (n) from the i-th node to the end point in the reverse path search process. Because the reverse search (starting to search the path from the end point) algorithm design is adopted, the i-1 th node on the path is the father node of the i-th node, and the G (i-1) value of the father node and the attribute value P of the protection function element on the current node are only required to be considered when the current node is calculatedD、PCAnd PR|AAnd (4) finishing.
P in the above recursionDCan be obtained according to the simulation of the detection probability field, PCIs generally set to a constant value, PR|AiIt can be calculated based on the assumption of normal distribution by the following formula:
wherein X is a random variable representing the difference between the remaining time TR required for the enemy to complete the task and the system response time RFT, and satisfies:
X＝TR-RFT≥0 (9)
assuming that the random variables TR and RFT obey a normal distribution and are independent of each other, the random variable X also obeys a normal distribution, and the mean and variance of X can be further expressed as:
E(X)＝μx＝E(TR-RFT)＝E(TR)-E(RFT) (10)
(1) the embodiment provides two methods for calculating the TR value under reverse path planning: a TR value calculation based on the weakest path and a TR value calculation based on the smallest delay path.
(1-1) a method of calculating a TR value based on the weakest path.
The remaining time TR required by the enemy task to complete consists of two parts, namely time delay t brought by the remaining distance of the enemy invasion pathtrAnd time delay t introduced by the delay functiond。
Assuming that the distance from the current node to the end point on the path is l, and the average travelling speed of the adversary is v, then:
at this time, if there are k delay function protection elements on the path after the current node, acc _ t is accumulateddComprises the following steps:
the final TR value should be the sum of both:
TR＝ttr+acc_td (14)
generalizing the above calculation formula, the TR value calculation of the ith node can be obtained by stacking the TR values of its father nodes:
in the formula, TRi-1The time remaining for the enemy task of the father node to complete; l is the distance between the ith and the (i-1) th nodes; t is tdiDelay brought by the protection element of the delay function at the ith node, if there is no delay element at the ith node, tdi＝0。
The above TR value calculation method based on the weakest path provided by this patent is performed according to the determined path having the minimum interception probability from the end point to the parent node of the current node, because the algorithm is a backward search from the end point, when the ith node is checked, the path having the minimum interception probability from the parent node (i-1 st node) to the end point is determined.
(1-2) a method of calculating a TR value based on a minimum delay path.
The TR value calculation method of the minimum time delay path provided by the patent determines the minimum time consumed by a target node to all nodes through breadth-first search, and therefore a static residual time parameter distribution field required by an adversary to complete tasks is constructed, and the problems that the TR calculation is complex and difficult to implement due to specific path selection can be effectively avoided.
The TR value calculation method for the minimum delay path provided in this patent may refer to equations (14) and (15) above to perform calculation on the remaining time TR required for the enemy task at the ith node to complete, but is different from the TR value calculation method based on the weakest path provided in the foregoing, the path selected by the two calculation methods is different.
The method for calculating the TR value based on the weakest path selects the path with the minimum interception probability between the current node and the terminal point as the reference basis for calculating the TR value, namely the path with the minimum system interception probability is used as l and tdiThe basis of the calculation of (2).
The TR value calculation provided by the method is based on the path with the minimum time delay from the current node to the end point as a reference basis, namely the path with the minimum time delay is used as l and tdiThe basis of calculation of (2); and the minimum time delay path and the static field distribution of the minimum TR value between different nodes and the target protection end point are obtained by traversing, calculating and constructing the end point layer by layer and outwards through a breadth-first search algorithm.
(2) The embodiment provides an RFT calculation method based on the shortest path.
According to the system design input, the starting point of the general response force and the end point position of the protection target are determined, and after the response force is communicated with the alarm evaluation center, the response force is prepared and integrated in the shortest time and reaches the protection target in the shortest travel route. The method assumes that the delay function of the system does not act on the response force during its travel, and therefore does not need to consider the action-impeding effect of the delay function on the response force. The system response time RFT can be directly reduced to the time t required for preparing and integrating response forceempApplied path travel time ttrNamely, the following steps are provided:
RFT＝temp+ttr (16)
in the formula, tempGiven based on the simulation data assumptions, ttrAnd determining the shortest distance path from the response force to the target point, wherein the RFT calculation method takes the Euclidean distance from the current node to the target node as a heuristic function and realizes the search of the shortest distance path through an A-algorithm.
