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

Abstract

The invention 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, adopting a multivariate decision diagram to represent all feasible lower bound vectors meeting service requirements of the target network, and obtaining a feasible lower bound decision diagram; performing state space expansion on the feasible lower bound decision diagram to obtain a link feasible capacity decision diagram; based on the link set of the target network, representing the capacity state of each link of the target network by adopting a multivariate decision diagram to obtain a link state decision diagram of each link; combining the link feasible capacity decision diagram and the link state decision diagrams of all links of the target network to obtain a capacity reliability decision diagram; and obtaining the capacity reliability of the target network by using the capacity reliability decision diagram. By adopting the method, a reliability evaluation method can be expanded 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 field of network operation and maintenance technology, and in particular to a network reliability assessment method, device, computer equipment, storage medium and program product. Background Art

[0002] Assessing network reliability and providing network reliability evaluation indicators are of key guiding significance to network management and operation and maintenance, and are also an indispensable part of network optimization and design.

[0003] However, the reliability assessment method for network-carrying service needs in traditional technologies only considers the service carrying capacity of a single-layer network, and when using the inclusion-exclusion principle to calculate reliability, it is necessary to assume that link failures are independent, resulting in the inability to extend the reliability assessment method to multi-layer networks, and thus the inability to accurately describe complex, multi-layer actual networks. Summary of the invention

[0004] Based on this, it is necessary to provide a network reliability assessment method, apparatus, computer equipment, storage medium and program product that can accurately describe the actual network in response to the above technical problems.

[0005] In a first aspect, in one embodiment, the present application provides a network reliability assessment method, the method comprising:

[0006] 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 business requirements of the target network, and a feasible lower bound decision graph is obtained;

[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 business requirements;

[0008] Based on the link set of the target network, a multivariate decision diagram is used to represent the capacity state of each link of the target network, so as to obtain a link state decision diagram of each link; the link state decision diagram 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 status decision graph of all links in the target network are combined to obtain a capacity reliability decision graph;

[0010] The capacity reliability decision diagram is used to obtain the capacity reliability of the target network.

[0011] In one embodiment, 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, and a feasible lower bound decision graph is obtained, including:

[0012] According to the link set, determine the nodes of the feasible lower bound decision graph;

[0013] Determine the terminal nodes of the feasible lower bound decision graph based on whether the target network can meet business requirements;

[0014] According to the capacity status of each link in the link set, the arcs of the feasible lower bound decision graph are determined; the arcs of the feasible lower bound decision graph represent the capacity lower bounds of the corresponding links;

[0015] Based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph, a feasible lower bound decision graph matching the target network is constructed;

[0016] Among them, a path from the starting node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds that meet business requirements; all feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph where the target network meets business requirements.

[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] Perform sorting and marking operations on each arc of the node of the feasible lower bound decision graph in ascending order according to the capacity, and obtain the target expansion arc corresponding to the node of the feasible lower bound decision graph;

[0019] Expand each target extension arc into a set of capacity sets to obtain the link feasible capacity decision graph;

[0020] Among them, the capacity set of the current target expansion arc of the link feasible capacity decision diagram 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 numbered 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.

[0021] In one embodiment, based on the link set of the target network, a multivariate decision diagram is used to represent the capacity state of each link of the target network to obtain a link state decision diagram of each link, including:

[0022] Based on all components and / or all events that affect the capacity state of the link, a link state decision graph component is obtained; wherein different links may contain the same link state decision graph component;

[0023] According to the link state decision graph component, determine the node of the link state decision graph;

[0024] Determine an arc of the link state decision graph according to a component state of the link state decision graph component;

[0025] According to the capacity value of the link, determine the terminal node of the link state decision graph;

[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 terminal 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 status 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 a capacity reliability decision graph is constructed;

[0030] According to the component set, determine the nodes of the capacity reliability decision diagram;

[0031] Based on the preset decision graph merging rule, 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 diagram is constructed based on a link set and a link state decision diagram component of a target network, including:

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

[0034] Among them, component collections do not count repeated components.

