Network reliability evaluation method and device, computer device, storage medium and program product

Abstract

The application relates to a network reliability evaluation method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: based on all feasible lower bound vectors of a target network, using a multi-element decision graph to represent all feasible lower bound vectors meeting the service requirement of the target network, and obtaining a feasible lower bound decision graph; performing state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph; based on a link set of the target network, using a multi-element decision graph to represent the capacity state of each link of the target network, so as to obtain a link state decision graph of each link; merging the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph; and using the capacity reliability decision graph to obtain the capacity reliability of the target network. The method can expand the reliability evaluation method to a multi-layer network, so that the capacity reliability of a complex multi-layer actual network can be accurately described.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network operation and maintenance, and in particular to a network reliability evaluation method and device, computer equipment, a storage medium and a program product. BACKGROUND

[0002] Evaluating the reliability of a network, providing a network reliability evaluation index, has a key guiding significance for network management and operation, and is an indispensable part of network optimization and design.

[0003] However, the reliability evaluation method for network service demand in the prior art only considers the service carrying capacity of a single-layer network, and needs to assume that link failures are independent when calculating reliability using the inclusion-exclusion principle, which makes it impossible to extend the reliability evaluation method to multi-layer networks, and thus cannot accurately describe complex and multi-layer actual networks. SUMMARY

[0004] Therefore, it is necessary to provide a network reliability evaluation method, device, computer equipment, storage medium and program product capable of accurately describing an actual network.

[0005] In a first aspect, in one embodiment, the present application provides a network reliability evaluation method, which comprises:

[0006] Based on all feasible lower bound vectors of the target network, a multi-element decision graph is used to represent all feasible lower bound vectors that meet the service demand of the target network, to obtain a feasible lower bound decision graph;

[0007] The state space of the feasible lower bound decision graph is expanded to obtain a link feasible capacity decision graph; the link feasible capacity decision graph represents the feasible capacity state of all links in the target network that meet the service demand;

[0008] Based on the link set of the target network, a multi-element decision graph is used to represent the capacity state of each link of the target network, to obtain a link state decision graph of each link; the link state decision graph represents the influence of events and / or components of the target network on the capacity state of the link;

[0009] The link feasible capacity decision graph and the link state decision graph of all links of the target network are merged to obtain a capacity reliability decision graph;

[0010] The capacity reliability of the target network is obtained using the capacity reliability decision graph.

[0011] In one embodiment, based on all feasible lower bound vectors of the target network, a multi-element decision graph is used to represent all feasible lower bound vectors that meet the service demand of the target network, to obtain a feasible lower bound decision graph, which comprises:

[0012] determining nodes of the feasible lower bound decision graph according to the link set;

[0013] determining terminal nodes of the feasible lower bound decision graph according to whether the target network can meet the service requirement;

[0014] determining arcs of the feasible lower bound decision graph according to capacity states of each link in the link set; the arcs of the feasible lower bound decision graph represent capacity lower bounds of corresponding links;

[0015] constructing the feasible lower bound decision graph matching the target network based on the terminal nodes, the nodes and the arcs of the feasible lower bound decision graph;

[0016] wherein a path from the start node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds meeting the service requirement; all the feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph meeting the service requirement of the target network.

[0017] In one embodiment, the state space of the feasible lower bound decision graph is expanded to obtain a link feasible capacity decision graph, including:

[0018] performing a sorting and marking operation on each arc of the nodes of the feasible lower bound decision graph in ascending order of capacity to obtain target expansion arcs corresponding to the nodes of the feasible lower bound decision graph;

[0019] expanding each target expansion arc into a set of capacities to obtain the link feasible capacity decision graph;

[0020] wherein the set of capacities of the current target expansion arc of the link feasible capacity decision graph is greater than or equal to the capacity lower bound represented by the target expansion arc before expansion and less than the capacity lower bound represented by the target expansion arc with the next serial number before expansion; the expansion range of the set of capacities is the state space of the link corresponding to the target expansion arc.

[0021] In one embodiment, based on the link set of the target network, the capacity state of each link of the target network is represented by a multi-element decision graph to obtain a link state decision graph of each link, including:

[0022] obtaining link state decision graph components based on all components and / or all events affecting the capacity state of the link; different links can contain the same link state decision graph components;

[0023] determining nodes of the link state decision graph according to the link state decision graph components;

[0024] determining arcs of the link state decision graph according to component states of the link state decision graph components;

[0025] determining terminal nodes of the link state decision graph according to capacity values of the link;

[0026] Based on the terminal nodes, nodes, and arcs of the link state decision graph, a link state decision graph corresponding to each link of the target network is constructed;

[0027] Among them, a path from the start node to the end node in the link state decision graph represents the capacity value of the link in the corresponding component state.

[0028] In one embodiment, the link feasible capacity decision graph and the link state decision graph of all links of the target network are merged to obtain a capacity reliability decision graph, including:

[0029] Based on the link set and link state decision graph components of the target network, a component set of the capacity reliability decision graph is constructed;

[0030] Determine the nodes of the capacity reliability decision diagram based on the component set;

[0031] Based on the preset decision graph merging rules, the link feasible capacity decision graph and the link status decision graph of each link are merged to obtain the capacity reliability decision graph.

[0032] In one embodiment, a component set of a capacity reliability decision graph is constructed based on a link set and a link state decision graph component of a target network, including:

[0033] Based on the sorting of the link set, components that affect each link element in the link set are added before the corresponding link element to obtain a component set;

[0034] Component collections do not count recurring components.

[0035] In one embodiment, obtaining the capacity reliability of the target network using the capacity reliability decision diagram includes:

[0036] Based on the capacity reliability decision diagram and the preset network capacity reliability formula, the capacity reliability corresponding to the target network meeting business needs is calculated.