S1024, heuristic estimation function H (n).
The reverse weakest path identification method based on the physical protection system structural parameter two-dimensional visualization model provided by the patent takes the straight line shortest distance from the current node to the terminal point (enemy intrusion starting point) as heuristic information. Because the weakest path search algorithm takes the system interception probability as the cost value evaluation index, the distance needs to be converted into a corresponding probability index parameter form in the process of solving the heuristic function. When the method is used for calculating the heuristic estimation function H, no protection functional element is assumed on the shortest path from the current node to the end point, and only the travel delay is considered. The reverse path search heuristic function H (n) designed by the method is as follows:
H(n)＝k·H_P(R|A) (17)
in the formula, k represents an influence factor of the heuristic function estimated value, so as to ensure that the cost estimated value of the heuristic function is not larger than the actual cost value, and can also be simply understood as the detection probability and the communication probability of the unknown road section from the current node to the target protection terminalTaken together, like the system interception probability PIAnd (4) calculating. H _ P(R|A)The conversion variable of the shortest path distance to the probability evaluation index is obtained by calculation according to the formula (8). The calculation of the heuristic function H is finally converted into the probability P of interrupting the enemy intrusion in response to the strength(R|A)Calculation of a value, P(R|A)The probability value is determined by the ratio of the system response time RFT to the time TR remaining for the adversary to complete the task. As can be seen from equation (16), the system response time RFT is determined, and only the remaining time TR required for the adversary to complete the task needs to be determined.
Assume that the coordinate value of the origin (the origin of enemy intrusion) is (x)0，y0) If the coordinate value of the current node is (x, y), the linear distance l from the starting point to the current node is:
according to the average speed v of the enemy, the distance between the nodes can be directly converted into time, and meanwhile, the distance of the determined path is considered, and a heuristic function of the remaining time required by the enemy to complete the task is constructed:
in the formula, TRiRepresenting the time left from the current node to the target protection end point for the enemy to complete the task, l/v representing the time required for the enemy to travel from the starting point to the current node, lambda1And λ2Are each TRiAnd an amplification factor of l/v by lambda1And λ2The modification of the influence factors of (a) enables the adjustment of the degree of action of the different terms on the heuristic function. As can be seen from the above equation (8), the larger the TR value is, the larger P(R|A)The larger the value is, so the lambda should be enlarged as much as possible when designing the heuristic function1And λ2The value of (c). H obtained by estimating heuristic functionTRSubstituting equation (8), i.e.:
X＝HTR-RFT
respectively taking HTRThe variance of the RFT is calculated by substituting the variance of the RFT and the variance of the RFT into the following equation(R|A)The value:
since the two TR calculation methods provided by the patent are slightly different in inspiration, the influence factors k and lambda are carried out1And λ2The selection of (A) is determined by combining a specific TR calculation method.
S103, identifying the weakest path of the physical protection system through a reverse path heuristic search algorithm based on graph theory.
Example 2:
before implementing the weakest path identification method of the physical protection system provided by the embodiment, the following modeling analysis assumptions are made clear:
suppose I: the detection range of the detector is a circular area with the detection midpoint as the center of a circle, the detection probability is inversely proportional to the detection distance, the detector cannot detect nodes behind the obstacle, and the maximum value of the detection probability is taken for the detection area with the overlapped coverage. Infrared detection devices/access control systems, sensors on doors and windows, and other devices with detection functions in a small range are set to only function in certain specific grids, and the range outside the grids is not affected.
Suppose II: the enemy can go to the adjacent grid nodes along eight directions of up, down, left, right, left-up, right-up, left-down and right-down, and is allowed to pass through the diagonal line to reach other grid areas.
