CN115086198A - Reliability analysis method and system based on network digital twin body - Google Patents
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Abstract
The invention relates to a reliability analysis method and system based on a network digital twin body, belongs to the technical field of network reliability analysis, and solves the problem that the existing network reliability analysis method is not suitable for calculating the overall communication reliability of the network digital twin body. The method comprises the following steps: mapping the network entity into a network digital twin; dividing a network digital twin into a plurality of network modules, and acquiring the reliability of each network module and the reliability of a link between two adjacent network modules; obtaining the reliability of the modularized network digital twin body formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules; determining the comprehensive reliability of the network digital twin body based on the coverage rate of each node and the reliability of the modular network digital twin body; the integrated reliability is taken as the reliability analysis result of the network entity.
Description
Technical Field
The invention relates to the technical field of network reliability analysis, in particular to a reliability analysis method and system based on a network digital twin body.
Background
In recent years, the internet and mobile internet technology is gradually developed and matured, and new application modes are continuously expanded in various industry fields; meanwhile, new technologies and fields such as artificial intelligence, big data, digital twins, block chains and the like are diversified, and a technical foundation is laid for the future development of the internet and the mobile internet era. The new virtual world and the content service application mode thereof not only greatly expand social entertainment scenes, but also provide new solutions and schemes for digital transformation in various industrial fields in China.
In the industrial field, compared with the entertainment and social fields, the digital modeling and digital twin technology oriented to the virtual world has relatively higher requirements on the capability of a digital product for truly simulating the system performance of the physical world; in addition, industrial field systems and products have stringent performance requirements and require products with better stability. Therefore, advanced simulation verification is carried out on complex products and systems based on a meta-universe framework, products of all production cooperation units are integrated together in the meta-universe through virtual people in a virtual world and robots in a physical world for overall analysis, performance and reliability of the products are evaluated through a digital twin technology, and the method is a future development trend for reducing cost and improving product research and development efficiency. In order to meet the above requirements, it is necessary to consider that the system function operation and performance analysis are performed at the conceptual design stage when the system is not yet finalized.
Reliability analysis is an important means for measuring system performance under the action of uncertainty factors. The reliability reflects the capability of the network digital twin system to complete the established service function under certain conditions, is the internal reflection of the operation situation of the network system, and is the centralized reflection of the comprehensive performance of the network system. The existing research shows that the network system with high reliability not only can obviously reduce the cost of self operation and maintenance, but also can greatly improve the service efficiency. Reliability has become the basis and important component for planning and designing the whole system based on the network digital twin body and making schemes for system operation, maintenance, management and the like. For a system manager and an operator, the system reliability assessment is helpful for scientifically mastering the operation situation of the network system, is helpful for accurately identifying weak links influencing the system reliability, and is also helpful for determining a reasonable and effective scheme to improve the reliability level of the system. The system reliability is concerned by system managers and operators, and research on the aspect can be carried out for exploring a new system reliability evaluation method, so that the system reliability evaluation method has important theoretical value and practical significance.
For a generalized network digital twin body, a constituent node of the network digital twin body is a system with a complex structure and multiple functions, not only is the design and development technology advanced, but also the design is quite complex in order to meet certain functions, and the design brings multiple uncertain factors to the reliability guarantee of the whole network, thereby bringing severe test to the system design. In addition, with the development of technologies in various aspects, the performance and service requirements of people on the network are gradually improved, the system composition is more complicated, and the network digital twin behavior is also developed towards the direction of complication.
Most of the current researches on the network reliability are based on network node individuals, and the researches on the overall reliability of the network are less. For example, Castet J F uses statistical data to analyze and model the reliability of nodes. Peng Z proposes a logical specification of formal modeling of individual nodes and their reliability, availability and maintainability, and automated quantitative analysis of these characteristics using probabilistic model inspection prisms. Guo proposes a Bayesian/Markov chain Monte Carlo method for node reliability modeling and proves that it is more suitable for Weibull models than the classical MLE method. In dynamic fault analysis, the reliability of the node is analyzed by using a method of a static sub-tree and a method of a dynamic sub-tree. Pellisetti M combines an uncertainty parameter model and a non-parameter model, and discusses the reliability problem of the node structure under low-frequency harmonic excitation.
In the prior art, the research on network digital twins is less, and the qualitative analysis and the comprehensive evaluation of indexes are mainly focused. With respect to studies on network Reliability, Patrick O' Connor et al in Practical Reliability Engineering define Reliability as the ability of a product to perform a desired function within a given time under specified conditions. The probability of reliability is reliability. Accordingly, reliability is defined as the probability that a product will perform a desired function within a given time and under a given condition without failure. The existing literature researches on network reliability mainly comprise end-to-end, K-to-end and all-end reliability, and three conditions of node failure, link failure and simultaneous node and link failure are considered. End-to-end reliability describes the ability to keep connectivity between two nodes in a network; the reliability of the K end is defined as the probability that any two points in a node subset K given in the network are communicated; full-end reliability refers to the ability of all nodes of the entire network to remain connected in the event of a component failure. In addition, research finds that the existing network reliability evaluation analysis is mostly a plane network, such as a computer network and a traffic network.