[0037] In a second aspect, in one embodiment, the present application provides a network reliability assessment device, the device comprising:

[0038] A feasible lower bound decision graph construction module is used to represent all feasible lower bound vectors that meet the service requirements of the target network using a multivariate decision graph based on all feasible lower bound vectors of the target network, thereby obtaining a feasible lower bound decision graph;

[0039] a link feasible capacity decision graph construction module configured to perform state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph, wherein the link feasible capacity decision graph represents feasible capacity states of all links in the target network that satisfy the service demand;

[0040] a link state decision graph construction module configured to represent capacity states of each link in the target network by using a multivariate decision graph based on a link set of the target network to obtain a link state decision graph of each link, wherein the link state decision graph represents influences of events and / or components of the target network on the capacity states of the links;

[0041] a capacity reliability decision graph construction module configured to merge the link feasible capacity decision graph and the link state decision graphs of all links in the target network to obtain a capacity reliability decision graph;

[0042] a capacity reliability evaluation module configured to obtain the capacity reliability of the target network by using the capacity reliability decision graph.

[0043] In a third aspect, in an embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the method embodiments of the first aspect when executing the computer program.

[0044] In a fourth aspect, in an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the method embodiments of the first aspect when executed by a processor.

[0045] In a fifth aspect, in an embodiment, a computer program product is provided, including a computer program, and the computer program implementing the steps in the method embodiments of the first aspect when executed by a processor.

[0046] The network reliability evaluation method, device, computer device, storage medium and program product, the method represents all feasible lower bounds of the target network by using a multivariate decision graph based on capacity states of all links in the target network to obtain a feasible lower bound decision graph, then performs state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph, then represents capacity states of each link in the target network by using a multivariate decision graph based on a link set of the target network to obtain a link state decision graph, merges the link feasible capacity decision graph and the link state decision graphs of all links in the target network to obtain a capacity reliability decision graph, and finally obtains the capacity reliability of the target network by using the capacity reliability decision graph. The multivariate decision graph is used to evaluate the capacity reliability of the network, and the reliability evaluation method can be extended to a multi-layer network, so that the capacity reliability of a complex multi-layer actual network can be accurately described. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained on the basis of these drawings without creative labor.

[0048] Figure 1 A flowchart of a network reliability evaluation method in an embodiment;

[0049] Figure 2 A flowchart of constructing a feasible lower bound decision graph in an embodiment;

[0050] Figure 3 A schematic diagram of a feasible lower bound decision graph in an embodiment;

[0051] Figure 4 A flowchart of constructing a link feasible capacity decision graph in an embodiment;

[0052] Figure 5 A flowchart of constructing a link state decision graph in an embodiment;

[0053] Figure 6 A flowchart of constructing a capacity reliability decision graph in an embodiment;

[0054] Figure 7 A schematic diagram of a decision graph merging operation in an embodiment;

[0055] Figure 8 A schematic diagram of a target network in a wide area network quantitative evaluation system in an embodiment;

[0056] Figure 9 A schematic diagram of a feasible lower bound decision graph in a quantitative evaluation system scenario in an embodiment;

[0057] Figure 10 A link feasible capacity decision graph in a quantitative evaluation system scenario in an embodiment;

[0058] Figure 11 A decision graph merging operation schematic diagram in a quantitative evaluation system scenario in an embodiment;

[0059] Figure 12 A structural block diagram of a network reliability evaluation device in an embodiment;

[0060] Figure 13 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0061] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0062] At present, evaluating the reliability of a network and providing a network reliability evaluation index have a key guiding significance for network management and operation, and are indispensable links for network optimization and design. Network reliability evaluation is also a key functional module of a network planning system, a network simulation system and a digital twin system.

[0063] Since actual communication network links and nodes have capacity upper limits and the whole network has transmission capacity limitations, compared with traditional technologies that only evaluate the connectivity reliability of a network, evaluating the capacity reliability of a network for service carrying capacity is obviously more in line with reality.

[0064] Traditional network capacity reliability evaluation methods mainly model to evaluate the probability that a node pair composed of two nodes in a network can meet the traffic demand, that is, given two nodes and traffic demand, the probability that the network can complete the demand transmission. The following lists three traditional reliability evaluation methods: ① a capacity-constrained reliability algorithm is evaluated on the basis of a two-state flow network, the model constructed by this method assumes that network links have two states of working or failure; ② a d-minimum path method-based algorithm for calculating the capacity reliability of a random flow network is evaluated on the basis of a random flow network, the model constructed by this method assumes that network links have multiple capacity states, and each link has a capacity probability distribution; ③ a random flow network capacity reliability algorithm under time and cost constraints, the evaluation model of this method considers time and cost constraints.

[0065] However, the model assumptions of traditional network capacity reliability evaluation methods have the following defects, which may cause deviations between the reliability evaluation and the actual situation, as follows:

[0066] ① The actual communication network structure is complex and may present a multi-layer structure, while the traditional reliability evaluation method only considers the service carrying capacity of a single-layer network, and when calculating reliability using the inclusion-exclusion principle, it also needs to assume that link failures are independent, which makes the traditional capacity reliability evaluation model that only considers a single-layer network unable to accurately describe the actual network;

[0067] ② The traditional capacity reliability evaluation model that only considers a single-layer network cannot directly reflect the conduction relationship of bottom-layer network failures. For example, in an IP+optical network, IP layer and optical layer routing are independently converged in the current implementation, and optical layer failures are difficult to model and represent in the IP layer;

[0068] ③ The traditional capacity reliability assessment model makes an unreasonable assumption about the independence of network link failures: Taking IP networks as an example, from the perspective of IP networks, two seemingly unrelated links may actually pass through the same computer room and be transmitted through the same physical link. As a result, a failure of the underlying physical link will cause multiple links in the upper-layer network to fail simultaneously. However, these failures cannot be specifically reflected from the perspective of the upper-layer network.