As shown in fig. 4, the weakest path identification method of the physical protection system takes a hypothetical physical protection system facility as an example, designs a two-dimensional physical structure model of the system constructed by using a matchlibrary of Python based on a plan view thereof, and constructs a system time field and a detection probability field by laying out protection function elements such as system detection, delay, response and the like, thereby laying a model foundation for system effectiveness analysis.
1. The two-dimensional structure parameter visualization modeling process of the material protection system specifically comprises the following steps:
(1-1) system data design input analysis: taking a certain protection facility in a laboratory as an example, a two-dimensional plan view of an imaginary PPS system is constructed, the facility protection area of the embodiment has the length of 128 meters and the width of 125 meters, the periphery of the whole area is provided with a fence corresponding to the outermost black thick line in the figure, and the inside of the facility protection area comprises four main buildings which respectively correspond to the eight, the nine, the three and the three in the figure,
The reference numerals are used to designate the parts of the document,
the water pool is a water pool, a protection target (radioactive source) is positioned in the No. 1 building to form a target invaded by an enemy, response force is deployed in the lower left corner area of the No. 4 building, and the enemy initiates an attack from a certain position point at the right end of the periphery of the protection area.
In fig. 4: firstly, fencing; ② building; ③ detecting points and detecting range; an entrance and an exit; -target protection point (source position); sixthly, responding to the force deployment point; seventhly, the position of the starting point of the invasion of the enemy; building No. 1; ninthly-2 building; building # r-3;
-building No. 4;
-a basin.
(1-2) for the two-dimensional plan view structure of the above embodiment, the homogenization grid division is adopted to divide the whole protection area of the facility into a 78 × 80 two-dimensional square grid matrix, and each grid corresponds to a node. Each small grid in the figure is a small square with the side length of 1, and the actual distance corresponding to the side length of the square is 1.6 m. During system modeling analysis, supposing that the invasion targetability of enemies is strong, namely the enemies cannot enter other buildings in the invasion action, other buildings are regarded as obstacles in a system two-dimensional structure model, and people only need to care about the layout condition in the key buildings and do not perform detailed modeling on the interiors of other buildings.
And (1-3) deploying the protection function elements (such as detection, delay and response functions) of the PPS system, and arranging detection devices such as infrared and cameras at key and proper positions such as entrances, exits, windows and the like in the whole protection area.
And (1-4) calculating and forming a distribution field such as system detection probability and time parameters through a relevant theoretical model, and calculating the system detection probability field according to the arrangement condition of the detection functional elements. In this embodiment, it is assumed that the probability that an adversary is detected by a detector in the detection range of the detector is inversely proportional to the linear distance between the grid and the detector, and the detection probability of a region outside the detection range is 0, and a specific probability field is calculated by the following formula:
PD(x)＝-0.045x+0.5 (20)
the detection probability of the intrusion detection device and the entrance guard system which are arranged at each entrance and door and window position of the fence and the building is PD＝0.6。
Meanwhile, the above intrusion detection device and the gate system also have a delay function, and it is assumed that the time delay provided by the above intrusion detection device and the gate system is 30s, that is, an additional 30s of time is required to pass through the nodes: t is td＝30s。
In addition, a parameter is set such that the communication probability is set to PC0.9, response team preparation and integration time tempThe average speed of travel of the response troop is 2.5m/s at 20s, and the normal distribution time deviation of the time TR required for the enemy to complete the mission and the system response time RFT is calculated by 30%, that is, the deviation is
(5) The system parameter information is overlapped and integrated into a system two-dimensional structural gridding model, and data is analyzed and rendered by means of a Matplotlib library in Python to form a system two-dimensional structural parameter visualization model. The two-dimensional structural parameter visualization modeling of the PPS system is not only limited to the method adopted by the embodiment, but also can be achieved through other data processing programs or scene modeling software.