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

[0036] According to the capacity reliability decision diagram and the preset network capacity reliability formula, the corresponding capacity reliability of the target network when it meets 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 obtain a feasible lower bound decision graph based on all feasible lower bound vectors of the target network and using a multivariate decision graph to represent all feasible lower bound vectors that meet the business requirements of the target network;

[0039] A link feasible capacity decision graph construction module is used to expand the state space of 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 that meet business requirements;

[0040] A link state decision diagram construction module is used to represent the capacity state of each link of the target network based on the link set of the target network by using a multivariate decision diagram to obtain a link state decision diagram of each link; the link state decision diagram represents the impact of events and / or components of the target network on the capacity state of the link;

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

[0042] The capacity reliability evaluation module is used to obtain the capacity reliability of the target network using the capacity reliability decision diagram.

[0043] In a third aspect, in one embodiment, the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the method embodiments of the first aspect are implemented.

[0044] In a fourth aspect, in one embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in each method embodiment of the above-mentioned first aspect are implemented.

[0045] In a fifth aspect, in one embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the method embodiments of the above-mentioned first aspect when the computer program is executed by a processor.

[0046] The above-mentioned network reliability assessment method, device, computer equipment, storage medium and program product, the method is based on the capacity status of all links of the target network, and a multivariate decision diagram is used to represent all feasible lower bounds of the target network to obtain a feasible lower bound decision diagram, and then the state space of the feasible lower bound decision diagram is expanded to obtain a link feasible capacity decision diagram, and then based on the link set of the target network, a multivariate decision diagram is used to represent the capacity status of each link of the target network, thereby obtaining a link state decision diagram, and the link feasible capacity decision diagram and the link state decision diagram of all links of the target network are merged to obtain a capacity reliability decision diagram, and finally the capacity reliability of the target network is obtained through the capacity reliability decision diagram. The present application uses a multivariate decision diagram to evaluate the capacity reliability of the network, and can extend the reliability assessment method to multi-layer networks, thereby accurately describing the capacity reliability of complex and multi-layer actual networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic diagram of a flow chart of a network reliability evaluation method in one embodiment;

[0049] Figure 2 A schematic diagram of a process for constructing a feasible lower bound decision graph in one embodiment;

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

[0051] Figure 4 A schematic diagram of a process for constructing a link feasible capacity decision diagram in an embodiment;

[0052] Figure 5 A schematic diagram of a process of constructing a link state decision diagram in one embodiment;

[0053] Figure 6 A schematic diagram of a process for constructing a capacity reliability decision diagram in an embodiment;

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

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

[0056] Fig. 9 A schematic diagram of a feasible lower bound decision diagram in a quantitative evaluation system scenario in one embodiment;

[0057] Fig.10 A link feasible capacity decision diagram in a quantitative evaluation system scenario in one embodiment;

[0058] Fig.11 A schematic diagram of a decision graph merging operation in a quantitative evaluation system scenario in one embodiment;

[0059] Fig.12 is a structural block diagram of a network reliability evaluation device in one embodiment;

[0060] Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] At present, evaluating network reliability and providing network reliability evaluation indicators are of key guiding significance for network management and operation and maintenance, and are also an indispensable part of network optimization and design. Network reliability evaluation is also a key functional module of network planning systems, network simulation systems, and digital twin systems.

[0063] Since actual communication network links and nodes have upper capacity limits and the entire network has transmission capacity limitations, it is obviously more practical to evaluate the capacity reliability of the network's service carrying capacity than traditional technologies that only evaluate the network's connectivity reliability.

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

[0065] However, the model assumptions of traditional network capacity reliability assessment methods have the following defects, which lead to deviations between reliability assessment and actual conditions, as follows:

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

[0067] ② The traditional capacity reliability assessment model that only considers a single-layer network cannot directly reflect the transmission relationship of the underlying network failure. For example, in an IP+optical network, the routing of the IP layer and the optical layer converge independently in the current implementation, and it is difficult to model and represent the failure of the optical layer in the IP layer;

[0068] ③ The traditional capacity reliability assessment model does not make reasonable assumptions about the independence of network link failures: Taking IP networks as an example, from the perspective of IP networks, two links that seem to have no relationship may actually pass through the same computer room and be transmitted through the same physical link, resulting in the failure of the underlying physical link causing multiple links in the upper network to fail simultaneously. However, these failures cannot be specifically reflected from the perspective of the upper network.

[0069] ④ The transmission of common cause failures will cause even originally independent components to fail simultaneously when encountering regional or related events. For example, two nodes in different buildings in the same district will become unavailable at the same time when encountering a regional power outage. The traditional capacity reliability assessment model is also not applicable to the above common cause failure scenarios.