[0069] ④ Common-cause failures can cause even previously independent components to fail simultaneously when faced with regional or correlated events. For example, two nodes in different buildings in the same district may become simultaneously unavailable during a regional power outage. Traditional capacity reliability assessment models are also unsuitable for these common-cause failure scenarios.

[0070] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features that may appear in the embodiments of the present invention:

[0071] BDD (Binary Decision Diagram): A BDD is a directed acyclic graph based on Shannon decomposition, which is essentially a simplified Boolean function.

[0072] MDD (Multiple-valued Decision Diagram): An MDD is a decision diagram where nodes represent multiple values.

[0073] In one embodiment, Figure 1 As shown, the present application provides a network reliability assessment method. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps S102 to S110. Among them:

[0074] Step S102 : Based on all feasible lower bound vectors of the target network, a multivariate decision graph is used to represent all feasible lower bound vectors that meet the service requirements of the target network, thereby obtaining a feasible lower bound decision graph.

[0075] The feasible lower bound vector can be a capacity lower bound vector that satisfies the service requirements of the target network. It is understood that when the network link capacity of the target network is greater than or equal to the feasible lower bound vector, the target network can meet the service requirements of the target network. The feasible lower bound decision diagram represents all feasible lower bound vectors that meet the service requirements of the target network in the form of a multivariate decision diagram.

[0076] Specifically, the server can represent all feasible lower bound vectors of the target network satisfying the service requirements in the manner of a multi-dimensional decision diagram (MDD) based on all feasible lower bound vectors.

[0077] Exemplarily, all feasible flow vectors in the target network can be obtained in the manner of a fast enumeration method, and the capacity lower bound of the network link can be obtained based on the feasible flow vector, and then the capacity lower bound vector satisfying the service requirements of the target network is obtained.

[0078] It should be noted that each feasible flow vector is a solution that can satisfy the service requirements, for example, the target network can represent that the traffic transmitted on all paths of the target network can finally add up to satisfy the traffic demand matrix under the condition of having the traffic demand matrix.

[0079] It can be understood that since there are different capacity states of each link in the target network, for each feasible flow, the capacity lower bound satisfying the feasible flow can be obtained for each link. For example, when the link e is required to satisfy a feasible flow, the link capacity needs to reach 3 mbps, while the link The actual possible capacity states are 10 mbps, 5 mbps, and 0 mbps, and at this time, the link The capacity lower bound satisfying the feasible flow is 5 mbps.

[0080] In some examples, the link set of the target network can be represented as Each link of the link set has multiple possible capacity states. Exemplarily, for the link , there are capacity states, and then the link corresponding state space is , where the state space has . It can be assumed that all feasible lower bound vectors satisfying the service requirements obtained are , where indicates that there are capacity lower bound vectors that can satisfy all service requirements. It can be understood that at this time, the following relationship exists:

[0081] ,

[0082] wherein, indicates the capacity lower bound vector, indicates the link, indicates the total number of links of the target network, indicates the state of the link in the capacity lower bound vector , and indicates the link the state space (i.e. capacity space) of the target network. In the case that the capacity of each link of the target network is greater than the lower bound (i.e. , i.e. , it can be determined that the target network can satisfy the traffic demand.

[0083] Optionally, by enumerating the capacity lower bound vectors of all possible states in which the target network satisfies the traffic demand, all feasible lower bound vectors can be obtained.

[0084] In step S104, the state space of the feasible lower bound decision graph is expanded to obtain a link feasible capacity decision graph.

[0085] The link feasible capacity decision graph can be used to represent the feasible capacity state of all links in the target network that satisfy the traffic demand. Exemplarily, the feasible capacity state is the capacity state of the link that satisfies the traffic demand of the target network, which includes the capacity lower bound corresponding to the current link.

[0086] It can be understood that the feasible lower bound decision graph represents all feasible lower bound vectors that satisfy the traffic demand of the target network, and the state space of the feasible lower bound decision graph is expanded to obtain the link feasible capacity decision graph, and the link feasible capacity decision graph can further represent all link capacity states that satisfy the traffic demand of the target network.

[0087] Specifically, the feasible lower bound decision graph obtained in step S102 is expanded in state space to further obtain the MDD representing all link capacity states that satisfy the traffic demand of the target network, i.e. to obtain the link feasible capacity decision graph.

[0088] In step S106, based on the link set of the target network, the capacity state of each link of the target network is represented by the MDD to obtain a link state decision graph.

[0089] The link state decision graph can represent the influence of events and / or components of the target network on the capacity state of the link. Exemplarily, the components are various link components such as optical nodes, optical links, etc., which affect the capacity state of the link; the events are events that affect the capacity state of the link, such as power failure, natural disaster, network attack, etc.

[0090] Specifically, the server represents the capacity state of each link of the target network by the MDD, as well as the events and / or components that affect the capacity state of the link, and correspondingly generates the MDD of the number of links of the target network, i.e. generates the link state decision graph corresponding to each link.

[0091] Step S108, merging the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph.

[0092] The capacity reliability decision graph can be used to represent the capacity reliability of the target network.

[0093] Exemplarily, the decision graph merging operation between the link feasible capacity decision graph and the link state decision graph can be performed through an AND logic operation and / or an OR logic operation.