2. The weakest path identification method of the two-dimensional structure parameter visualization model based on the physical protection system specifically comprises the following steps:
(2-1) according to different calculation methods of TR (time remaining) required by an adversary to complete tasks, the embodiment respectively adopts the following heuristic function influence factors to perform inverse heuristic search and identification analysis on the weakest path of the case system: a) the influence factor of the heuristic function corresponding to the TR value calculation method based on the weakest path is lambda1＝2，λ 23, k is 0.2; b) the influence factor of the heuristic function corresponding to the TR value calculation method based on the minimum time delay path is lambda1＝5，λ2＝3，k＝0.2。
And (2-2) analyzing the system by using a target protection point (the position of the radioactive source) as a path search starting point and applying a reverse path heuristic search algorithm shown in the figure 1, wherein the path search process uses the system interception probability as a cost value to realize the preferential search of the weakest path of the system. As shown in fig. 6, the system interception probability cost value F is 0.7426 obtained along the weakest path based on the inverse heuristic search result obtained by the TR calculation method for the weakest path, and the cumulative detection probability P on the weakest path of the systemD0.99938, the number of meshes the path passes through is 123, and the number of meshes the algorithm searches through (the number of meshes in CloseList) is 3254. Compared with the analysis result (the number of traversed grids is 3369) of the non-heuristic algorithm (Dijkstra algorithm) in FIG. 5, the number of traversed nodes of the reverse path heuristic search algorithm provided by the patent is obviously less, and the high efficiency of the designed heuristic function is reflected to a certain extent.
As shown in fig. 7, the system interception probability cost value F is 0.7368 along the path based on the inverse heuristic search result obtained by the TR value calculation method for the minimum delay path, and the cumulative probing probability P on the pathD0.99495, the algorithm searches through a number of grids of 3460. Compared with a non-heuristic search algorithm (Dijkstra algorithm), the method can be used for searchingThe nodes searched by the heuristic function based on the minimum TR value estimation are increased, but the peripheral intrusion path of the protection area under the Dijkstra algorithm can be corrected more reasonably. In addition, compared with a reverse path heuristic search algorithm under the TR calculation method of the weakest path, the cost value of the system interception probability obtained based on the heuristic function estimated by the minimum TR value is smaller, because an adversary directly selects a path with the shortest time spent on going to a target point after realizing that the intrusion behavior is detected, namely the response time left for the system is shorter, the physical protection system is easier to be defeated by the adversary at the moment, and the situation is more suitable for the intrusion behavior developed on the premise that an internal threat source is colluded or an external intruder has a good knowledge of the internal advancing path of the physical protection facility. Generally, the enemy still follows the principle of avoiding harm and benefiting and does not act hastily to increase the possibility of task completion before being unaware of the detection and discovery of the enemy.
It is noted that, although the reverse optimal path search algorithm provided by the present invention needs to specify a start point of enemy intrusion in advance, as long as the intrusion start point is located outside the field, the weakest path of the system obtained by the present invention is only different outside the protective facility fence and is consistent inside the fence, the entry and exit position of enemy entering the protective facility and the selection of subsequent paths are the same, and the obtained system interception probability P isIThe values are completely consistent, so that the reverse heuristic path search algorithm provided by the invention selects the starting point of intrusion to a certain extent without depending on expert experience, and the algorithm can identify the primary gateway of an adversary entering a protection facility according to the specific situation of the physical protection system.