According to literature research, network reliability assessment is mainly divided into two categories, accurate computation and approximate estimation.
The precise calculation method includes a state enumeration method, a non-intersection sum method, a factorization method, a state space decomposition method and the like. The use of state enumeration to calculate network reliability was first proposed by Moore and Shannon in 1956, the main idea being to calculate network reliability by enumerating all mutual exclusion events that the network can normally operate under specified conditions. The disjointed sum method is a method for calculating the reliability of the network by applying the disjointed sum theorem, and the main idea is to calculate the reliability of the network by representing the reliability of the network as the sum of all minimum sets (or representing the unreliability of the network as the sum of all minimum sets), and then merging the sum into the sum of mutually disjoint terms. Factorization computing network reliability was first proposed by F Moskowitz and h.mine, the basic idea being to decompose a network into two sub-networks according to some criterion and to decompose the sub-networks recursively until they cannot be decomposed, with successive iterations to obtain network reliability. The method for solving the network reliability by the state space decomposition method is proposed by Doulliez and Jamoule in 1972, and the basic idea is to decompose the state space into a set of three states: a set of acceptable states, a set of unacceptable states, and a set of ambiguous states. If the amount of data to be transmitted can be successfully (or cannot be) transmitted from the source point to the sink point under the network limit, the network status is called acceptable (unacceptable) status. Each ambiguous state includes an acceptable state and an unacceptable state. Then Mishra R uses a minimum cut and disjoint sum method to get an expression of the unreliability. Hayashi-Masahiro derives a matrix decomposition formula for calculating the fault frequency of the telecommunication network. Hardy G proposes a network decomposition method for calculating network reliability by using a binary decision diagram. Kuo S Y and Sahinoglu designed a fast algorithm that performs state enumeration in a hybrid fashion over a complex network with the aid of the polish encoding method to compute accurate S-t reliability.
Approximate estimation methods include delimitation methods and simulation algorithms. The network reliability obtained by an approximate algorithm is a method for reducing the calculation difficulty by sacrificing the reliability precision. The main idea of the bounding method is to approximate the exact value of the network reliability by finding the upper and lower boundary values of the network. The simulation method is to approximate the network reliability by simulation technology, and mainly includes Monte Carlo method, intelligent algorithm (such as ant colony algorithm, genetic algorithm, fuzzy genetic algorithm, neural network algorithm, tabu search algorithm, simulated annealing algorithm), etc. SharafatAR proposes a recursive truncation algorithm, a boundary approximation algorithm, for estimating the full-terminal reliability of a given network with a pre-specified accuracy. Manzi E uses the sampling method proposed by fisherman to calculate the 2-terminal and global reliability of the network. Houben proposes a Monte Carlo simulation method based on importance sampling, and solves the problem of the failure probability of 2-K full-end connection of a high-reliability network.
However, these methods are not suitable for calculating the overall connectivity reliability of the network digital twin. On the one hand, the complex system network digital twin has a three-dimensional structure. Unlike planar network architectures, such networks require a re-modeling for analysis. On the other hand, network digital twins need special attention to traffic service coverage performance. Since the network digital twin is serving users within coverage. To calculate the overall connectivity of a network, the coverage characteristics of the network are considered from the user's perspective. Meanwhile, the overall reliability of the network digital twin is improved by considering the multiple coverage characteristics and considering the backup mode of the business service based on the coverage weight.
Disclosure of Invention
In view of the foregoing analysis, embodiments of the present invention are directed to providing a reliability analysis method and system based on a network digital twin, so as to solve the problem that the existing network reliability analysis method is not suitable for calculating the overall connectivity reliability of the network digital twin.
In one aspect, the present invention provides a reliability analysis method based on a network digital twin, including:
mapping a network entity into a network digital twin body, and acquiring the reliability of each node in the network digital twin body and the reliability of a link between two adjacent nodes;
dividing a network digital twin into a plurality of network modules, and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
obtaining the reliability of the modularized network digital twin body formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
simulating the operation of a network entity by using a network digital twin body to obtain the coverage rate of each heavy node;
determining the comprehensive reliability of the network digital twin body based on the coverage rate of each node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
On the basis of the scheme, the invention also makes the following improvements:
further, regarding the network module and the modular network digital twin as a network map, the reliability of the network map is obtained by:
acquiring a state space set of a network graph, wherein each state space in the state space set corresponds to a sub-graph of the network graph;
and obtaining the reliability of the corresponding network graph based on the reliability and the connection probability of all the subgraphs corresponding to the state space set of the network graph.