[0094] Specifically, the MDD obtained in step S104, i.e., the link feasible capacity decision graph, representing all capacity states of the target network satisfying the service demand is merged with the MDD obtained in step S106, i.e., the link state decision graph, representing the capacity state of each link, to obtain the MDD finally representing the capacity reliability of the target network, i.e., the capacity reliability decision graph.

[0095] Step S110, obtaining the capacity reliability of the target network by using the capacity reliability decision graph.

[0096] The capacity reliability can represent the network capacity reliability of the target network capable of satisfying the service traffic demand.

[0097] Specifically, the server can calculate the network reliability of the target network by using the MDD representing the capacity reliability of the target network, i.e., the capacity reliability decision graph.

[0098] The network reliability evaluation method described above, based on all feasible lower bound vectors of the target network, uses a multi-element decision graph to represent all feasible lower bound vectors satisfying the service demand of the target network, thereby obtaining a feasible lower bound decision graph, and then can perform state space expansion on the obtained feasible lower bound decision graph to further obtain a link feasible capacity decision graph, and then based on the link set of the target network and events and / or components, constructs a corresponding link state decision graph, and after merging the link feasible capacity decision graph and the link state decision graph of all links of the target network, obtains a capacity reliability decision graph capable of obtaining the capacity reliability of the target network. Through the above-mentioned manner, the present application can expand the reliability evaluation method to a multi-layer network, thereby accurately describing a complex multi-layer actual network.

[0099] In one of the embodiments, as shown in FIG. 2, Figure 2 based on all feasible lower bound vectors of the target network, a multi-element decision graph is used to represent all feasible lower bound vectors satisfying the service demand of the target network, to obtain a feasible lower bound decision graph, including the following steps S202 to S208. Wherein:

[0100] Step S202, determining the nodes of the feasible lower bound decision graph according to the link set.

[0101] The node of the feasible lower bound decision graph can represent each link of the link set of the target network. It should be noted that the node does not include a terminal node.

[0102] Specifically, based on each link in the link set of the current target network, each node of the feasible lower bound decision graph corresponding to the current target network is determined.

[0103] In step S204, a terminal node of the feasible lower bound decision graph is determined according to whether the target network can meet the service requirement.

[0104] The terminal node of the feasible lower bound decision graph is a Boolean value (including True and False), which represents whether the target network can meet the service requirement.

[0105] Specifically, the Boolean value of the terminal node is determined according to whether the target network can meet the service requirement, and then the terminal node of the feasible lower bound decision graph is determined.

[0106] In step S206, an arc of the feasible lower bound decision graph is determined according to the capacity state of each link in the link set.

[0107] The arc of the feasible lower bound decision graph can represent the capacity lower bound of the corresponding link in the target network.

[0108] Specifically, the arc of the corresponding node is determined according to the capacity state of the link represented by the node of the feasible lower bound decision graph.

[0109] In step S208, a feasible lower bound decision graph matched with the target network is constructed based on the terminal node, the node and the arc of the feasible lower bound decision graph.

[0110] A path from the starting node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds meeting the service requirement; all feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph of the target network meeting the service requirement.

[0111] Specifically, based on the terminal node, the node and the arc of the feasible lower bound decision graph determined in steps S202 to S206, a feasible lower bound decision graph matched with the target network can be constructed, which can represent all feasible lower bound vectors meeting the service requirement of the target network.

[0112] To further illustrate the specific form of each multi-element decision graph constructed by the present application, the present application provides an exemplary feasible lower bound decision graph as shown in Figure 3 The exemplary feasible lower bound decision graph is only an explanation of the present application, and does not limit the present application.

[0113] It should be noted that Figure 3 The numbers (e.g., "1", "2, 3, 4, 5", etc.) on the MDD arcs represent the states of the nodes of the MDD, such as the capacity states of the links; the subscript of each node represents the number of the link corresponding to the node, such as the link set corresponding to the node .

[0114] In one embodiment, as Figure 4 shown, the state space expansion is performed on the feasible lower bound decision graph to obtain a link feasible capacity decision graph, including steps S402 to S404. Among them:

[0115] Step S402, for each arc of the node of the feasible lower bound decision graph, a sorting and marking operation is performed in ascending order according to the capacity size, to obtain the target expansion arc corresponding to the node of the feasible lower bound decision graph.

[0116] Among them, the node of the feasible lower bound decision graph can include multiple arcs, and each arc represents a capacity lower bound of a link corresponding to a node.

[0117] Specifically, each arc is sorted in ascending order according to the capacity of the corresponding link, so that each arc obtains a serial number subscript. For example, if the capacity of the link is 2, 3, and 7, the subscript serial number of the corresponding arc can be 1, 2, and 3.

[0118] Step S404, each target expansion arc is expanded into a set of capacity sets to obtain a link feasible capacity decision graph.

[0119] Among them, the capacity set of the current target expansion arc of the link feasible capacity decision graph is greater than or equal to the capacity lower bound represented by the target expansion arc before expansion, and is less than the capacity lower bound represented by the next serial number target expansion arc before expansion; the expansion range of the capacity set is the state space of the link corresponding to the target expansion arc.

[0120] Specifically, each arc corresponding to the node of the feasible lower bound decision graph is expanded in state, so that each arc can represent a set of capacity sets, which is greater than or equal to the capacity lower bound originally represented by the arc itself, less than the capacity lower bound represented by the next serial number arc, and the capacity of the capacity set belongs to the state space of the link corresponding to the arc.