The invention takes a real object protection system as a research object, utilizes the grid cell division of a protection area to establish a two-dimensional structure gridding model of the system according to the design data and parameter input of the system, embeds the performance parameter calculation of protection function elements such as system detection, delay and response and the like and the superposition rendering of analysis data to form a PPS system two-dimensional structure parameter visualization model, and takes the system interception probability as a cost value and takes the current node to form a PPS system two-dimensional structure parameter visualization modelThe distance between the target protection end points is used as heuristic information, and the remaining time TR required by the enemy to complete the task and the system interception probability P are executedIBy a graph theory based reverse iterative search to preferentially select the interception probability PIThe lowest adjacent node, until the target protection end point, is stored in the minimum P in CloseList through the path selection processIAnd the index relationship between the value node and the father node is reversely reconstructed to generate the weakest path of the system, so that the effectiveness analysis of the physical protection system is realized, and the optimization and the improvement of the system design scheme are completed.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
It should be noted that although the method operations of the above-described embodiments are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Example 3:
the embodiment provides a weakest path recognition device of a physical protection system, which comprises a generation module, a design module and a recognition module, wherein:
the generation module is used for performing two-dimensional structure parameter visual modeling on the physical protection system to generate a system structure parameter visual model;
the design module is used for designing a reverse path heuristic search algorithm based on a graph theory by taking the system interception probability as a cost value based on a system structure parameter visualization model; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and the identification module is used for identifying the weakest path of the physical protection system through a reverse path heuristic search algorithm based on graph theory.
The specific implementation of each module in this embodiment may refer to embodiment 1, which is not described herein any more; it should be noted that, the apparatus provided in this embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.
Example 4:
the present embodiment provides a computer device, which may be a computer, as shown in fig. 8, and includes a processor 802, a memory, an input device 803, a display 804 and a network interface 805 connected by a system bus 801, the processor is configured to provide computing and control capabilities, the memory includes a nonvolatile storage medium 806 and an internal memory 807, the nonvolatile storage medium 806 stores an operating system, computer programs and a database, the internal memory 807 provides an environment for the operating system and the computer programs in the nonvolatile storage medium to run, and when the processor 802 executes the computer programs stored in the memory, the thinnest path identification method of the above embodiment 1 is implemented, as follows:
performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model;
based on a system structure parameter visualization model, taking system interception probability as a cost value, and designing a graph theory-based reverse path heuristic search algorithm; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and identifying the weakest path of the physical protection system by a graph theory-based reverse path heuristic search algorithm.
Example 5:
the present embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program, and when the computer program is executed by a processor, the method for identifying the weakest path in embodiment 1 above is implemented as follows:
performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model;
based on a system structure parameter visualization model, taking system interception probability as a cost value, and designing a graph theory-based reverse path heuristic search algorithm; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and identifying the weakest path of the physical protection system by a graph theory-based reverse path heuristic search algorithm.
It should be noted that the computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In summary, the present invention provides a material objectA method for identifying the weakest path of protection system features that two different methods for calculating the residual time TR value needed by enemy task under reverse path planning are proposed, that is, the calculation of TR of weakest path and the calculation of minimum residual time TR are used to solve the problem of unknown TR and P caused by unknown subsequent intrusion path of forward search relative nodeIValue-independent computation and P in reverse search procedure EASIIThe analysis theory is not applicable any more, the designed heuristic function can effectively provide the global optimal solution of the weakest path of the system, and the non-heuristic weakest path deviation result can be effectively corrected. Moreover, the number of nodes traversed by the designed heuristic function in the searching process is obviously less than that of the nodes traversed by the conventional path searching algorithm, and the execution efficiency of the algorithm is higher.
The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the scope of the present invention.
Claims (10)
1. A method for identifying the weakest path of a physical protection system is characterized by comprising the following steps:
performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model;
based on a system structure parameter visualization model, taking system interception probability as a cost value, and designing a graph theory-based reverse path heuristic search algorithm; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and identifying the weakest path of the physical protection system by a graph theory-based reverse path heuristic search algorithm.