Further, reliability P of network graph G G Expressed as:
wherein, R (T) G′ ) Representing the connectivity probability of a subgraph G 'of the network graph G, R (T) if the subgraph G' is able to connect all the nodes in the network graph G G′ ) 1, otherwise, R (T) G′ )＝0； P(T G′ ) Representing the reliability of sub-graph G'; t represents the state space set of the network graph G, T G′ Representing the state space of sub-graph G'.
Further, the reliability P (T) of the subgraph G G′ ) Expressed as:
wherein, V G′s 、V G′f Respectively representing node sets in a connected state and a failure state in the state space of the subgraph G'; alpha is alpha vi Represents V G′s Reliability of the vi-th node, α vj Denotes V G′f Reliability of the vj-th node; e G′s 、E G′f Representing a link set in a connected state and a failure state in a state space of the subgraph G'; beta is a el Represents E G′s Reliability of the middle (el) link, beta ek Represents E G′f Reliability of the ef link.
Further, the overall reliability p (b) of the network digital twin is expressed as:
wherein, P (A) x ) Representing X node coverage, wherein X represents total coverage weight; x is not greater than the total number of nodes in the network digital twin;
when x is 1, P (B | a) 1 )＝P(G I )·β；
When x ≠ 1, it is,
P(G I ) The reliability of the modular network digital twin is represented, and beta represents the link reliability between the node and the user terminal.
Further, the coverage rate of each heavy node is obtained by the following method:
taking the sum of the areas covered by each node in the network digital twin as a total service area; in the total service area, simulating the operation of a network entity by using a network digital twin body to obtain the area of each user coverage area in the total service area;
recording the user coverage in the total service area as an event A; event A is divided into a double coverage A 1 Double coverage A 2 Repeating step A x (ii) a D, X repeatedly covering A X (ii) a Then, x node coverage P (A) x ) Expressed as:
P(A x )＝D x /D (4)
wherein D is x Denotes x user coverage area and D denotes total service area.
Further, if the position of each node in the network entity is fixed, simulating the operation of the network entity at any moment by using a network digital twin body to obtain the area of each user coverage area in the total service area;
if the position of each node in the network entity is variable, simulating the operation of the network entity for a period of time by using the network digital twin body, observing each user coverage area once every certain time in the period of time, and taking the average value of the user coverage areas obtained by multiple times of observation as the corresponding user coverage area in the total service area.
Further, the network digital twin body is divided into a plurality of network modules with similar structures.
Further, obtaining link reliability between network modules includes:
acquiring all links between a network node in one network module and a network node in another network module;
obtaining all link events which can enable the one network module and the other network module to be communicated from all the obtained links;
and taking the sum of the reliability of the links of all the link events as the reliability of the link between the one network module and the other network module.
In another aspect, the present invention further provides a reliability analysis system based on a network digital twin, including:
the network digital twin mapping unit is used for mapping the network entity into a network digital twin and acquiring the reliability of each node in the network digital twin and the reliability of a link between two adjacent nodes;
the network module dividing unit is used for dividing the network digital twin into a plurality of network modules and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
the modularized network digital twin reliability acquisition unit is used for acquiring the reliability of the modularized network digital twin formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
the node coverage rate acquisition unit is used for simulating the operation of a network entity by utilizing a network digital twin body to acquire the coverage rate of each heavy node;
the comprehensive reliability obtaining unit is used for determining the comprehensive reliability of the network digital twin body based on the coverage rate of each heavy node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
the reliability analysis method and system based on the network digital twin provided by the invention have the following beneficial effects:
firstly, in the reliability analysis mode of the invention, a network entity is mapped into a computational network digital twin; the method comprises the steps that the operation of a network digital twin simulation network entity is utilized through a simulation network, the coverage rate of each node is obtained, and the comprehensive reliability of the network digital twin considering the coverage characteristics of a user is finally obtained; and the comprehensive reliability is used as the reliability analysis result of the network entity. Therefore, the problem that the existing network reliability analysis method is not suitable for calculating the integral communication reliability of the network digital twin body is effectively solved.