[0121] For example, assuming the state space of link e is {1, 2, 3, 4, 5, 6, 7}, and the MDD node corresponding to the current link e has three arcs {1, 4, 7}, when the lower bound of the expanded state is required, the arc corresponding to 1 of the node can be expanded to {1, 2, 3}. Based on the same expansion principle, it can be understood that the three arcs of the MDD node corresponding to link e can be expanded as a whole to {{1, 2, 3}, {4, 5, 6}, {7}}.

[0122] In one embodiment, as shown in FIG. 5, based on the link set of the target network, a multi-element decision diagram is used to represent the capacity state of each link of the target network to obtain a link state decision diagram, including steps S502 to S510. Wherein: Figure 5

[0123] Step S502, based on all components and / or all events affecting the capacity state of the link, obtain a link state decision diagram component.

[0124] Wherein, different links can contain the same link state decision diagram component.

[0125] For example, the components affecting the capacity state of the link can include various link components, for example, the components affecting the capacity state of the link can be optical nodes, optical links, etc. Further, the events affecting the capacity state of the link can include power failure, natural disaster, network attack, etc. It should be noted that any link can contain the same link state decision diagram component, and by setting this condition, the link state independence assumption of the target network can be removed.

[0126] Specifically, for each link of the target network, all components and / or all events affecting the capacity state of the link are obtained, and based on the above components and / or events, a corresponding set of link state decision diagram components can be obtained. The link state decision diagram component can be used to represent any component or event affecting the capacity state of the link.

[0127] Step S504, determine the node of the link state decision diagram according to the link state decision diagram component.

[0128] Wherein, the node of the link state decision diagram represents the components (i.e. the above components and / or events) affecting the link of the target network.

[0129] Specifically, the server can determine the node of the link state decision diagram according to the link state decision diagram component obtained above.

[0130] Step S506, determine the arc of the link state decision diagram according to the component state of the link state decision diagram component.

[0131] ​The arc of the link state decision graph represents the component state of the corresponding component. For example, the component state can be a binary state, which can represent the occurrence / non-occurrence of an event corresponding to the component, or the normal / fault of a component corresponding to the component.

[0132] It can be understood that the specific setting mode of the component state is not limited to the implementation mode mentioned in the above embodiment. For example, the component state can also be set to other specific numerical values. The specific setting mode of the component state is not limited in the present application.

[0133] Specifically, the server can determine the arc of the link state decision graph according to the component state of the component of the link state decision graph.

[0134] In step S508, the terminal node of the link state decision graph is determined according to the capacity value of the link.

[0135] For example, assuming that the capacity space of the link e is , then the link has terminal nodes corresponding to the link state decision graph, which represent the capacity value of the link under the current component state.

[0136] Specifically, the server can determine the terminal node of the link state decision graph based on the capacity value of the link in the target network.

[0137] In step S510, the link state decision graph corresponding to each link of the target network is constructed based on the terminal node, node and arc of the link state decision graph.

[0138] A path from the starting node to the terminal node in the link state decision graph represents the capacity value of the link under the corresponding component state.

[0139] Specifically, according to the terminal node, node and arc of the link state decision graph determined above, the link state decision graph corresponding to each link of the target network is constructed.

[0140] In one embodiment, as shown in Figure 6 , the link feasible capacity decision graph and the link state decision graph of all links of the target network are merged to obtain a capacity reliability decision graph, including the following steps S602 and S606. Wherein:

[0141] In step S602, the component set of the capacity reliability decision graph is constructed based on the link set of the target network and the component of the link state decision graph.

[0142] The component set of the capacity reliability decision graph can include all links of the target network and link state decision graph components affecting the capacity state of the links.

[0143] Specifically, the component set of the capacity reliability decision graph is constructed according to the link set of the target network and the obtained link state decision graph components.

[0144] At step S604, nodes of the capacity reliability decision graph are determined according to the component set.

[0145] The nodes of the capacity reliability decision graph correspond to the respective capacity reliability decision graph components in the component set of the capacity reliability decision graph.

[0146] Specifically, after the component set of the capacity reliability decision graph is constructed, the nodes of the capacity reliability decision graph can be determined according to the respective capacity reliability decision graph components in the component set of the capacity reliability decision graph.

[0147] At step S606, the link feasible capacity decision graph and the link state decision graph of each link are merged based on a preset decision graph merging rule to obtain the capacity reliability decision graph.

[0148] The path of the capacity reliability decision graph can represent a set of feasible states, i.e., a feasible lower bound vector space.

[0149] Specifically, the link feasible capacity decision graph obtained above and the link state decision graph corresponding to each link in the target network are merged using the preset decision graph merging rule to obtain the capacity reliability decision graph, and the network reliability of the target network can be calculated using the capacity reliability decision graph.

[0150] Exemplarily, the merging operation of the multi-decision graph can be denoted as The specific preset decision graph merging rule of the two decision graphs can be represented by the following operation process:

[0151]

[0152] wherein, MDD has k states; “if-else-then” algorithm; and represent variables, i.e., the current decision graph node (MDD node) in the merging operation process; and represent two different multi-decision graphs to be merged, represents the index value of the current node. It can be understood that when all nodes of the MDD are determined values, the state of the terminal node of the MDD can be uniquely determined as one of . Further, the in the above merging operation can represent a "conjunction" operation or an "exclusive or" operation in logical operation.

[0153] It can be understood that the above preset decision graph merging rule can be used to merge two MDDs representing logical expressions into one MDD. When the above merging rule is applied, the order index of the two root nodes (i.e. ) needs to be compared.

[0154] Exemplarily, referring to Figure 7 , the preset decision graph merging rule can be a merging process as shown in Figure 7 . For example, if , it indicates that they belong to the same component, then the above operation process is applied to their child nodes; otherwise, the variable with a smaller index value will become the new root node of the combined MDD.