2. The method for identifying the weakest path of claim 1, wherein each grid in the system structure parameter visualization model corresponds to a node, a target protection node is used as a starting point, and a starting position point corresponding to enemy attack is an end point;
the method for designing the graph theory-based reverse path heuristic search algorithm based on the system structure parameter visualization model by taking the system interception probability as a cost value specifically comprises the following steps:
adding the starting point into an OpenList, taking the system interception probability as the cost value of the nodes, and initializing the cost value of each node to be 0;
judging whether the OpenList is empty or not, if so, failing to search, and ending the algorithm search; if not, then:
selecting the node with the minimum replacement value from the OpenList as the node N to be processed currentlya；
Judging the current node NaWhether it is the termination point NeIf so, reconstructing a reverse path according to the nodes in the CloseList and the parent node index information of the nodes to obtain the weakest path of the system, and finishing the algorithm search; if not, then:
the current node NaDeleting the OpenList and adding the OpenList and the CloseList;
obtaining the current node N based on graph theory data structure searchaAll adjacent nodes N ofnAnd access the current node N one by one according to the access ruleaAdjacent node N ofn；
Judging adjacent node NnIf not, accessing next adjacent node and using the next adjacent node as the current adjacent node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation; if yes, then:
judging adjacent node NnIf the current neighbor node is in the CloseList, if so, accessing a next neighbor node, and taking the next neighbor node as the current neighbor node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation; if not, then:
judging adjacent node NnWhether in OpenList or not：
If not, the current node N is determinedaSet as its adjacent node NnParent node N ofpCalculating the neighbor node NnAnd will be adjacent to node NnAdding OpenList, and continuously accessing the next adjacent node; taking the next adjacent node as the current adjacent node NnAnd returns to judge the adjacent node NnIf yes, continuing to execute the subsequent operation;
if so, judging that the current node N is passed throughaTo the adjacent node NnIf the actual cost value is smaller, the current node N is determined to be the node NaSet as adjacent node NnParent node N ofpAnd recalculating and updating the neighbor node NnReturning to judge whether the OpenList is empty or not, and continuously executing subsequent operations; if not, returning to judge whether the OpenList is empty or not, and continuing to execute subsequent operations.
3. The weakest path identification method of claim 2, wherein the neighboring node NnThe cost value F is calculated by a cost function F (n), and the expression of F (n) is as follows:
F(n)＝G(n)+H(n)
wherein n is a current node to be detected, g (n) represents an actual cost value consumed for reaching the current node to be detected from an initial position along a generated path, h (n) represents an estimated cost value consumed for reaching a terminal point from the current node to be detected, and h (n) is called a heuristic function;
when the algorithm is used for the heuristic search design of the weakest path of the physical protection system, the conditions which must be met include:
for any node x, y, all paths from the starting point to the node y after passing through the node x and then to the end point and paths from the starting point to the end point directly through the node x satisfy the following inequality:
G(x)+H(x)≤G(y)+H(y)
suppose G*(n) represents the actual cost value from the starting point to the current node, H*(n) represents the actual cost from the current node to the endpointThe values then are:
F*(n)＝G*(n)+H*(n)
satisfies the following conditions:
H(n)≤H*(n)。
4. the method for identifying the weakest path according to claim 1, wherein the TR calculation method comprises the TR calculation based on the weakest path and the TR calculation based on the minimum delay path, and specifically comprises:
the calculation expression of TR for the ith node is as follows:
wherein, TRi-1The remaining time required for the enemy task of node i-1 to complete; l is the distance between the ith and the (i-1) th nodes; t is tdiDelay brought by the protection element of the delay function at the ith node, if there is no delay element at the ith node, tdi＝0；
The calculation of TR based on the weakest path is based on the determined path with the minimum system interception probability from the terminal point to the parent node of the current node as l and tdiThe basis of calculation of (2);
the calculation of the TR based on the minimum time delay path is that the path with the minimum time delay from the current node to the terminal point is used as l and tdiThe path of the minimum time delay is obtained by a calculation structure of traversing the end point layer by layer outwards through a priority search algorithm.
5. The method for identifying the weakest path according to claim 1, wherein the graph theory based inverse path heuristic search algorithm further comprises a calculation method of system response time RFT, wherein the calculation method of RFT is specifically:
assuming that the delay function of the system does not work during the travel in response to the force, the calculation expression of the RFT is as follows:
RFT＝temp+ttr
wherein, tempTime required for aggregation; t is ttrDetermining the path travel time according to the shortest distance from the response force to the target point;
the shortest distance path is realized by an A-algorithm by taking the Euclidean distance from the current node to the target node as a heuristic function.