Secondly, in the reliability analysis mode, the reliability calculation process of the network digital twin is simplified and the accuracy of the reliability calculation result is improved by dividing a plurality of network modules.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of a reliability analysis method based on a network digital twin in embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a reliability analysis system based on a network digital twin in embodiment 2 of the present invention;
FIG. 3 is a schematic diagram of a 16-node network digital twin;
FIG. 4 is a diagram illustrating the division results of network digital twins;
fig. 5 is a modular network digital twin network illustration.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example 1
The embodiment 1 of the present invention discloses a reliability analysis method based on a network digital twin, and a flowchart is shown in fig. 1, and includes:
step S1: mapping a network entity into a network digital twin body, and acquiring the reliability of each node in the network digital twin body and the reliability of a link between two adjacent nodes;
when mapping a network entity into a network digital twin, specifically performing:
mapping a node in the network entity to a node in the network digital twin, and mapping the reliability of the node in the network entity to the reliability of a corresponding node in the network digital twin;
mapping a link in a network entity to a link in a network digital twin body, and mapping the reliability of the link between two adjacent nodes in the network entity to the reliability of a corresponding link in the network digital twin body;
step S2: dividing a network digital twin into a plurality of network modules, and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
considering the complexity of the network digital twin, in the present embodiment, the network digital twin is divided into a plurality of network modules for the purpose of performing reduced order calculation for high complexity network reliability.
In a specific implementation process, in order to further simplify a calculation process, the network digital twin body may be divided into a plurality of network modules with similar structures, and since the calculation processes of the reliability of the network modules with similar structures are also similar, the purpose of further simplifying the reliability calculation is achieved.
In a specific implementation process, the network digital twin can be divided into a plurality of network modules according to the following modes:
determining the size of a network module core and the number of stages of the network module according to the complexity of a network formed by the network digital twin; the more the number of nodes and links in the network digital twin is, the higher the complexity is;
(1) if the complexity of the network formed by the network digital twin is low, the size of the network module can be directly used as the size of a network module core, and the stage number of the network module is 1;
dividing adjacent nodes and links between the nodes into corresponding network modules according to the size of the network modules;
for example, for a 4 × 4 mesh network, the number of stages of network modules is 1, that is, the network is divided once to obtain 2 × 2 network modules, and the size of the network module core is defined as the size of the network module 2 × 2;
(2) if the complexity of the network formed by the network digital twin is higher, a multi-level network module can be defined; the size of the first-stage network module is the size of a network module core; the size of the second-level network module is the result of amplifying the first-level network module by a plurality of times; the size of the third-stage network module is a result of amplifying the second-stage network module by a plurality of times; and sequentially executing the division of each level of network modules until the size of a network structure formed by a plurality of highest level network modules is the same as the size of a network module core, and taking the highest level network modules as subsequent network modules for forming network digital twins.
For example, for a 16 × 16 mesh network, the size of the network module core may be defined as 2 × 2; at this time, the size of the first-level network module is 2 × 2, the size of the second-level network module is 4 × 4, and the size of the third-level network module is 8 × 8; at this time, the size of the network structure formed by the 4 third-level network modules with the size of 8 × 8 and the size of the network module core are both 2 × 2; an 8 x 8 network module may be used as a network module for subsequent use in forming a network digital twin.
Dividing adjacent nodes and links among the nodes into corresponding first-level network modules according to the size of a network module core; and then dividing links between adjacent first-level modules into corresponding second-level network modules by taking the first-level network modules as nodes until the highest-level network module is determined.
Obtaining the reliability of a link between two network modules in the following manner, including:
acquiring all links between a network node in one network module and a network node in another network module;
obtaining all link events which can enable the one network module and the other network module to be communicated from all the obtained links;
and taking the sum of the reliability of the links of all the link events as the reliability of the link between the one network module and the other network module.
It should be emphasized that if the network digital twin involves multi-level division, the reliability of the link between the network modules of each adjacent level is also determined in the above manner.
Step S3: obtaining the reliability of the modularized network digital twin body formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
it is noted that both network modules and modular network digital twins can be considered as network diagrams. In the modularized network digital twin network diagram, the network modules are regarded as nodes in the network diagram, and links between two adjacent network modules are regarded as links between two corresponding nodes in the network diagram; in the network graph corresponding to the network module, the nodes in the network module are the nodes in the network graph, and the links between two adjacent nodes are regarded as the links in the network graph. Thus, the manner in which the reliability of the network graph is obtained is collectively described herein:
(1) acquiring a state space set of a network graph, wherein each state space in the state space set corresponds to a sub-graph of the network graph;
it is assumed that the state space of the network graph contains m nodes and h edges, which can be expressed as
State space partitioning of network graph into T 0 And its complement
According to the De-Morgan law, the state can be further decomposed into a complete combination state with the state space elements removed, as shown in the following formula.