[0070] To facilitate the understanding of the technical solution of the present invention, the technical feature terms that may appear in the embodiments of the present invention are first explained:

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

[0072] MDD (Multiple-valued Decision Diagram): MDD is a decision diagram in which 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. It can be understood 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 is 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, to obtain a feasible lower bound decision graph.

[0075] The feasible lower bound vector may be a capacity lower bound vector that meets the service requirements of the target network. It can be 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 use a multivariate decision diagram (MDD) method to represent all feasible lower bound vectors of the target network that meet the business requirements based on all feasible lower bound vectors.

[0077] Exemplarily, all feasible flow vectors in the target network may be obtained by using a fast enumeration method or the like, and the capacity lower bound of the network link may be obtained based on the feasible flow vectors, thereby obtaining a capacity lower bound vector that meets the service requirements of the target network.

[0078] It should be noted that each feasible flow vector is a solution that can meet business needs. For example, when the target network has a traffic demand matrix, the feasible flow vector can represent that the traffic transmitted on all paths of the target network can eventually add up to meet the traffic demand matrix.

[0079] It is understandable that, since each link in the target network has different capacity states, for each feasible flow, each link can obtain the capacity lower bound that satisfies the feasible flow. For example, when link e is required to satisfy a feasible flow, the link capacity needs to reach 3 Mbps, and link The actual possible capacity states are 10mbps, 5mbps, and 0mbps. The lower bound of the capacity that satisfies this feasible flow is 5 mbps.

[0080] In some examples, you can use represents a link set of the target network, each link in the link set has multiple possible capacity states. , its existence capacity state, the link The corresponding state space is , where the state space exists It can be assumed that all feasible lower bound vectors that meet business requirements are obtained as ,in Indicates a total of This capacity lower bound vector can meet all business requirements. It can be understood that the following relationship exists at this time:

[0081] ,

[0082] in, Indicates capacity lower bound vector, Indicates Links, Indicates the total number of links in the target network, Represents the capacity lower bound vector Middle Link status, Indicates link The state space (i.e., capacity space) of the target network is greater than the lower bound. (Right now , that is ), it can be determined that the target network can meet business needs.

[0083] Optionally, by enumerating the capacity lower bound vectors of all possible states of the target network satisfying the service requirements, all feasible lower bound vectors may be obtained.

[0084] Step S104, performing state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph.

[0085] The link feasible capacity decision diagram can be used to characterize the feasible capacity state of all links in the target network that meet the service requirements. Exemplarily, the feasible capacity state is the capacity state of the link that meets the service requirements 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 diagram represents all feasible lower bound vectors that meet the business requirements of the target network, and the state space is expanded on the basis of the feasible lower bound decision diagram to obtain the link feasible capacity decision diagram. The link feasible capacity decision diagram can further represent all link capacity states that meet the business requirements of the target network.

[0087] Specifically, the feasible lower bound decision diagram obtained in step S102 is expanded in state space to further obtain an MDD representing all link capacity states that satisfy the service requirements of the target network, that is, a link feasible capacity decision diagram.

[0088] Step S106, based on the link set of the target network, a multivariate decision diagram is used to represent the capacity status of each link of the target network to obtain a link status decision diagram.

[0089] The link state decision diagram may characterize the impact of events and / or components of the target network on the capacity state of the link. Exemplarily, the above components are various link components such as optical nodes and optical links, which may affect the capacity state of the link; the above events are events such as power outages, natural disasters, network attacks, etc., which may affect the capacity state of the link.

[0090] Specifically, for each link of the target network, the server uses MDD to represent the capacity status of the link and the events and / or components that affect the capacity status of the link, and accordingly generates MDDs for the number of links of the target network, that is, generates a link status decision diagram corresponding to each link.

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

[0092] Among them, the capacity reliability decision diagram can be used to characterize 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 may be performed through a conjunction logic operation and / or a disjunction logic operation.

[0094] Specifically, the MDD obtained in step S104 representing all capacity states of the target network that meet business needs (i.e., link feasible capacity decision diagram) and the MDD obtained in step S106 representing the capacity state of each link (i.e., link state decision diagram) are merged to obtain the final MDD representing the capacity reliability of the target network, that is, to obtain the capacity reliability decision diagram.

[0095] Step S110, using the capacity reliability decision diagram to obtain the capacity reliability of the target network.

[0096] Among them, capacity reliability can characterize the network capacity reliability of the target network to meet the business traffic demand.

[0097] Specifically, the server may calculate the network reliability of the target network using an MDD (ie, a capacity reliability decision diagram) representing the capacity reliability of the target network.

[0098] The above network reliability assessment method, based on all feasible lower bound vectors of the target network, uses a multivariate decision diagram to represent all feasible lower bound vectors that meet the business needs of the target network, thereby obtaining a feasible lower bound decision diagram, and then the state space of the obtained feasible lower bound decision diagram can be expanded to obtain a link feasible capacity decision diagram, and then the corresponding link state decision diagram is constructed based on the link set of the target network and events and / or components, and after merging the link feasible capacity decision diagram and the link state decision diagram of all links of the target network, a capacity reliability decision diagram that can obtain the capacity reliability of the target network is obtained. Through the above method, the present application can expand the reliability assessment method to multi-layer networks, thereby accurately describing complex and multi-layer actual networks.

[0099] In one embodiment, if Figure 2 As shown, 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, and a feasible lower bound decision graph is obtained, including the following steps S202 to S208. Among them:

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

[0101] The nodes of the feasible lower bound decision graph may represent the links of the link set of the target network. It should be noted that the above nodes do not include terminal nodes.

[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] Step S204, determining the terminal nodes of the feasible lower bound decision graph according to whether the target network can meet the service requirements.

[0104] Among them, 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 business requirements.

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

[0106] Step S206, determining arcs of a feasible lower bound decision graph according to the capacity status of each link in the link set.

[0107] Among them, the arcs of the feasible lower bound decision graph can represent the capacity lower bound of the corresponding link in the target network.

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

[0109] Step S208, constructing a feasible lower bound decision graph that matches the target network based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph.

[0110] Among them, a path from the starting node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds that meet business requirements; all feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph where the target network meets business requirements.

[0111] Specifically, based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph determined in steps S202 to S206, a feasible lower bound decision graph matching the target network can be constructed accordingly, and the feasible lower bound decision graph can represent all feasible lower bound vectors that meet the business requirements of the target network.

[0112] Exemplarily, in order to further illustrate the specific forms of each multivariate decision diagram constructed in the present application, the present application provides the following Figure 3 An exemplary feasible lower bound decision diagram is shown. The exemplary feasible lower bound decision diagram is merely an explanation of the present application and does not constitute a limitation of the present application.

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

[0114] In one embodiment, if Figure 4 As shown, the feasible lower bound decision diagram is expanded in state space to obtain a link feasible capacity decision diagram, including steps S402 to S404. Among them:

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

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

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

[0118] Step S404: Expand each target extension arc into a set of capacity sets to obtain a link feasible capacity decision diagram.

[0119] Among them, the capacity set of the current target expansion arc of the link feasible capacity decision diagram 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 numbered 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, the state of each arc corresponding to the node of the feasible lower bound decision graph is expanded 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, and less than the capacity lower bound represented by the arc with the next serial number, and the capacity of the capacity set belongs to the state space of the link corresponding to the arc.

[0121] For example, assuming that 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 state lower bound is required to be expanded, 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, if Figure 5 As shown, based on the link set of the target network, a multivariate 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:

[0123] Step S502: obtaining a link state decision diagram component based on all components and / or all events that affect the capacity state of the link.

[0124] Different links may contain the same link state decision graph component.

[0125] Exemplarily, the components that affect the capacity state of the link may include various link components, for example, the components that affect the capacity state of the link may be optical nodes, optical links, etc. Further, events that affect the capacity state of the link may include power outages, natural disasters, network attacks, etc. It should be noted that any link may contain the same link state decision graph 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 that affect the capacity state of the link are obtained, and a corresponding set of link state decision graph components can be obtained based on the above components and / or events. The link state decision graph component can be used to represent any component or event that affects the capacity state of the link.

[0127] Step S504: determine the nodes of the link state decision graph according to the link state decision graph component.

[0128] The nodes of the link state decision graph represent the components (ie, the above-mentioned components and / or events) that affect the links of the target network.

[0129] Specifically, the server may determine the nodes of the link state decision graph according to the link state decision graph components obtained above.

[0130] Step S506, determining arcs of the link state decision graph according to the component states of the link state decision graph components.

[0131] The arcs of the link state decision diagram represent the component states of the corresponding components. Exemplarily, the component states may be binary states, which may indicate that an event corresponding to the component occurs / does not occur, or that a component corresponding to the component is normal / faulty.

[0132] It is understandable that the specific setting method of the above-mentioned component status is not limited to the implementation method mentioned in the above-mentioned embodiment. For example, the component status can also be set to other specific values. This application does not specifically limit the specific setting method of the component status.

[0133] Specifically, the server may determine the arcs of the link state decision graph according to the component states of the link state decision graph components.

[0134] Step S508: Determine the terminal node of the link state decision diagram according to the capacity value of the link.

[0135] For example, suppose the capacity space of link e is , then the link The corresponding terminal nodes of the link state decision graph are This terminal node indicates that in the current component state, the link The corresponding capacity value.

[0136] Specifically, the server may determine the terminal nodes of the link state decision graph based on the capacity values ​​of the links in the target network.

[0137] Step S510: construct a link state decision graph corresponding to each link of the target network based on the terminal nodes, nodes and arcs of the link state decision graph.

[0138] Among them, a path from the start node to the terminal node in the link state decision graph represents the capacity value of the link in the corresponding component state.

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

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

[0141] Step S602: construct a component set of a capacity reliability decision diagram based on the link set and link state decision diagram components of the target network.

[0142] The component set of the capacity reliability decision graph may include all links of the target network and link status decision graph components that affect the capacity status of the links.

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

[0144] Step S604: determining nodes of the capacity reliability decision diagram according to the component set.

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

[0146] Specifically, after constructing a component set of the capacity reliability decision graph, the nodes of the capacity reliability decision graph may be determined according to each capacity reliability decision graph component in the component set of the capacity reliability decision graph.

[0147] Step S606: Based on a preset decision graph merging rule, the link feasible capacity decision graph and the link status decision graph of each link are merged to obtain a capacity reliability decision graph.

[0148] Among them, a path of the capacity reliability decision diagram can represent a set of feasible states, that is, the feasible lower bound vector space.

[0149] Specifically, the preset decision graph merging rules are used to merge the link feasible capacity decision graph obtained above and the link status decision graph corresponding to each link in the target network to obtain a capacity reliability decision graph. The network reliability of the target network can be calculated using the capacity reliability decision graph.

[0150] For example, the merging operation of the multivariate decision diagram can be written as , then the specific preset decision diagram merging rules of the two decision diagrams can be expressed as the following operation process:

[0151]

[0152] in, Indicates that MDD has state; Represents an "if-else-then" algorithm; and Represents the variable, which is the current decision diagram node (MDD node) during the merge operation; and represents two different multivariate decision diagrams to be merged, Indicates the index value corresponding to the current node. It can be understood that when all nodes of the MDD are determined, the state of the terminal node of the MDD can be uniquely determined as Furthermore, in the above merging operation It can represent the "conjunction" operation or the "disjunction" operation in logical operations.

[0153] It can be understood that the above-mentioned preset decision graph merging rule can be used to merge the two MDDs represented by the logical expression and into one MDD. When applying the above-mentioned merging rule, it is necessary to compare the order indexes of the two root nodes (i.e. ).

[0154] For example, see Figure 7 , the preset decision graph merging rules can be as follows Figure 7 For example, if , indicating that they belong to the same component, 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] Furthermore, the decision graph merging operation for obtaining the capacity reliability decision graph can be expressed as follows:

[0156] ,

[0157] in, is the capacity reliability decision diagram, is the link feasible capacity decision diagram, to is the link state decision graph for each link in the target network.

[0158] In one embodiment, constructing a component set of a capacity reliability decision graph based on a link set and a link state decision graph component of a target network includes the following steps:

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

[0160] Among them, component collections do not count repeated components.

[0161] Specifically, based on the sorting basis of the link set, the components that affect the current link are added before the corresponding link; if there are repeated components, they are not counted repeatedly, and the smallest component order is used as the basis; among them, the smallest group order means that when sorting repeated components, the order of the components that appear for the first time is used as the basis.

[0162] For example, suppose there is a link , and each has components , , then the corresponding component set that can be finally obtained based on the above link set and components is ).

[0163] In one embodiment, obtaining the capacity reliability of the target network using the capacity reliability decision diagram includes:

[0164] According to the capacity reliability decision diagram and the preset network capacity reliability formula, the corresponding capacity reliability of the target network when it meets business needs is calculated.

[0165] Specifically, based on the capacity reliability decision diagram constructed above, the network capacity reliability formula can be used to calculate the corresponding capacity reliability when the target network meets the business needs, thereby determining the capacity reliability of the current target network.

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

[0167]

[0168] in, Represents the feasible state represented by the capacity reliability decision diagram, i.e., the feasible lower bound vector space; represents a set of feasible lower bound vectors; Indicates the capacity status of the link, Indicates 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 corresponding to 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. At this time, the capacity state space of the link is {0.1, 0.2, 0.7}.

[0169] This application uses a decision graph to evaluate the capacity reliability of the target network and sets the lower bound of the link capacity that the target network needs to meet business needs. A feasible lower bound decision diagram is formed, and the state space of the decision diagram is expanded to obtain a feasible capacity decision diagram representing the capacity states of all links of the target network that meet the business needs; then, the present application expands the components that affect the capacity of each link so that the dependencies between the links can be described by the decision diagram composed of all components, and because the components can be widely defined, the reliability assessment method of the present application can remove the independence assumption while extending the method to more universal scenarios, thereby more accurately describing complex and multi-layered actual networks.

[0170] In order to make the purpose, technical solution and advantages of this application more clear, this application is further described in detail by taking the network reliability evaluation method of this application applied to the quantitative evaluation system of the wide area network as an example. It should be noted that the specific embodiments described here are only used to explain this application and are not used to limit this application. The specific implementation method is as follows:

[0171] The evaluation requires input of all necessary components, the state of each component, the value and the probability of the link capacity corresponding to you, 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 method: , corresponding to the links arrive Then, calculate as follows:

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

[0174] ② If Fig.10 As shown, Expanding the state space yields (i.e., link feasible capacity decision diagram), through Indicates the capacity status of all links in the target network that meet business requirements.

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

[0176] ④ Such as Fig.11 As shown in Figure 1, the above multi-decision graphs are merged, that is, the link feasible capacity decision graph and all link status 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 is the link state decision diagram for each link in the network.

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

[0178] When the network reliability evaluation method of the present application is applied to the quantitative evaluation system of the wide area network, it can be used as an evaluation system to provide network quantitative evaluation results for the network planning system and the network simulation system. In fact, for the service reliability evaluation of the wide area network, it is necessary not only to consider whether the IP layer can carry the service capacity, but also to consider the impact of the underlying network on the IP link capacity. The network reliability evaluation method of the present application, by modeling the inter-layer mapping relationship of the multi-layer network, takes into account the dependency of network construction, 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 scenarios such as network attack and defense drills, network fault drills, etc. In these scenarios, the evaluation of network capacity reliability is also crucial. The network reliability evaluation method of the present application can, on the one hand, provide optimized quantitative indicators of reliability for network optimization, and on the other hand, provide optimization indicators for the optimization of network attack techniques.

[0180] For example, in network failure drills, a large number of regional failure events are usually involved, as well as force majeure factors such as disasters and destruction. These destructive events will have a significant impact on the network, and traditional network reliability assessment schemes cannot accurately consider the impact of such events on the network, so traditional assessment models cannot be applied to such scenarios. The network reliability assessment method of this application can model network attack events as one of the components of MDD for evaluating network reliability, and a component can affect the capacity status of multiple links, so that this application can fully consider the impact of failure events on network reliability.

[0181] It can be understood that based on the application of the network reliability assessment method of the present application in the above-mentioned scenarios, the present application includes at least the following beneficial technical effects: the method of the present application performs network capacity reliability assessment based on MDD, which provides scalability for the reliability assessment model; the method of the present application can directly connect with the existing reliability assessment method by expanding the state space of MDD, so that the capacity lower bound is converted into a decision diagram, thereby improving the adaptability of the present application; the method of the present application improves the scalability of the problem model by defining components as links of the target network and components and / or events that affect the target network links, and no longer needs to make fault independence assumptions, and fully considers the dependencies between various factors, so as to finally establish an accurate probability model; the present application can also be directly expanded on the original model, and the expansion process only needs to supplement the components and dependencies of the complete link, and the expansion process can be achieved through the merging of MDD.

[0182] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

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

[0184] In an exemplary embodiment, Fig.12 As shown, a network reliability evaluation device 120 is provided, comprising:

[0185] A feasible lower bound decision graph construction module 121 is used to obtain a feasible lower bound decision graph by using a multivariate decision graph to represent all feasible lower bound vectors that meet the service requirements of the target network based on all feasible lower bound vectors of the target network;

[0186] The link feasible capacity decision diagram construction module 123 is used to perform state space expansion on the feasible lower bound decision diagram to obtain a link feasible capacity decision diagram; the link feasible capacity decision diagram represents the feasible capacity state of all links in the target network that meet the service requirements;

[0187] The link state decision diagram construction module 125 is used to represent the capacity state of each link of the target network using a multivariate decision diagram based on the link set of the target network, so as to obtain a link state decision diagram of each link; the link state decision diagram represents the impact of events and / or components of the target network on the capacity state of the link;

[0188] The capacity reliability decision graph construction module 127 is used to merge the link feasible capacity decision graph and the link status decision graphs of all links of the target network to obtain a capacity reliability decision graph;

[0189] The capacity reliability evaluation module 129 is used to obtain the capacity reliability of the target network using the capacity reliability decision diagram.

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

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

[0192] Determine the terminal nodes of the feasible lower bound decision graph based on whether the target network can meet business requirements;

[0193] According to the capacity status of each link in the link set, the arcs of the feasible lower bound decision graph are determined; the arcs of the feasible lower bound decision graph represent the capacity lower bounds of the corresponding links;

[0194] Based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph, a feasible lower bound decision graph matching the target network is constructed;

[0195] Among them, a path from the starting node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds that meet business requirements; all feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph where the target network meets business requirements.

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

[0197] Perform sorting and marking operations on each arc of the node of the feasible lower bound decision graph in ascending order according to the capacity, and obtain the target expansion arc corresponding to the node of the feasible lower bound decision graph;

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

[0199] Among them, the capacity set of the current target expansion arc of the link feasible capacity decision diagram 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 numbered 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.

[0200] In one embodiment, the link state decision diagram construction module 125 is further configured to:

[0201] Based on all components and / or all events that affect the capacity state of the link, a link state decision graph component is obtained; wherein different links may contain the same link state decision graph component;

[0202] According to the link state decision graph component, determine the node of the link state decision graph;

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

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

[0205] 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;

[0206] Among them, a path from the start node to the terminal node in the link state decision graph represents the capacity value of the link in the corresponding component state.

[0207] In one embodiment, the capacity reliability decision diagram construction module 127 is further used to:

[0208] Based on the link set and link state decision graph components of the target network, a component set of a capacity reliability decision graph is constructed;

[0209] According to the component set, determine the nodes of the capacity reliability decision diagram;

[0210] 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.

[0211] In one embodiment, the capacity reliability decision diagram construction module 127 is further used to:

[0212] Based on the sorting of the link set, components affecting each link element in the link set are added before the corresponding link element to obtain a component set;

[0213] Among them, component collections do not count repeated components.

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

[0215] According to the capacity reliability decision diagram and the preset network capacity reliability formula, the corresponding capacity reliability of the target network when it meets business needs is calculated.

[0216] Each module in the network reliability evaluation device 120 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0217] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.13 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, 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. Among them, the processor of the computer device is used 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 operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device can be used to store relevant data of each multivariate decision diagram. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a network reliability assessment method is implemented.

[0218] Those skilled in the art will understand that Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may 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 further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0220] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

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

[0222] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0223] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0224] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations 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 shall be subject to the attached claims.

Claims

1. A network reliability evaluation method, characterized in that: The method comprises: 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; Expanding the state space of the feasible lower bound decision graph to obtain a link feasible capacity decision graph; the link feasible capacity decision graph represents the feasible capacity states of all links in the target network that meet the service requirements; Based on the link set of the target network, a multivariate decision diagram is used to represent the capacity state of each link of the target network, so as to obtain a link state decision diagram of each link; the link state decision diagram represents the influence of events and / or components of the target network on the capacity state of the link; Merging the link feasible capacity decision graph and the link status decision graphs of all links of the target network to obtain a capacity reliability decision graph; The capacity reliability of the target network is obtained by using the capacity reliability decision diagram.

2. The method according to claim 1, characterized in that: Based on all feasible lower bound vectors of the target network, a multivariate decision diagram is used to represent all feasible lower bound vectors that meet the service requirements of the target network, and a feasible lower bound decision diagram is obtained, including: Determine the nodes of the feasible lower bound decision graph according to the link set; Determine the terminal nodes of the feasible lower bound decision graph according to whether the target network can meet the service demand; Determining arcs of the feasible lower bound decision graph according to the capacity status of each link in the link set; the arcs of the feasible lower bound decision graph represent the capacity lower bounds of the corresponding links; Based on the terminal nodes, nodes and arcs of the feasible lower bound decision graph, construct the feasible lower bound decision graph matching the target network; Among them, a path from the starting node to the terminal node in the feasible lower bound decision graph represents a set of feasible lower bounds that meet the business requirements; all the feasible lower bounds in the feasible lower bound decision graph point to the terminal node of the feasible lower bound decision graph where the target network meets the business requirements.

3. The method according to claim 1, characterized in that The state space expansion of the feasible lower bound decision graph to obtain a link feasible capacity decision graph includes: Performing a sorting and marking operation on each arc of the node of the feasible lower bound decision graph in ascending order according to the capacity, to obtain a target expansion arc corresponding to the node of the feasible lower bound decision graph; Expanding each of the target extension arcs into a set of capacity sets to obtain the link feasible capacity decision graph; Among them, the capacity set of the current target expansion arc of the link feasible capacity decision diagram is greater than or equal to the lower bound of the capacity represented by the target expansion arc before expansion, and is smaller than the lower bound of the capacity represented by the target expansion arc of the next sequence 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 according to claim 1, characterized in that The link set based on the target network, using a multivariate decision diagram to represent the capacity state of each link of the target network to obtain a link state decision diagram of each link, includes: Based on all the components and / or all the events that affect the capacity state of the link, a link state decision diagram component is obtained; wherein different links may contain the same link state decision diagram component; According to the link state decision graph component, determining the nodes of the link state decision graph; Determining arcs of the link state decision graph according to component states of the link state decision graph components; Determining a terminal node of the link state decision graph according to the capacity value of the link; Based on the terminal nodes, nodes and arcs of the link state decision graph, construct the link state decision graph corresponding to each link of the target network; Among them, a path from a starting node to a terminal node in the link state decision graph represents the capacity value of the link in the corresponding component state.

5. The method according to claim 1, characterized in that The step of merging the link feasible capacity decision graph and the link status decision graphs of all links of the target network to obtain a capacity reliability decision graph includes: Constructing a component set of the capacity reliability decision diagram based on the link set of the target network and the link state decision diagram component; Determining nodes of the capacity reliability decision graph according to the component set; Based on a preset decision graph merging rule, the link feasible capacity decision graph and the link status decision graph of each link are merged to obtain a capacity reliability decision graph.

6. The method according to claim 5, characterized in that The component set of constructing the capacity reliability decision diagram based on the link set and the link state decision diagram component of the target network includes: Based on the sorting 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; The component set does not include the components that appear repeatedly.

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 diagram includes: According to the capacity reliability decision diagram, combined with a preset network capacity reliability formula, the capacity reliability corresponding to when the target network meets the business demand is calculated.

8. A network reliability assessment device, characterized in that: The device comprises: A feasible lower bound decision graph construction module is used to obtain a feasible lower bound decision graph by using a multivariate decision graph to represent all feasible lower bound vectors that meet the service requirements of the target network based on all feasible lower bound vectors of the target network; A link feasible capacity decision diagram construction module is used to perform state space expansion on the feasible lower bound decision diagram to obtain a link feasible capacity decision diagram; the link feasible capacity decision diagram represents the feasible capacity state of all links in the target network that meet the business requirements; A link state decision diagram construction module is used to represent the capacity state of each link of the target network using a multivariate decision diagram based on the link set of the target network, so as to obtain a link state decision diagram of each link; the link state decision diagram represents the impact of events and / or components of the target network on the capacity state of the link; A capacity reliability decision graph construction module is used to merge the link feasible capacity decision graph and the link status decision graphs of all links of the target network to obtain a capacity reliability decision graph; A capacity reliability evaluation module is used to obtain the capacity reliability of the target network using the capacity reliability decision diagram.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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