6. The weakest path identification method of claim 1, wherein the optimized heuristic function h (n) specifically comprises:
constructing a heuristic function of the time left for an adversary to complete a task:
wherein v is the average speed of the enemy; TR (transmitter-receiver)iThe time remaining for the enemy to complete the task from the current node to the target protection terminal; lambda [ alpha ]1And λ2Are each TRiAnd the amplification factor of l/v by modifying lambda1And λ2The value of (c), the adjustment of the degree of action of the heuristic function is achieved;
calculating the obtained RFT and HTRSubstituting the following formula to obtain a mean value X:
X＝HTR-RFT
respectively reacting obtained HTRAs the respective variance and X, for 30% of RFT, into the following equation:
h _ P to be obtained(R|A)Substituting the following formula to obtain the optimized heuristic function:
H(n)＝k·H_P(R|A)
where k is the influencing factor of the heuristic function's pre-evaluation, H \P(R|A)To realize the conversion variable of the shortest path distance to the probability evaluation index.
7. The method for identifying the weakest path according to any one of claims 1 to 6, wherein the performing two-dimensional structural parameter visualization modeling on the physical protection system to generate a system structural parameter visualization model specifically comprises:
inputting design information of a physical protection system, and determining relevant parameters of a target protection facility;
carrying out grid division on the whole protection area of the target protection facility to generate a system two-dimensional grid planar model;
deploying protection function elements of the physical protection system to complete the construction of a system detection probability field and a time parameter distribution field;
and superposing related attribute parameters and distribution fields of the protection function elements of the physical protection system to a two-dimensional gridding planar model of the system to generate a system structure parameter visualization model.
8. An apparatus for identifying a weakest path of a physical object protection system, the apparatus comprising:
the generation module is used for performing two-dimensional structure parameter visual modeling on the physical protection system to generate a system structure parameter visual model;
the design module is used for designing a reverse path heuristic search algorithm based on a graph theory by taking the system interception probability as a cost value based on a system structure parameter visualization model; the graph theory-based reverse path heuristic search algorithm comprises a calculation method of residual time TR required by enemy tasks under different reverse path plans and an optimized heuristic function;
and the identification module is used for identifying the weakest path of the physical protection system through a reverse path heuristic search algorithm based on graph theory.
9. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the weakest path identification method of any one of claims 1-7.
10. A storage medium storing a program, wherein the program, when executed by a processor, implements the weakest path identification method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110690073.8A CN113496547A (en) | 2021-06-22 | 2021-06-22 | Method, device, equipment and medium for identifying weakest path of physical protection system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110690073.8A CN113496547A (en) | 2021-06-22 | 2021-06-22 | Method, device, equipment and medium for identifying weakest path of physical protection system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113496547A true CN113496547A (en) | 2021-10-12 |
Family
ID=77997349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110690073.8A Pending CN113496547A (en) | 2021-06-22 | 2021-06-22 | Method, device, equipment and medium for identifying weakest path of physical protection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113496547A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833699A (en) * | 2009-03-12 | 2010-09-15 | 北京博懋易通科技有限公司 | Heuristic route segment path-finding method for ship route design |
| KR20120062345A (en) * | 2010-12-06 | 2012-06-14 | 한국전자통신연구원 | Apparatus and method reconstructing 3d model |
| CN108444482A (en) * | 2018-06-15 | 2018-08-24 | 东北大学 | A kind of autonomous pathfinding barrier-avoiding method of unmanned plane and system |