Wherein the content of the first and second substances,
T 0 ＝{S 1 ,...,S m ,e 1,2 ,...,e i,j }
each state space in the state space set corresponds to a specific state of the network graph and is called a subgraph; the set of nodes in the state space of subgraph G' is V G′ Wherein the node set in the connected state is V G′s The node set in the failure state is V G′f . The set of link points in the state space of sub-graph G' is E G′ Wherein the link set in the connected state is E G′s The set of links in the failure state is E G′f 。
Because the subgraph may contain invalid nodes and links, in order to judge the connectivity of the network graph after the nodes and links fail, the concept of subgraph connectivity probability is introduced. If sub-graph G' is such that all points in network graph G are connected, the probability of connectivity R (T) for sub-graph G G′ ) 1, otherwise, R (T) G′ )＝0。
(2) And obtaining the reliability of the corresponding network based on the reliability and the connection probability of all the subgraphs corresponding to the state space set of the network graph.
The reliability of the network graph G is represented as:
wherein, P G Representing the reliability of the network graph G, R (T) G′ ) Representing the connectivity probability of a subgraph G 'of the network graph, R (T) if the subgraph G' is able to connect all the nodes in the network graph G G′ ) 1, otherwise, R (T) G′ )＝0；P(T G′ ) Representing the reliability of sub-graph G'; t represents the state space set of the network graph G, T G′ Representing the state space of sub-graph G'.
Reliability P (T) of sub-graph G G′ ) Expressed as:
wherein, V G′s 、V G′f Respectively representing node sets in a connected state and a failure state in the state space of the subgraph G'; alpha is alpha vi Represents V G′s Reliability of the vi-th node, α vj Represents V G′f Reliability of the vj-th node; e G′s 、E G′f Representing a link set in a connected state and a failure state in a state space of the subgraph G'; beta is a el Represents E G′s Reliability of the middle (el) link, beta ek Represents E G′f Reliability of the ef link.
It should be noted that if the sub-graph G' cannot connect all the nodes in the network graph G, then R (T) G′ ) 0; thus, in equation (1), this term R (T) G′ )P(T G′ ) 0. To simplify the calculation process, in practical implementation, only subgraphs that can connect all nodes in the network graph G can be considered, since only these subgraphs have an effect on the reliability of the network graph.
Step S4: simulating the operation of a network entity by using a network digital twin body to obtain the coverage rate of each heavy node;
specifically, in the present embodiment, the coverage of each heavy node is acquired by:
taking the total area covered by each node in the network digital twin as a total service area; in the total service area, simulating the operation of a network entity by using a network digital twin body to obtain the area of each user coverage area in the total service area;
recording the user coverage in the total service area as an event A; event A is divided into a double coverage A 1 Double coverage A 2 Repeating step A x (ii) a D, X repeatedly covering A X (ii) a Then, x node coverage P (A) x ) Expressed as:
P(A x )＝D x /D (3)
wherein D is x Denotes x user coverage area and D denotes total service area.
It should be noted that, in the actual implementation process, if the position of each node in the network entity is fixed, the network entity operation at any time is simulated by using the network digital twin, and the area of each user coverage area in the total service area is obtained; if the position of each node in the network entity is variable, simulating the operation of the network entity for a period of time by using the network digital twin body, observing each user coverage area once every certain time in the period of time, and taking the average value of the user coverage areas obtained by multiple times of observation as the corresponding user coverage area in the total service area.
Step S5: determining the comprehensive reliability of the network digital twin body based on the coverage rate of each node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
Specifically, the overall reliability p (b) of the network digital twin is expressed as:
wherein, P (A) x ) Representing X node coverage, wherein X represents total coverage weight; x is not greater than the total number of nodes in the network digital twin;
when x is 1, P (B | a) 1 )＝P(G I )·β；
When x is not equal to 1, the reaction mixture is,
P(G I ) The reliability of the modular network digital twin is represented, and beta represents the link reliability between the node and the user terminal.
In summary, compared with the prior art, the reliability analysis method in this embodiment maps the network entity into the computational network digital twin; the method comprises the steps that the operation of a network digital twin simulation network entity is utilized through a simulation network, the coverage rate of each node is obtained, and the comprehensive reliability of the network digital twin considering the coverage characteristics of a user is finally obtained; and the comprehensive reliability is used as the reliability analysis result of the network entity. Therefore, the problem that the existing network reliability analysis method is not suitable for calculating the integral communication reliability of the network digital twin body is effectively solved.
Example 2
Embodiment 2 of the present invention discloses a reliability analysis system based on a network digital twin, and a schematic structural diagram is shown in fig. 2, and includes:
the network digital twin mapping unit is used for mapping the network entity into a network digital twin and acquiring the reliability of each node in the network digital twin and the reliability of a link between two adjacent nodes;
the network module dividing unit is used for dividing the network digital twin into a plurality of network modules and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
the modularized network digital twin reliability acquisition unit is used for acquiring the reliability of the modularized network digital twin formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
the node coverage rate acquisition unit is used for simulating the operation of a network entity by utilizing a network digital twin body to acquire the coverage rate of each heavy node;
the comprehensive reliability obtaining unit is used for determining the comprehensive reliability of the network digital twin body based on the coverage rate of each heavy node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
The specific implementation process of the system embodiment of the present invention may refer to the method embodiment described above, and this embodiment is not described herein again.
Since the principle of the present embodiment is the same as that of the above method embodiment, the present system also has the corresponding technical effects of the above method embodiment.
Example 3
The specific embodiment 3 of the invention discloses a specific time delay process of a reliability analysis method based on a network digital twin body, so as to verify the reliability analysis effect of the reliability analysis method in the embodiment 1. In particular, the amount of the solvent to be used,
step S1: taking a network digital twin of 16 nodes as an example, the link connection relationship between the nodes is shown in fig. 3. Node S i Has a reliability of alpha i Reliability of link between node i and node j is beta i,j 。
Step S2: dividing a network digital twin into a plurality of network modules; at this time:
set { S 1 ,S 2 ,S 5 ,S 6 ,e 1,2 ,e 1,5 ,e 2,6 ,e 5,6 Reorganize to a network module SC 1;
set { S 3 ,S 4 ,S 7 ,S 8 ,e 3,4 ,e 3,7 ,e 4,8 ,e 7,8 Recombining the data into a network module SC 2;
set { S 9 ,S 10 ,S 13 ,S 14 ,e 9,10 ,e 9,13 ,e 10,14 ,e 13,14 Recombining the data into a network module SC 3;
set { S 11 ,S 12 ,S 15 ,S 16 ,e 11,12 ,e 11,15 ,e 12,16 ,e 15,16 Recombines into the network module SC 4.
A schematic diagram of the division result of the network digital twin is shown in fig. 4. I.e. the original 4 x 4 mesh network G I ＝{S,E}，
S＝{S 1 ,S 2 ,S 3 ,S 4 ,S 5 ,S 6 ,S 7 ,S 8 ,S 9 ,S 10 ,S 11 ,S 12 ,S 13 ,S 14 ,S 15 ,S 16 }，
E＝{e 1,2 ,e 1,5 ,e 2,6 ,e 5,6 ,e 3,4 ,e 3,7 ,e 4,8 ,e 7,8 ,e 9,10 ,e 9,13 ,e 10,14 ,e 13,14 ,e 11,12 ,e 11,15 ,e 12,16 ,e 15,16 ,e 2,3 ,
e 6,7 ,e 5,9 ,e 6,10 ,e 7,11 ,e 8,12 ,e 10,11 ,e 14,15 }
Reorganized to 2 x 2 mesh network G I′ ＝{SC 1 ,SC 2 ,SC 3 ,SC 4 ,e c1,c2 ,e c1,c3 ,e c2,c4 ,e c3,c4 }
Restructuring into a 2 x 2 mesh network G I′ Referred to as a modular network digital twin network diagram, the modular network digital twin network diagram is intended as shown in fig. 5.
Due to the network module SC 1 -SC 4 The structure of the network module is similar, so that the reliability of each network module can be calculated in the same calculation mode; the network module SC is used below 1 For example, the process of obtaining the reliability of the network module is described:
T 1 ＝{S 1 ,S 2 ,S 5 ,S 6 ,e 1,2 ,e 1,5 ,e 2,6 ,e 5,6 }
Based on the foregoing, it can be seen that only subgraphs that can have all nodes in the network graph connected have an impact on the reliability of the network graph, and therefore only T is considered here 1 And
that is, at this time, the connection probability R (T) of such subgraphs G′ ) 1. At this time, the network module SC 1 Reliability P of SC1 Can be expressed as:
in the same way, the network module SC can be obtained 2 Reliability P of SC2 Network module SC 3 Reliability of (P) SC3 Network module SC 4 Reliability P of SC4 . Network module SC 1 -SC 4 May also be expressed as alpha SC1 -α SC4 (ii) a I.e. alpha SC1 ＝P SC1 ，α SC2 ＝P SC2 ，α SC3 ＝P SC3 ，α SC4 ＝P SC4 ；
Because the network modules SC1 and SC2 pass through the link e in the process of module division 2,3 And e 6,7 Connecting; analysis shows that the link events which make the network modules SC1 communicate with SC2 include:
(1) link e 2,3 And e 6,7 Are all communicated;
(2) link e 2,3 Connectivity, link e 6,7 Failure;
(3) link e 2,3 Failure, link e 6,7 Communicating;
therefore, the reliability of the link between network modules SC1 and SC 2:
β SC1,SC2 ＝β 2,3 β 6,7 +β 2,3 (1-β 6,7 )+(1-β 2,3 )β 6,7 ＝β 2,3 +(1-β 2,3 )β 6,7
in the same way, the reliability of the link between the network modules SC1 and SC 3:
β SC1,SC3 ＝＝β 5,9 +(1-β 5,9 )β 6,10
reliability of the link between network modules SC2 and SC 4:
β SC2,SC4 ＝＝β 7,11 +(1-β 7,11 )β 8,12
reliability of the link between network modules SC3 and SC 4:
β SC3,SC4 ＝＝β 10,11 +(1-β 10,11 )β 14,15
in the network diagram of the modular network digital twin, the network modules may be regarded as nodes in the network diagram, and the links between two adjacent network modules may be regarded as links between two corresponding nodes in the network diagram, where a schematic structure of the modular network digital twin is shown in fig. 5.
The analysis shows that the modularized network digital twin body and the network module SC 1 -SC 4 Are similar in structure, therefore, the calculation process of the reliability of the modularized network digital twin body is referred to the network module SC 1 The calculation process of (2) can be just adjusted by the angle marks of the nodes and the links, and the calculation process is not described again here.
If the reliability of each node in the network digital twin is 0.99 and the reliability of each link is 0.85, the above process is executed to obtain the modulusReliability P (G) of a blocking network digital twin I )＝0.73504513。
Step S4: simulating the operation of a network entity by using a network digital twin body to obtain the coverage rate of each heavy node;
taking the total area covered by each node in the network digital twin as a total service area; in the total service area, simulating twenty-four hours of operation of a network entity by using a network digital twin body, and observing once per minute to obtain the area of each user coverage area in the total service area; node coverage data is finally obtained as shown in table 1.
TABLE 1 node coverage data
Based on the area corresponding to each observed overlapping (for example, for triple overlapping, the sum of the areas covered by the coverage areas of three nodes in the total service area is calculated), the area corresponding to each observed overlapping is averaged, and the average is divided by the total service area to obtain the coverage rate corresponding to each overlapping in the table.
The reliability of the network digital twin at the time of one-to twelve-fold coverage can be obtained based on different user coverage characteristics. The link reliability between a node and a user terminal is represented by beta, and the coverage of x-multiple nodes is represented by P (A) x ) Represents; at this time:
digital twin reliability of a repetitive range network
P(B|A 1 )＝P(G I )·β＝0.73504513×0.85＝0.62478836
Dual coverage network digital twin reliability
P(B|A 2 )＝P(G I )·[1-(1-β) 2 ]＝0.73504513×[1-(1-0.85) 2 ]＝0.7185066
Triple coverage area network digital twin reliability
P(B|A 3 )＝P(G I )·[1-(1-β) 2 ]＝0.73504513×[1-(1-0.85) 3 ]＝0.7325644
Quadruple coverage network digital twin reliability
P(B|A 4 )＝P(G I )·[1-(1-β) 2 ]＝0.73504513×[1-(1-0.85) 4 ]0.7346730 quintuple coverage network digital twin reliability
P(B|A 5 )＝0.73504513×[1-(1-0.85) 5 ]＝0.7349893
Six-coverage network digital twin reliability
P(B|A 6 )＝0.73504513×[1-(1-0.85) 6 ]＝0.7350368
Seven-coverage network digital twin reliability
P(B|A 7 )＝0.73504513×[1-(1-0.85) 7 ]＝0.7350439
Octave coverage network digital twin reliability
P(B|A 8 )＝0.73504513×[1-(1-0.85) 8 ]＝0.7350449
Nine-coverage-range network digital twin reliability
P(B|A 9 )＝0.73504513×[1-(1-0.85) 9 ]＝0.7350451
Ten-fold coverage network digital twin reliability
P(B|A 10 )＝0.73504513×[1-(1-0.85) 10 ]＝0.7350451
Eleven-fold coverage range network digital twin reliability
P(B|A 11 )＝0.73504513×[1-(1-0.85) 11 ]＝0.7350451
Twelve-repeated coverage range network digital twin reliability
P(B|A 12 )＝0.73504513×[1-(1-0.85) 12 ]＝0.7350451
The comprehensive reliability of the digital twin body of the computing network is
Therefore, the overall reliability of the network digital twin is finally determined to be 0.71645508. Further, it is known that the overall reliability of the network entity is 0.71645508.
In conclusion, by adopting the method in the embodiment, the calculation process can be effectively simplified, the calculation complexity is reduced, and the comprehensive reliability calculation result of the network digital twin body with higher accuracy is obtained.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A reliability analysis method based on a network digital twin body is characterized by comprising the following steps:
mapping a network entity into a network digital twin body, and acquiring the reliability of each node in the network digital twin body and the reliability of a link between two adjacent nodes;
dividing a network digital twin into a plurality of network modules, and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
obtaining the reliability of the modularized network digital twin body formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
simulating the operation of a network entity by using a network digital twin body to obtain the coverage rate of each heavy node;
determining the comprehensive reliability of the network digital twin body based on the coverage rate of each node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
2. The method for analyzing the reliability of the network-based digital twin according to claim 1, wherein the network module and the modular network digital twin are both regarded as a network map, and the reliability of the network map is obtained by:
acquiring a state space set of a network graph, wherein each state space in the state space set corresponds to a sub-graph of the network graph;
and obtaining the reliability of the corresponding network graph based on the reliability and the connection probability of all the subgraphs corresponding to the state space set of the network graph.
3. The method for analyzing the reliability of a network-based digital twin according to claim 2, wherein the reliability P of the network map G G Expressed as:
wherein, R (T) G′ ) Representing the connectivity probability of a subgraph G 'of the network graph G, R (T) if the subgraph G' is able to connect all the nodes in the network graph G G′ ) 1, otherwise, R (T) G′ )＝0；P(T G′ ) Representing the reliability of sub-graph G'; t represents the state space set of the network graph G, T G′ Representing the state space of sub-graph G'.
4. The method according to claim 3, wherein the reliability P (T) of the sub-graph G' is determined by the reliability of the network digital twin G′ ) Expressed as:
wherein, V G′s 、V G′f Respectively representing node sets in a connected state and a failure state in the state space of the subgraph G'; alpha is alpha vi Represents V G′s Reliability of the vi-th node, α vj Represents V G′f Reliability of the vj-th node; e G′s 、E G′f Representing a link set in a connected state and a failure state in a state space of the subgraph G'; beta is a el Represents E G′s Reliability of the middle (el) link, beta ek Represents E G′f Reliability of the ef link.
5. The network digital twin-based reliability analysis method according to any one of claims 1-4, wherein the comprehensive reliability P (B) of the network digital twin is expressed as:
wherein, P (A) x ) Representing X node coverage, wherein X represents total coverage weight; x is not greater than the total number of nodes in the network digital twin;
when x is 1, P (B | a) 1 )＝P(G I )·β；
When x ≠ 1, it is,
P(G I ) The reliability of the modular network digital twin is represented, and beta represents the link reliability between the node and the user terminal.
6. The method for analyzing the reliability of the network-based digital twin according to claim 5, wherein the coverage rate of each heavy node is obtained by:
taking the sum of the areas covered by each node in the network digital twin as a total service area; in the total service area, simulating the operation of a network entity by using a network digital twin body to obtain the area of each user coverage area in the total service area;
recording the user coverage in the total service area as an event A; event A is divided into a double coverage A 1 Double coverage A 2 Repeating step A x (ii) a D, X repeatedly covering A X (ii) a Then, x node coverage P (A) x ) Expressed as:
P(A x )＝D x /D (4)
wherein D is x Denotes x user coverage area and D denotes total service area.
7. The network digital twin-based reliability analysis method according to claim 6,
if the position of each node in the network entity is fixed, simulating the operation of the network entity at any moment by using a network digital twin body to obtain the area of each user coverage area in the total service area;
if the position of each node in the network entity is variable, simulating the operation of the network entity for a period of time by using the network digital twin body, observing each user coverage area once every certain time in the period of time, and taking the average value of the user coverage areas obtained by multiple times of observation as the corresponding user coverage area in the total service area.
8. The method for analyzing the reliability of the network-based digital twin according to any one of claims 1 to 4, wherein the network digital twin is divided into a plurality of network modules with similar structures.
9. The network digital twin-based reliability analysis method according to any one of claims 1-4, wherein obtaining link reliability between network modules comprises:
acquiring all links between a network node in one network module and a network node in another network module;
obtaining all link events which can enable the one network module and the other network module to be communicated from all the obtained links;
and taking the sum of the reliability of the links of all the link events as the reliability of the link between the one network module and the other network module.
10. The system for analyzing the reliability of a network-based digital twin according to claim 1, comprising:
the network digital twin mapping unit is used for mapping the network entity into a network digital twin and acquiring the reliability of each node in the network digital twin and the reliability of a link between two adjacent nodes;
the network module dividing unit is used for dividing the network digital twin into a plurality of network modules and respectively acquiring the reliability of each network module and the reliability of a link between two adjacent network modules;
the modularized network digital twin reliability acquisition unit is used for acquiring the reliability of the modularized network digital twin formed by all the network modules and the links between the network modules based on the reliability of each network module and the reliability of the links between two adjacent network modules;
the node coverage rate acquisition unit is used for simulating the operation of a network entity by utilizing a network digital twin body to acquire the coverage rate of each heavy node;
the comprehensive reliability obtaining unit is used for determining the comprehensive reliability of the network digital twin body based on the coverage rate of each heavy node and the reliability of the modular network digital twin body; the integrated reliability is used as a reliability analysis result of the network entity.
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