[0155] Further, the decision graph merging operation for obtaining the capacity reliability decision graph can be represented as follows:

[0156] ,

[0157] wherein, is the capacity reliability decision graph, is the link feasible capacity decision graph, to are the link state decision graphs of each link in the target network.

[0158] In one of the embodiments, based on the link set and the link state decision graph component of the target network, a component set of the capacity reliability decision graph is constructed, including the following steps:

[0159] Based on the ordering of the link set, the components affecting each link element in the link set are added before the corresponding link element to obtain the component set.

[0160] wherein, the component set does not count the repeatedly appearing components.

[0161] Specifically, based on the ordering of the link set, the components affecting the current link are added before the corresponding link; if there are repeated components, they are not counted repeatedly, and the smallest component sequence is used as the criterion; wherein, the smallest component sequence means that when the repeated components are sorted, the component that appears first is sorted as the criterion.

[0162] For example, assuming that there are links , and there are components , , then based on the link set and the corresponding component set that the component can finally obtain is ).

[0163] In one embodiment, the capacity reliability decision diagram is used to obtain the capacity reliability of the target network, comprising:

[0164] According to the capacity reliability decision diagram, the corresponding capacity reliability of the target network satisfying the service demand is calculated by combining the preset network capacity reliability formula.

[0165] Specifically, based on the capacity reliability decision diagram constructed above, the corresponding capacity reliability of the target network satisfying the service demand can be calculated by using the network capacity reliability formula, so as to determine the capacity reliability of the current target network.

[0166] Exemplarily, the preset network capacity reliability formula can be expressed as follows:

[0167]

[0168] wherein, represents the feasible state represented by the capacity reliability decision diagram, i.e. the feasible lower bound vector space; represents a group of feasible lower bound vectors; represents the capacity state of the link, represents the probability corresponding to the capacity state of the link. For example, the probability corresponding to the capacity state of the link can represent the probability of the link being in a certain capacity state, for example: the probability of the link capacity being 0 is 0.1, the probability of the link capacity being 1 is 0.2, and the probability of the link capacity being 2 is 0.7. The sum of the above probabilities is 1, and the capacity state space of the link is {0.1, 0.2, 0.7}.

[0169] The present application evaluates the capacity reliability of the target network by using the decision diagram, and obtains the lower bound of the link capacity of the target network satisfying the service demand The lower bound decision diagram is composed of the lower bound of the link capacity of the target network satisfying the service demand, and the state space of the decision diagram is expanded, so as to obtain the feasible capacity decision diagram representing all link capacity states of the target network satisfying the service demand. Then, the present application expands the components affecting the capacity of each link, so that the dependency relationship between the links can be described by the decision diagram composed of all components. Since the components can be widely defined, the reliability evaluation method of the present application can be extended to more general scenarios while removing the independence assumption, so as to more accurately describe complex and multi-layer actual networks.

[0170] To make the purpose, technical solutions, and advantages of this application more clearly understood, this application is further described in detail using the network reliability assessment method of this application as an example of a quantitative assessment system for a wide area network. It should be noted that the specific embodiments described herein are merely intended to explain this application and are not intended to limit this application. The specific implementation is as follows:

[0171] The evaluation requires input of all necessary components, the state and value of each component and the probability of the link capacity being captured, the network topology, and the capacity requirement D.

[0172] by Figure 8 For example, the traffic demand matrix D only has the traffic demand 3 from nodes 1 to 4, so it can be degenerated into the problem of how to meet the end-to-end service requirements of the network. Figure 8 As shown, the capacity states of the links at the IP layer are , the following three feasible solutions for the capacity lower bound can be obtained by enumeration: , corresponding to links arrive Then, calculate it as follows:

[0173] ① Such as Figure 9 As shown, the above capacity lower bound Convert to (i.e., feasible lower bound decision diagram), Figure 9 The MDD shown represents all feasible lower bounds for the target network to meet the business requirements.

[0174] ② If Figure 10 As shown, Expand the state space to get (i.e., link feasible capacity decision diagram), through Indicates the capacity status of all links in the target network that meet service requirements.

[0175] ③ General arrive For each link and the components that affect the link, the capacity status of each link is represented by MDD, that is, the link state decision diagram of each link is obtained.

[0176] ④ Such as Figure 11 As shown in Figure 2, the above multi-decision graphs are merged, that is, the link feasible capacity decision graph and all link state decision graphs are merged, so as to obtain the MDD (i.e., capacity reliability decision graph) that finally represents the target network capacity reliability. The specific process can be expressed as ;in, is the capacity reliability decision diagram, is the link feasible capacity decision diagram, to a link state decision diagram for each link in the network.

[0177] ⑤According to the above-obtained Calculate the final reliability.

[0178] The network reliability evaluation method of the present application can be applied to a wide area network quantitative evaluation system, and can be used as an evaluation system to provide network quantitative evaluation results for network planning systems and network simulation systems. In fact, for the reliability evaluation of wide area network services, not only the ability of the IP layer to carry service capacity needs to be considered, but also the impact of the underlying network on IP link capacity. The network reliability evaluation method of the present application considers the dependency relationship of network construction by modeling the inter-layer mapping relationship of a multi-layer network, thereby avoiding the irrationality of the assumption of link failure independence.

[0179] It can be understood that the network reliability evaluation method of the present application can also be applied to network attack and defense drills, network fault drills, and other scenarios. In these scenarios, the evaluation of network capacity reliability is also crucial. The network reliability evaluation method of the present application can provide reliable optimization quantitative indicators for network optimization on the one hand, and can also provide optimization indicators for network attack techniques on the other hand.

[0180] For example, in network fault drills, a large number of regional fault events are usually involved, and factors such as disasters and destruction are also considered. These destructive events have a significant impact on the network, and traditional network reliability evaluation schemes cannot accurately consider the impact of such events on the network. Therefore, traditional evaluation models cannot be applied to such scenarios. The network reliability evaluation method of the present application can model network attack events as one of the components of the MDD used to evaluate network reliability. A component can affect the capacity state of multiple links, thereby enabling the present application to fully consider the impact of fault events on network reliability.

[0181] It can be understood that based on the application network reliability evaluation method in the above various scenarios, the application at least includes the following beneficial technical effects: the method of the application performs network capacity reliability evaluation based on MDD, providing scalability for the reliability evaluation model; the method of the application expands the state space of MDD, can directly connect the existing reliability evaluation method, and converts the capacity lower bound into a decision graph, improving the adaptability of the application; the method of the application defines components as links of the target network and components and / or events affecting the links of the target network, improves the scalability of the problem model, and no longer needs to make a fault independent assumption, fully considers the dependency relationship between various factors, and finally establishes an accurate probability model; the application can also directly extend the original model, and the extension process only needs to supplement the components and dependency relationships of the complete link, and the extension process can be realized by merging the MDD.

[0182] It should be understood that although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0183] Based on the same inventive concept, the embodiments of the application also provide a network reliability evaluation device for implementing the network reliability evaluation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more network reliability evaluation device embodiments provided below can refer to the limitations of the network reliability evaluation method in the above text, which will not be repeated here.

[0184] In one exemplary embodiment, as shown in Figure 12 a network reliability evaluation device 120 is provided, comprising:

[0185] a feasible lower bound decision graph construction module 121, configured to represent all feasible lower bound vectors satisfying the service requirements of the target network by a multi-element decision graph based on all feasible lower bound vectors of the target network, to obtain a feasible lower bound decision graph;

[0186] The link feasible capacity decision graph construction module 123 is configured to perform state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph. The link feasible capacity decision graph represents feasible capacity states of all links in the target network that satisfy the service demand.

[0187] The link state decision graph construction module 125 is configured to represent, based on the link set of the target network, a capacity state of each link of the target network by using a multi-element decision graph to obtain a link state decision graph of each link. The link state decision graph represents an influence of an event and / or component of the target network on the capacity state of the link.

[0188] The capacity reliability decision graph construction module 127 is configured to combine the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph.

[0189] The capacity reliability evaluation module 129 is configured to obtain the capacity reliability of the target network by using the capacity reliability decision graph.

[0190] In one embodiment, the feasible lower bound decision graph construction module 121 is further configured to:

[0191] determine nodes of the feasible lower bound decision graph according to the link set;

[0192] determine terminal nodes of the feasible lower bound decision graph according to whether the target network can satisfy the service demand;

[0193] determine arcs of the feasible lower bound decision graph according to the capacity state of each link in the link set. The arcs of the feasible lower bound decision graph represent a capacity lower bound of the corresponding link;

[0194] construct the feasible lower bound decision graph matched with the target network based on the terminal nodes, the nodes and the arcs of the feasible lower bound decision graph;

[0195] In the feasible lower bound decision graph, a path from a starting node to a terminal node represents a feasible lower bound that satisfies the service demand. All feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph that satisfies the service demand of the target network.

[0196] In one embodiment, the link feasible capacity decision graph construction module 123 is further configured to:

[0197] perform a sorting and marking operation on each arc of the nodes of the feasible lower bound decision graph in ascending order of capacity to obtain target expansion arcs corresponding to the nodes of the feasible lower bound decision graph;

[0198] expand each target expansion arc into a set of capacity sets to obtain the link feasible capacity decision graph;

[0199] The capacity set of the current target expansion arc of the link feasible capacity decision graph is greater than or equal to the capacity lower bound represented by the target expansion arc before expansion and less than the capacity lower bound represented by the target expansion arc with the next serial number before expansion; and the expansion range of the capacity set is the state space of the link corresponding to the target expansion arc.

[0200] In one of the embodiments, the link state decision graph construction module 125 is further configured to:

[0201] obtain a link state decision graph component based on all components and / or all events affecting the capacity state of the link; wherein different links can contain the same link state decision graph component;

[0202] determine a node of the link state decision graph according to the link state decision graph component;

[0203] determine an arc of the link state decision graph according to the component state of the link state decision graph component;

[0204] determine a terminal node of the link state decision graph according to the capacity value of the link;

[0205] construct a link state decision graph corresponding to each link of the target network based on the terminal node, the node and the arc of the link state decision graph;

[0206] wherein a path from the starting node to the terminal node in the link state decision graph represents the capacity value of the link under the corresponding component state.

[0207] In one of the embodiments, the capacity reliability decision graph construction module 127 is further configured to:

[0208] construct a component set of the capacity reliability decision graph based on the link set of the target network and the link state decision graph component;

[0209] determine a node of the capacity reliability decision graph according to the component set;

[0210] merge the link feasible capacity decision graph and the link state decision graph of each link based on a preset decision graph merging rule to obtain the capacity reliability decision graph.

[0211] In one of the embodiments, the capacity reliability decision graph construction module 127 is further configured to:

[0212] add the components affecting each link element in the link set to the corresponding link element to obtain the component set based on the ordering of the link set;

[0213] wherein the component set does not count the repeatedly appearing components.

[0214] In one of the embodiments, the capacity reliability evaluation module 129 is further configured to:

[0215] According to the capacity reliability decision diagram, the network capacity reliability formula is combined to calculate the capacity reliability of the target network satisfying the service demand.

[0216] The modules in the network reliability evaluation apparatus 120 can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of the processor of the computer device in hardware form, or stored in the memory of the computer device in software form, so as to be called and executed by the processor to perform the operations of the modules.

[0217] In one example embodiment, a computer device is provided, which can be a server, and the internal structure diagram thereof can be as shown in Figure 13 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device can be used to store related data of each multi-element decision diagram. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a network reliability evaluation method.

[0218] Those skilled in the art can understand that Figure 13 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0219] In one embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0220] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0221] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0222] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned method embodiments. Any reference to a memory, database or other medium used in each embodiment provided by the present application can include at least one of a non-volatile memory and a volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in each embodiment provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in each embodiment provided by the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0223] Each technical feature of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of each technical feature in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered that the combinations are within the scope of the present application.

[0224] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A network reliability evaluation method, characterized by, The method comprises: Based on all feasible lower bound vectors of the target network, a multi-element decision graph is used to represent all feasible lower bound vectors satisfying the service requirement of the target network, to obtain a feasible lower bound decision graph; State space expansion is performed on the feasible lower bound decision graph to obtain a link feasible capacity decision graph; the link feasible capacity decision graph represents the feasible capacity state of all links in the target network satisfying the service requirement; Based on the link set of the target network, a multi-element decision graph is used to represent the capacity state of each link of the target network, to obtain a link state decision graph of each link; the link state decision graph represents the influence of events and / or components of the target network on the capacity state of the link; The link feasible capacity decision graph and the link state decision graph of all links of the target network are merged to obtain a capacity reliability decision graph; The capacity reliability of the target network is obtained by using the capacity reliability decision graph.

2. The method of claim 1, wherein, Based on all feasible lower bound vectors of the target network, a multi-element decision graph is used to represent all feasible lower bound vectors satisfying the service requirement of the target network, to obtain a feasible lower bound decision graph, which comprises: According to the link set, nodes of the feasible lower bound decision graph are determined; According to whether the target network can satisfy the service requirement, terminal nodes of the feasible lower bound decision graph are determined; According to the capacity state of each link in the link set, arcs of the feasible lower bound decision graph are determined; the arcs of the feasible lower bound decision graph represent the capacity lower bound of the corresponding link; Based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph, the feasible lower bound decision graph matching the target network is constructed; In the feasible lower bound decision graph, a path from a starting node to a terminal node represents a group of feasible lower bounds satisfying the service requirement; all feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph in which the target network satisfies the service requirement.

3. The method of claim 1, wherein, The state space expansion is performed on the feasible lower bound decision graph to obtain a link feasible capacity decision graph, which comprises: A sorting and marking operation is performed on each arc of the node of the feasible lower bound decision graph in ascending order of capacity size, to obtain a target expansion arc corresponding to the node of the feasible lower bound decision graph; Each target expansion arc is expanded into a set of capacity sets, to obtain the link feasible capacity decision graph; The capacity set of the current target expansion arc of the link feasible capacity decision graph is greater than or equal to the capacity lower bound represented by the target expansion arc before expansion and less than the capacity lower bound represented by the target expansion arc with the next serial number before expansion; the expansion range of the capacity set is the state space of the link corresponding to the target expansion arc.

4. The method of claim 1, wherein, Based on the link set of the target network, a multi-element decision graph is used to represent the capacity state of each link of the target network, to obtain a link state decision graph of each link, which comprises: obtain a link state decision graph component based on all the components and / or all the events affecting the capacity state of the link; wherein different links contain the same link state decision graph component; determine a node of the link state decision graph according to the link state decision graph component; determine an arc of the link state decision graph according to a component state of the link state decision graph component; determine a terminal node of the link state decision graph according to the capacity value of the link; construct the link state decision graph corresponding to each link of the target network based on the terminal node, the node and the arc of the link state decision graph; wherein a path from a start node to a terminal node in the link state decision graph represents the capacity value of the link under the corresponding component state.

5. The method of claim 1, wherein, The combining the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph comprises: construct a component set of the capacity reliability decision graph based on the link set of the target network and the link state decision graph component; determine a node of the capacity reliability decision graph according to the component set; combine the link feasible capacity decision graph and the link state decision graph of each link based on a preset decision graph combining rule to obtain a capacity reliability decision graph.

6. The method of claim 5, wherein, The constructing the component set of the capacity reliability decision graph based on the link set of the target network and the link state decision graph component comprises: add the components affecting each link element in the link set to the corresponding link element to obtain the component set based on the order of the link set; wherein the component set does not count the repeatedly appearing components.

7. The method according to any one of claims 1 to 6, characterized in that, The obtaining the capacity reliability of the target network by using the capacity reliability decision graph comprises: calculate the capacity reliability of the target network satisfying the service requirement according to the capacity reliability decision graph and a preset network capacity reliability formula.

8. A network reliability evaluation apparatus characterized by comprising: The apparatus comprises: a feasible lower bound decision graph construction module configured to represent all feasible lower bound vectors satisfying the service requirement of the target network by using a multi-element decision graph based on all feasible lower bound vectors of the target network to obtain a feasible lower bound decision graph; a link feasible capacity decision graph construction module configured to perform state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph; the link feasible capacity decision graph represents the feasible capacity state of all links satisfying the service requirement of the target network; a link state decision graph construction module configured to represent the capacity state of each link of the target network by using a multi-element decision graph based on a link set of the target network to obtain a link state decision graph of each link; the link state decision graph represents the influence of events and / or components of the target network on the capacity state of the link; a capacity reliability decision graph construction module, configured to combine the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph; a capacity reliability evaluation module, configured to obtain a capacity reliability degree of the target network by using the capacity reliability decision graph. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor implements the steps of the method in any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 7. The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 7.

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