| CN109598374A (en) * | 2018-11-21 | 2019-04-09 | 华南理工大学 | A kind of heuristic efficiency analysis method of key facility physical protection system |
| CN111178684A (en) * | 2019-12-10 | 2020-05-19 | 中国人民解放军96901部队23分队 | Weak path determination method for nuclear facility physical protection system |
| CN111190711A (en) * | 2019-12-13 | 2020-05-22 | 南京理工大学 | Multi-robot task allocation method combining BDD with heuristic A-search |
| CN112666948A (en) * | 2020-12-21 | 2021-04-16 | 燕山大学 | Autonomous underwater vehicle path planning method based on channel modeling |
-
2021
- 2021-06-22 CN CN202110690073.8A patent/CN113496547A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833699A (en) * | 2009-03-12 | 2010-09-15 | 北京博懋易通科技有限公司 | Heuristic route segment path-finding method for ship route design |
| KR20120062345A (en) * | 2010-12-06 | 2012-06-14 | 한국전자통신연구원 | Apparatus and method reconstructing 3d model |
| CN108444482A (en) * | 2018-06-15 | 2018-08-24 | 东北大学 | A kind of autonomous pathfinding barrier-avoiding method of unmanned plane and system |
| CN109598374A (en) * | 2018-11-21 | 2019-04-09 | 华南理工大学 | A kind of heuristic efficiency analysis method of key facility physical protection system |
| CN111178684A (en) * | 2019-12-10 | 2020-05-19 | 中国人民解放军96901部队23分队 | Weak path determination method for nuclear facility physical protection system |
| CN111190711A (en) * | 2019-12-13 | 2020-05-22 | 南京理工大学 | Multi-robot task allocation method combining BDD with heuristic A-search |
| CN112666948A (en) * | 2020-12-21 | 2021-04-16 | 燕山大学 | Autonomous underwater vehicle path planning method based on channel modeling |
Non-Patent Citations (2)
| Title |
|---|
| KLAUS JULISCH 等: "Clustering intrusion detection alarms to support root cause analysis", 《ACM TRANSACTIONS ON INFORMATION AND SYSTEM SECURITY》, vol. 6, no. 4, 1 November 2003 (2003-11-01), pages 443 - 471, XP058106789, DOI: 10.1145/950191.950192 * |
| 李梦堃: "核设施场景中行走路径规划技术研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, 15 June 2018 (2018-06-15), pages 040 - 57 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Multitarget tracking in clutter: Fast algorithms for data association | |
| US11300964B2 (en) | Method and system for updating occupancy map for a robotic system | |
| CN113219992B (en) | Path planning method and cleaning robot | |
| CN101867498A (en) | Network security situation evaluating method | |
| CN113219993A (en) | Path planning method and cleaning robot | |
| Zou et al. | Evaluation of vulnerable path: Using heuristic path-finding algorithm in physical protection system of nuclear power plant | |
| CN113865589A (en) | Long-distance rapid path planning method based on terrain slope | |
| CN113496547A (en) | Method, device, equipment and medium for identifying weakest path of physical protection system | |
| Kloder et al. | Barrier coverage for variable bounded-range line-of-sight guards | |
| Bernardini et al. | An optimization approach to robust goal obfuscation | |
| Kleiner et al. | Hierarchical visibility for guaranteed search in large-scale outdoor terrain | |
| Yu et al. | Cyber detectives: Determining when robots or people misbehave | |
| Monari et al. | Dynamic sensor selection for single target tracking in large video surveillance networks | |
| Li et al. | A dual approach to multi-dimensional assignment problems | |
| Sheikhi et al. | Solving the k-coverage and m-connected problem in wireless sensor networks through the imperialist competitive algorithm | |
| Kim et al. | Intruder capturing game on a topological map assisted by information networks | |
| Kochedykov et al. | Method of analysis the functional stability of the software of the infocommunication system in cyber-attack conditions | |
| Bine et al. | Coverage Path Planning for Internet of Drones | |
| Zhang et al. | Radioactive source search problem and optimisation model based on meta-heuristic algorithm | |
| Date et al. | Modeling space to support use-pattern simulation in buildings | |
| Semenov et al. | Global path planning in 4D environments using topological mapping | |
| Brown et al. | Multi-layered security investment optimization using a simulation embedded within a genetic algorithm | |
| Gaskell et al. | Sensor models and a framework for sensor management | |
| Spönemann et al. | Counting crossings for layered hypergraphs | |
| Hou et al. | Topological area graph generation and its application to path planning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |