Network connectivity reliability detection method and apparatus, computer device, readable storage medium, and program product

Abstract

The application relates to a network connectivity reliability detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining the dependency relationship of each link component and each influence component of a target network according to link component information, influence component information and component dependency information of the target network; constructing a component sequence according to the dependency relationship; constructing a connectivity ordered decision graph containing each link component according to the component sequence, and constructing an ordered decision subgraph corresponding to the link state of each link component; according to the component sequence, sequentially merging the ordered decision subgraph corresponding to each link component into the connectivity ordered decision graph to obtain a target connectivity ordered decision graph of the target network; and obtaining the connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connectivity ordered decision graph. The method can realize more accurate detection of network connectivity reliability.

Description

TECHNICAL FIELD

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

[0002] Solving the network connectivity reliability is the basis for evaluating the network reliability, which has important reference value for the network redundancy protection strategy and planning optimization design.

[0003] In the traditional technology, the connectivity reliability of the network is usually calculated based on the assumption that the occurrence probability of link failure in the network is independent of each other. However, in the actual network environment, different links may depend on the same low-layer network component at the same time, or may be affected by the same influencing factor at the same time, so that the occurrence probability of link failure is not independent of each other. Therefore, it is difficult to accurately evaluate the connectivity reliability of the network based on the assumption of link failure independence in the traditional technology. SUMMARY

[0004] Therefore, it is necessary to provide a network connectivity reliability detection method, device, computer equipment, computer readable storage medium and computer program product for the above technical problems.

[0005] In a first aspect, the present application provides a network connectivity reliability detection method, comprising:

[0006] According to the link component information, the influencing component information and the component dependency information of the target network, the dependency relationship between each link component and each influencing component of the target network is determined;

[0007] According to the dependency relationship, a component sequence of the target network is constructed; wherein the order of each component in the component sequence is prior to the order of the dependent component that depends on the component;

[0008] According to the component sequence, a connectivity ordered decision graph containing each link component is constructed, and an ordered decision subgraph corresponding to the link state of each link component is constructed respectively; the ordered decision subgraph contains the link component and each influencing component that the link component depends on;

[0009] According to the component sequence, the ordered decision subgraph corresponding to each link component is merged into the connectivity ordered decision graph in turn, and the target connectivity ordered decision graph of the target network is obtained;

[0010] According to the connectivity probability of each path in the target connectivity ordered decision graph, the connectivity reliability detection result of the target network is obtained.

[0011] In one of the embodiments, the constructing the component sequence of the target network according to the dependency relationship comprises: obtaining a first order of each link component, constructing a component sequence containing the link components according to the first order; determining an influence dependency relationship between each influence component according to the dependency relationship, and secondly ordering each influence component according to the influence dependency relationship; and judging whether each influence component relied by each link component in the component sequence exists in the component sequence according to the dependency relationship in sequence, and if not, each influence component relied by the link component is added to a previous position of the link component in the component sequence in sequence according to the second order.

[0012] In one of the embodiments, the merging each ordered decision subgraph corresponding to each link component into the connected ordered decision graph in sequence according to the component sequence to obtain a target connected ordered decision graph of the target network comprises: taking the ordered decision subgraph of the first link component in the component sequence as a to-be-merged subgraph; merging each component node of the to-be-merged subgraph into the connected ordered decision graph in sequence according to the order of each component node of the to-be-merged subgraph and the connected ordered decision graph in the component sequence to obtain an updated connected ordered decision graph; taking the ordered decision subgraph of each non-first link component in the component sequence as the to-be-merged subgraph in sequence, and merging each component node of the to-be-merged subgraph into the connected ordered decision graph in sequence according to the order of each component node of the to-be-merged subgraph and the connected ordered decision graph in the component sequence to obtain a connected ordered decision graph updated in this time; and obtaining the target connected ordered decision graph according to the latest connected ordered decision graph.

[0013] In one of the embodiments, the merging the component nodes of the to-be-merged subgraph into the connected ordered decision graph in sequence according to the order of the component nodes in the component sequence and the connected ordered decision graph to obtain a new connected ordered decision graph comprises: taking a root node of the connected ordered decision graph as a first node and taking a root node of the to-be-merged subgraph as a second node; comparing the order of the first node and the second node in the component sequence; if the order of the second node precedes the order of the first node, adding the second node as a parent node of the first node to the connected ordered decision graph and taking a child node of the second node in the ordered decision subgraph as a new second node; if the order of the second node lags behind the order of the first node, taking a child node of the first node as a new first node; if the orders of the first node and the second node are the same, merging the second node into the first node, taking a child node of the first node as a new first node and taking a child node of the second node in the ordered decision subgraph as a new second node; repeating the step of comparing the order of the first node and the second node in the component sequence until all the component nodes of the to-be-merged subgraph are merged into the connected ordered decision graph.

[0014] In one of the embodiments, the obtaining the first ranking of the link components comprises: determining the link components of the target network according to the link component information; and obtaining the first ranking of the link components by using a search algorithm with the objective of minimizing the size of the target connected ordered decision graph of the target network.

[0015] In one of the embodiments, the component dependency information comprises dependency constraints of the target network; and after the merging the ordered decision subgraph corresponding to each link component into the connected ordered decision graph in sequence according to the component sequence to obtain the target connected ordered decision graph of the target network, the method further comprises: if the target connected ordered decision graph contains an invalid path that does not conform to the dependency constraints of the target network, deleting the invalid path from the target connected ordered decision graph.

[0016] In one of the embodiments, after the constructing the component sequence of the target network according to the dependency relationship, the method further comprises: if there are multiple first influence components that are dependent on the same dependent component in the component sequence and the influence of the component state of any first influence component on the dependent component is the same, establishing a same-influence component set containing each first influence component and replacing each first influence component in the component sequence with the same-influence component set.

[0017] In one of the embodiments, before the component sequence of the target network is constructed according to the dependency relationship, the method further comprises: if there are multiple second influence components having a probabilistic dependency relationship with the same dependent component in the component sequence, constructing a component power set corresponding to the multiple second influence components; taking the influence probability of each element in the component power set as a third influence component respectively; wherein each third influence component depends on the second influence component, and each third influence component is dependent on the dependent component; and updating the dependency relationship according to the component dependency relationship between the third influence component and the second influence component and the dependent component.

[0018] In a second aspect, the present application further provides a network connectivity reliability detection device, comprising:

[0019] a dependency relationship determination module configured to determine a dependency relationship of each link component and each influence component of a target network according to link component information, influence component information and component dependency information of the target network;

[0020] a component sequence construction module configured to construct a component sequence of the target network according to the dependency relationship; wherein the order of each component in the component sequence is prior to the order of a dependent component dependent on the component;

[0021] a first decision graph construction module configured to construct a connectivity ordered decision graph containing each link component according to the component sequence, and construct an ordered decision subgraph corresponding to a link state of each link component respectively; the ordered decision subgraph contains the link component and each influence component dependent on the link component;

[0022] a second decision graph construction module configured to merge the ordered decision subgraph corresponding to each link component into the connectivity ordered decision graph in turn according to the component sequence, to obtain a target connectivity ordered decision graph of the target network;

[0023] a structure acquisition module configured to obtain a connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connectivity ordered decision graph.

[0024] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0025] determining a dependency relationship of each link component and each influence component of a target network according to link component information, influence component information and component dependency information of the target network;

[0026] constructing a component sequence of the target network according to the dependency relationship, wherein an order of each component in the component sequence precedes an order of a dependent component that depends on the component;

[0027] constructing a connected and ordered decision graph containing each link component according to the component sequence, and constructing an ordered decision sub-graph corresponding to a link state of each link component, respectively; the ordered decision sub-graph containing the link component and each influence component that the link component depends on;

[0028] merging the ordered decision sub-graph corresponding to each link component into the connected and ordered decision graph in sequence according to the component sequence, to obtain a target connected and ordered decision graph of the target network;

[0029] obtaining a connected reliability detection result of the target network according to a connected probability of each path in the target connected and ordered decision graph.

[0030] In a fourth aspect, the present application further provides a computer readable storage medium, having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0031] determining a dependency relationship of each link component and each influence component of a target network according to link component information, influence component information and component dependency information of the target network;

[0032] constructing a component sequence of the target network according to the dependency relationship, wherein an order of each component in the component sequence precedes an order of a dependent component that depends on the component;

[0033] constructing a connected and ordered decision graph containing each link component according to the component sequence, and constructing an ordered decision sub-graph corresponding to a link state of each link component, respectively; the ordered decision sub-graph containing the link component and each influence component that the link component depends on;

[0034] merging the ordered decision sub-graph corresponding to each link component into the connected and ordered decision graph in sequence according to the component sequence, to obtain a target connected and ordered decision graph of the target network;

[0035] obtaining a connected reliability detection result of the target network according to a connected probability of each path in the target connected and ordered decision graph.

[0036] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, the computer program being executed by a processor to implement the following steps:

[0037] determining dependency relationships of each link component and each influence component of the target network according to link component information, influence component information and component dependency information of the target network;

[0038] constructing a component sequence of the target network according to the dependency relationships; wherein the order of each component in the component sequence is prior to the order of a dependent component dependent on the component;

[0039] constructing a connected and ordered decision graph containing each link component according to the component sequence, and constructing an ordered decision sub-graph corresponding to a link state of each link component respectively; the ordered decision sub-graph contains the link component and each influence component dependent on the link component;

[0040] merging the ordered decision sub-graph corresponding to each link component into the connected and ordered decision graph according to the component sequence in sequence to obtain a target connected and ordered decision graph of the target network;

[0041] obtaining a connected reliability detection result of the target network according to the connected probability of each path in the target connected and ordered decision graph.

[0042] The network connected reliability detection method, device, computer equipment, computer readable storage medium and computer program product, first determine dependency relationships of each link component and each influence component of the target network according to link component information, influence component information and component dependency information of the target network, then construct a component sequence of the target network according to the dependency relationships, and make the order of each link component in the component sequence later than the order of each influence component dependent on the link component, then construct a connected and ordered decision graph containing each link component according to the component sequence, and construct an ordered decision sub-graph corresponding to a link state of each link component respectively, then merge the ordered decision sub-graph corresponding to each link component into the connected and ordered decision graph according to the component sequence in sequence to obtain a target connected and ordered decision graph of the target network, and finally obtain a connected reliability detection result of the target network according to the connected probability of each path in the target connected and ordered decision graph.

[0043] The scheme, in view of the characteristics that the connectivity of the links in the actual network and various influencing factors exist dependent relationship, takes each link and various influencing factors in the network as components, and constructs component sequence according to the dependent relationship between them, so that according to the component sequence, the connectivity ordered decision graph containing each link component and the ordered decision subgraph corresponding to each link component can be quickly constructed. By merging each ordered decision subgraph into the connectivity ordered decision graph in the order of the component sequence, the dependent relationship between each link component, each influencing component and various components in the network can be modeled, and the connectivity ordered decision graph capable of representing the connectivity of the target network can be obtained. Further, according to the connectivity ordered decision graph, the connectivity reliability of the target network can be detected in combination with the dependent relationship of each component in the network without assuming that the link faults of the target network are independent, which can realize more realistic detection of the connectivity reliability of complex networks such as multi-layer networks, virtual networks and logical networks, and can be applicable to scenarios with common cause failure events, so that more accurate connectivity reliability detection results can be obtained. Moreover, since the connectivity ordered decision graph is obtained by merging each link component corresponding ordered decision subgraph into the initial connectivity ordered decision graph, when solving the connectivity reliability detection result, the probability calculation can be performed according to the state superposition of the isomorphic subgraph, without saving the state of the entire connectivity ordered decision graph, so that the state space explosion and the situation that the calculation memory is insufficient to obtain the connectivity reliability detection result can be effectively avoided. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. 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 without creative labor.

[0045] Figure 1 A flowchart of a network connectivity reliability detection method in an embodiment;

[0046] Figure 2 A schematic diagram of the structure of a target network in an embodiment;

[0047] Figure 3 A schematic diagram of the connectivity ordered decision graph and the ordered decision subgraph of a network in an embodiment;

[0048] Figure 4 A schematic diagram of the target connectivity ordered decision graph of a network in an embodiment;

[0049] Figure 5 A flowchart of constructing a component sequence of a target network in an embodiment;

[0050] Figure 6 a flowchart of a process for obtaining a target connected ordered decision graph of a target network in an embodiment;

[0051] Figure 7 a flowchart of a process for merging decision graphs in an embodiment;

[0052] Figure 8 a flowchart of a process for obtaining a new connected ordered decision graph in an embodiment;

[0053] Figure 9 a flowchart of a merging rule of component nodes in a process for merging decision graphs in an embodiment;

[0054] Figure 10 a flowchart of a process for updating dependency relations in an embodiment;

[0055] Figure 11 a flowchart of an ordered decision subgraph of a link component in an embodiment;

[0056] Figure 12 a block diagram of a network connected reliability detection apparatus in an embodiment;

[0057] Figure 13 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0058] In order to make the objects, 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 should not be used to limit the present application.

[0059] In an embodiment, as shown in Figure 1 , a network connected reliability detection method is provided, and the embodiment takes the method applied to a server as an example. It should 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 realized through the interaction of the terminal and the server. In the embodiment, the method includes the following steps.

[0060] In step S101, the dependency relations of each link component and each influence component of a target network are determined according to link component information, influence component information and component dependency information of the target network.

[0061] The target network can be, but is not limited to, multi-layered networks, virtual networks, logical networks, etc. The link component information can include information about each link in the target network that needs to participate in the network connectivity reliability assessment. Based on the link component information, all link components corresponding to the target network can be identified. The influencing component information can include information about various influencing factors that can affect the connectivity of the target network. Based on the influencing component information, each influencing component corresponding to the target network can be identified. For example, the influencing components of the target network can include various things that can affect network connectivity and have a binary state (e.g., available or unavailable, occurred or not occurred). These can be links, nodes, and paths in the underlying network; various devices and optical cables in the physical network; natural disasters, network attacks, and other events; or a custom set including multiple influencing factors. Based on the given link components and the states of the influencing components, it can be determined whether the entire network satisfies multi-terminal connectivity requirements.

[0062] The component dependency information can include the relationships between each link and each influencing factor in the network, as well as the relationships between the influencing factors themselves. Based on this, the dependency relationships between each link component and each influencing component of the target network can be determined according to the link component information, influencing component information, and component dependency information. Specifically, the influencing component that a link component depends on can be an influencing factor that can affect the connectivity of that link, and other influencing components that an influencing component depends on can be other influencing factors that can affect the state of that influencing factor.

[0063] For example, with Figure 2 The network shown is used as an example for illustration. This network includes an IP layer and an optical layer, and contains IP links. Each link From the corresponding optical path Bearer. For example, link. By light path Bearer, link By light path bearer, link By light path Bearing. Among them, the optical path. By optical link Composition, optical path By optical link Composition, optical path By optical link Composition, optical path By optical link Composition. Based on this, the link can be... , , respectively as link components of the network, and the optical paths , , , , and the optical links , , , , , , respectively as impact components of the network, and the dependency between the components can be determined. Exemplarily, the link component depends on the impact components , , , , , while the impact component depends on the impact components , , , .

[0064] Optionally, in order to reduce the size of the ordered decision graph to be constructed subsequently, in the example of the network as shown in Figure 2 , a set of optical link components can also be constructed according to each optical link respectively: , and the set of optical link components is used as the component element of the decision graph. Among the set of optical link components, the state of any element being unavailable can cause the state of the component element represented by the set of optical link components to be unavailable, and the state of the component element represented by the set of optical link components being available requires all elements in the component set to be available. For example, , the state of any one of the components or being unavailable can cause the state of to be unavailable, and thus the state of any one of the components or being unavailable can cause the state of the components and to be unavailable, and cause the connectivity state of the link components and to be disconnected.

[0065] Among them, by converting various links, devices in the network structure, and various events that can affect the connectivity of the network into components in the system, the generality and scalability of the network model can be improved, so that it can be easily extended to multi-layer network, common cause failure events and other network scenarios.

[0066] In step S102, according to the dependency relationship, the component sequence of the target network is constructed.

[0067] According to the dependency relationship between the link components and the influence components of the target network, the link components and the influence components can be sorted, so as to construct the component sequence of the target network. In the component sequence of the target network, the order of the dependent component is prior to the order of the dependent component. The dependent component corresponding to a component can be a link component or an influence component dependent on the component.

[0068] For example, still taking the network shown in FIG. 3 as an example, the link components Figure 2 depend on the influence components , , , , in the component sequence corresponding to the network, the order of the component is prior to the order of the component , , . Similarly, the link components depend on the influence components , , , in the component sequence, the order of the component is prior to the order of the component , , . Moreover, since the influence components depend on , , depend on , , in the component sequence, the order of the component is prior to the order of the component , , and the order of the component is prior to the order of the component , . Based on this, the component sequence corresponding to the network shown in FIG. 3 can be exemplarily represented as Figure 2 .

[0069] By sorting the components according to the dependency relationship between the components to construct the component sequence, and then constructing the ordered decision graph according to the sequence, the size of the decision graph can be reduced.

[0070] In step S103, according to the component sequence, a connected ordered decision graph containing the link components is constructed, and an ordered decision subgraph corresponding to the link state of each link component is constructed.

[0071] ​Since the component sequence contains the dependency relationship between components of the target network, the construction of the connected ordered decision graph and each ordered decision sub-graph can be efficiently performed according to the component sequence. The ordered decision sub-graph corresponding to each link component contains the link component and each influence component on which the link component depends.

[0072] The connected ordered decision graph can be an ordered binary decision diagram (OBDD) for representing the overall connectivity of the target network, which is constructed according to the order of each link component in the component sequence. In the case of determining the state of each link component, the connectivity state of the target network can be determined according to the connected ordered decision graph.

[0073] The ordered decision sub-graph corresponding to the link component can be an ordered binary decision diagram for representing the connectivity of the link component, which is constructed according to each influence component on which the link component depends and the dependency relationship between these influence components. In the case of determining the state of each influence component on which the link component depends, the connectivity state of the corresponding link can be determined according to the ordered decision sub-graph.

[0074] For example, still taking the network as shown in Figure 2 as an example, the connected ordered decision graph corresponding thereto can be as shown in Figure 3 , and the ordered decision sub-graphs corresponding to the link components , , , in the network can be as shown in Figure 3 , , , respectively. In the connected ordered decision graph and the ordered decision sub-graphs as shown in Figure 3 , the non-terminal node (the circular node) represents the component, the solid arc represents the available state (or occurrence state) of the component, the dashed arc represents the unavailable state (or non-occurrence state) of the component, and the terminal node (the square node) corresponds to the connectivity state of the network or the link, wherein the terminal node being 1 corresponds to the network or the link being connected, and the terminal node being 0 corresponds to the network or the link being not connected.

[0075] In step S104, according to the component sequence, each ordered decision sub-graph corresponding to a link component is sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network.

[0076] In the process of merging the ordered decision sub-graphs into the connected ordered decision graph, the merging can be performed in combination with the dependency relationship between the components presented in the component sequence, so that the connected ordered decision graph after the merging can still accurately reflect the dependency relationship between the components.

[0077] In the process of merging the ordered decision sub-graphs into the connected ordered decision graph, the merging can be performed in combination with the dependency relationship between the components presented in the component sequence, so that the connected ordered decision graph after the merging can still accurately reflect the dependency relationship between the components.

[0078] For example, still taking the network as shown in FIG. 1 and the connected ordered decision graph and the ordered decision sub-graphs as shown in FIG. 2 and FIG. 3 as examples, the ordered decision sub-graphs corresponding to the link components L1, L2, L3 and L4 can be sequentially merged into the connected ordered decision graph according to the order of the link components in the component sequence, so that the target connected ordered decision graph as shown in FIG. 4 can be obtained. Figure 2 Figure 3 For example, still taking the network as shown in FIG. 1 and the connected ordered decision graph and the ordered decision sub-graphs as shown in FIG. 2 and FIG. 3 as examples, the ordered decision sub-graphs corresponding to the link components L1, L2, L3 and L4 can be sequentially merged into the connected ordered decision graph according to the order of the link components in the component sequence, so that the target connected ordered decision graph as shown in FIG. 4 can be obtained. Figure 4

[0079] In step S105, the connected reliability detection result of the target network can be obtained according to the connected probabilities of the paths in the target connected ordered decision graph.

[0080] In the target connected ordered decision graph, each path can correspond to a feasible component state set capable of connecting the target network. Based on this, the connected probability of the target network can be calculated according to the connected probabilities of the paths:

[0081]

[0082] wherein, P(H) is the connected probability of the target network H, P(X) represents that the target network is in a connected state given the component state X, X is a path (i.e. a feasible component state set) in the target connected ordered decision graph, and x is the component state on the path X. Based on this, the connected probability of the path can be obtained by calculating the product of the state probability values corresponding to the component states on the path, and the connected probability of the target network can be obtained by summing the connected probabilities of the paths. Then, the connected reliability detection result of the target network can be obtained according to the connected probability of the target network.

[0083] Based on this, the connected probability of the path can be obtained by calculating the product of the state probability values corresponding to the component states on the path, and the connected probability of the target network can be obtained by summing the connected probabilities of the paths. Then, the connected reliability detection result of the target network can be obtained according to the connected probability of the target network.

[0084] ​​​​​​​​​​​Since the target connected order decision graph is an order binary decision graph, according to the characteristics of the order binary decision graph, the probabilities of each path can be calculated in parallel, and the probabilities do not need to be calculated after the complete target connected order decision graph is generated. Instead, the probabilities can be calculated according to the state superposition of the isomorphic subgraph, and the state of the entire target connected order decision graph does not need to be saved. In this way, the problem of state space explosion and insufficient calculation memory leading to difficulty in obtaining the calculation result can be alleviated.

[0085] It can be understood that when the target network is a multi-layer network, the connectivity probability expression of the link of the next layer of the target network can be obtained by expanding the above formula, and the same modeling method is referred to for iterative modeling down to obtain the expression corresponding to the multi-layer network. In addition, all components in the multi-layer network and the dependency relationship therebetween can also be directly enumerated, and a target connected order decision graph containing these components and dependency relationships is established, and then the connectivity probability of the target network is calculated.

[0086] In the network connectivity reliability detection method, according to the characteristics that the connectivity of the link in the actual network and various influencing factors of the network have a dependency relationship, each link and various influencing factors in the network are taken as components, and a component sequence is constructed according to the dependency relationship therebetween. Therefore, according to the component sequence, the connected order decision graph containing each link component and the order decision subgraph corresponding to each link component can be quickly constructed. By merging each order decision subgraph into the connected order decision graph in the order of the component sequence, the dependency relationship between each link component, each influencing component, and various components in the network can be modeled, and the connected order decision graph capable of representing the connectivity of the entire target network can be obtained. Furthermore, according to the connected order decision graph, the connectivity reliability of the target network can be detected in combination with the dependency relationship between each component in the network without assuming that the link failure of the target network is independent. This can achieve more realistic detection of the connectivity reliability of complex networks such as multi-layer networks, virtual networks, and logical networks, and can be applicable to scenarios with common cause failure events, thereby obtaining more accurate connectivity reliability detection results. Moreover, since the connected order decision graph is obtained by merging the order decision subgraph corresponding to each link component into the initial connected order decision graph, the probability can be calculated according to the state superposition of the isomorphic subgraph when solving the connectivity reliability detection result, and the state of the entire connected order decision graph does not need to be saved. Therefore, the problem of state space explosion and insufficient calculation memory leading to difficulty in obtaining the connectivity reliability detection result can be effectively avoided.

[0087] In one example embodiment, as shown in FIG. 1, according to the dependency relationship, constructing the component sequence of the target network can include: Figure 5

[0088] ​Step S501, obtaining a first ordering of the link components, and constructing a component sequence containing the link components according to the first ordering.

[0089] The first ordering of the link components in the target network can be a pre-set arrangement order of the link components. Illustratively, the first ordering can be an ordering of the link components that can make the size of the target connectivity ordered decision graph of the target network as small as possible. In this step, an initial component sequence corresponding to the target network can be constructed based on the first ordering. For example, taking the network shown in FIG. 1 as an example, if the first ordering of the link components is Figure 2 , the component sequence

[0090] Step S502, determining the influence dependency relationship between the influence components according to the dependency relationship, and secondly ordering the influence components according to the influence dependency relationship.

[0091] According to the dependency relationship of each component in the target network, the mutual influence dependency relationship between the influence components can be determined, and then the influence components can be secondly ordered according to the influence dependency relationship.

[0092] For example, taking the network shown in FIG. 1 as an example, the influence components can include Figure 2 The influence dependency relationship between them can include: Depends on , Depends on , Depends on , Depends on Based on this, the influence components can be secondly ordered according to the influence dependency relationship, and the dependent influence components are arranged before the corresponding dependency components. Illustratively, the second ordering of the influence components can be: ​​​​​​​​​​​​​​​​​​​​​​​

[0093] In step S503, according to the dependency relationship, it is judged in sequence whether each influence component relied by each link component in the component sequence exists in the component sequence; if not, each influence component relied by the link component is added in sequence to the previous position of the link component in the component sequence according to the second sorting.

[0094] According to the dependency relationship, the influence components relied by each link component can be determined respectively. For each link component in the component sequence, it can be judged in sequence whether the influence components relied by the link component exist in the component sequence. If not, each influence component relied by the link component and not existing in the component sequence can be inserted in sequence to the previous position of the link component in the component sequence according to the order of the second sorting. When the influence components relied by the link component exist in the component sequence, the next link component can be judged.

[0095] For example, still taking the network shown in FIG. 1 as an example, assuming that the initial component sequence is Figure 2 , the second sorting of the influence components is: , , , , , , , , , the link component can be judged first. When it is judged that the influence components , , do not exist in the component sequence, the influence components , , can be added in sequence to the previous position of in the component sequence according to the order of the second sorting, and a new component sequence is obtained. Then, the link component can be judged. When it is judged that the influence components , do not exist in the component sequence, the influence components , can be added in sequence to the previous position of in the component sequence according to the order of the second sorting, and a new component sequence is obtained. Similarly, the same operation can be performed on the link component , and the influence components and relied by the link component and not existing in the component sequence are added in sequence to the previous position of in the component sequence according to the order of the second sorting, and a new component sequence is obtained.Inserting to the component sequence according to the second ordering Finally, the component sequence is obtained .

[0096] In this embodiment, by constructing the component sequence according to the first ordering of the link components, and then second-ordering the influence components according to the influence dependency relationship between the influence components, and inserting the influence components relied on by each link component into the component sequence according to the second ordering, the ordering of the components can be efficiently implemented, and the component sequence reflecting the dependency relationship between the components can be quickly constructed, thereby improving the overall detection efficiency of the network connectivity reliability.

[0097] In an exemplary embodiment, as shown in Figure 6 According to the component sequence, the ordered decision subgraph corresponding to each link component is sequentially merged into the connectivity ordered decision graph to obtain the target connectivity ordered decision graph of the target network, which can include:

[0098] Step S601, the ordered decision subgraph of the first link component in the component sequence is taken as the to-be-merged subgraph.

[0099] Step S602, according to the order of each component node in the connectivity ordered decision graph and the to-be-merged subgraph in the component sequence, each component node of the to-be-merged subgraph is sequentially merged into the connectivity ordered decision graph to obtain an updated connectivity ordered decision graph.

[0100] Step S603, the ordered decision subgraph of each non-first link component in the component sequence is sequentially taken as the to-be-merged subgraph, and each component node of the to-be-merged subgraph is sequentially merged into the connectivity ordered decision graph updated in the previous time according to the order of each component node in the connectivity ordered decision graph updated in the previous time and the to-be-merged subgraph in the component sequence, to obtain the connectivity ordered decision graph updated in the current time.

[0101] Step S604, the target connectivity ordered decision graph is obtained according to the latest connectivity ordered decision graph.

[0102] Specifically, in the process of sequentially merging the ordered decision subgraph into the connectivity ordered decision graph, the merging of the decision graph can be sequentially performed according to the order of each link component in the component sequence. Based on this, in step S601, the ordered decision subgraph corresponding to the first link component in the component sequence can be taken as the to-be-merged subgraph, and in step S602, the to-be-merged subgraph and the connectivity ordered decision graph are merged to obtain an updated connectivity ordered decision graph. In the merging process of the to-be-merged subgraph and the connectivity ordered decision graph, the dependency relationship between the component nodes contained in the two decision graphs can be determined by comparing the order of the component nodes in the component sequence, and then according to the dependency relationship, the component nodes in the to-be-merged subgraph and the corresponding paths can be merged into the connectivity ordered decision graph.

[0103] Specifically, a connected ordered decision graph is obtained by merging the ordered decision subgraph of the first link component with the connected ordered decision graph. In step S603, the ordered decision subgraphs corresponding to each non-first link component are sequentially selected as subgraphs to be merged according to the order of the link components in the component sequence. These subgraphs are then merged into the previously updated connected ordered decision graph in the same way as in S602, until the merging of the ordered decision subgraphs corresponding to all link components is completed, resulting in the latest connected ordered decision graph. Furthermore, in step S604, this latest connected ordered decision graph can be used as the target decision graph of the target network.

[0104] For example, suppose the connected ordered decision graph of the target network is as follows: Each link of the target network , corresponding to the ordered decision subgraph The process of merging the above decision graphs can then be represented as: ,in Let m be the target connectivity ordered decision graph of the target network, where m is the total number of link components in the target network. These are the merging rules for component nodes to be followed during the merging process of each subgraph to be merged with the connected ordered decision graph.

[0105] For example, as Figure 2 The network shown and as Figure 3 Taking the connected ordered decision graph and ordered decision subgraph shown as examples, assume that their component sequence is as follows: Then its decision graph merging process can be as follows: Figure 7 As shown. Among them, for a connected ordered decision graph... Ordered decision subgraph , , You can first Merge to We obtain the first updated connected ordered decision graph, and then... The second updated connected ordered decision graph is obtained by merging it with the first updated connected ordered decision graph. Finally, By merging the connected ordered decision graph of the second update, we can obtain the target connected ordered decision graph of the target network. .

[0106] In this embodiment, by merging the corresponding ordered decision subgraphs into the connected ordered decision graph according to the order of the link components in the component sequence, the merging of decision graphs can be achieved efficiently, thereby improving the efficiency of obtaining the target connected ordered decision graph.

[0107] In one exemplary embodiment, such as Figure 8As shown, according to the order of the component nodes of the connected ordered decision graph and the to-be-merged subgraph in the component sequence, the component nodes of the to-be-merged subgraph are sequentially merged into the connected ordered decision graph to obtain a new connected ordered decision graph, which can include:

[0108] In step S801, the root node of the connected ordered decision graph is taken as a first node, and the root node of the to-be-merged subgraph is taken as a second node.

[0109] In step S802, the order of the first node and the second node in the component sequence is compared.

[0110] In step S803, if the order of the second node precedes the order of the first node, the second node is added to the connected ordered decision graph as a parent node of the first node, and the child nodes of the second node in the ordered decision subgraph are taken as new second nodes.

[0111] In step S804, if the order of the second node is later than the order of the first node, the child nodes of the first node are taken as new first nodes.

[0112] In step S805, if the order of the first node and the second node is the same, the second node is merged into the first node, the child nodes of the first node are taken as new first nodes, and the child nodes of the second node in the ordered decision subgraph are taken as new second nodes.

[0113] In step S806, the step of comparing the order of the first node and the second node in the component sequence is repeatedly performed until all component nodes of the to-be-merged subgraph are merged into the connected ordered decision graph.

[0114] In the process of merging the to-be-merged subgraph into the connected ordered decision graph, the position where each component node of the to-be-merged subgraph should be inserted into the connected ordered decision graph can be determined by comparing the order of the component nodes in the two decision graphs in the component sequence.

[0115] Specifically, in step S801, the root node of the connected ordered decision graph can be taken as a first node, and the root node of the to-be-merged subgraph can be taken as a second node. Then, in step S802, the order of the first node and the second node in the component sequence can be obtained and compared.

[0116] If the order of the second node precedes the order of the first node, step S803 is performed, the second node is added to the connected ordered decision graph as a parent node of the first node, the first node remains unchanged, and the child nodes of the second node in the ordered decision subgraph are taken as new second nodes, and then step S806 can be performed.

[0117] Among them, if the order of the second node is after the order of the first node, then proceed to execute step S804, keep the second node unchanged, and use the child node of the first node as the new first node, and then it is possible to proceed to execute step S806.

[0118] Among them, if the orders of the first node and the second node are the same, then the first node and the second node correspond to the same component, so it is possible to turn to execute step S805, merge the first node and the second node, use the child node of the original first node as the new first node, and use the child node of the original second node as the new second node, and then it is possible to proceed to execute step S806.

[0119] In step S806, based on the latest first node and second node, the operation of step S802 can be re-executed until all the component nodes in the subgraph to be merged are all merged into the connected ordered decision diagram, so that this update of the connected ordered decision diagram can be completed.

[0120] Exemplarily, please refer to Figure 9 , assume is the connected ordered decision diagram, is the subgraph to be merged, is the merging rule of the component nodes followed during the merging of the subgraph to be merged and the connected ordered decision diagram, then merged into The process can be expressed as:

[0121]

[0122] In the formula, is the root node of G, and its order in the component sequence is i, and are respectively 's child nodes; is the root node of H, and its order in the component sequence is j, and are respectively 's child nodes.

[0123] Among them, as shown in the above formula, the orders of the root nodes of G and H in the component sequence can be compared first, and corresponding component node merging operations can be performed according to different comparison results. Among them, as Figure 9 shown in (a) in, in the case where i and j are equal, and can be merged as the root node of the merged decision diagram, and the operation is applied to their child nodes. And in the case where i < j, then as Figure 9 shown in (b) in, x can be used as the root node of the merged decision diagram, and the operation Applying to node and node y. Similarly, in the case of i>j, as shown in (c) of FIG. 6, y can be taken as the root node of the merged decision graph, and the operation Figure 9 is applied to node x and node , , Based on this, the nodes in the two decision graphs can be compared recursively, and all the nodes in (a) are merged into (b) in turn.

[0124] In this embodiment, in the merging process of the decision graph, the specific merging operation on the nodes is determined by comparing the order of the nodes in the component sequence in the decision graph to be merged, which can simplify the complex merging process into multiple judgments and processing of the nodes, thereby facilitating the improvement of the calculation efficiency of the merging process.

[0125] In an exemplary embodiment, obtaining the first ordering of the link components can include: determining the link components of the target network according to the link component information; and obtaining the first ordering of the link components by using a search algorithm, with the goal of minimizing the size of the target connected ordered decision graph of the target network.

[0126] The first ordering of the link components in the target network can be an ordering of the link components that can make the size of the target connected ordered decision graph of the target network as small as possible. Specifically, after determining the link components of the target network according to the link component information, a search algorithm such as a breadth-first search algorithm or a heuristic algorithm can be used to search for possible orderings of the link components, and the ordering that can make the size of the target connected ordered decision graph as small as possible can be selected as the first ordering of the link components in the target network.

[0127] In this embodiment, the search algorithm is used to explore the ordering of the link components, which can quickly obtain the first ordering of the link components that can make the size of the target connected ordered decision graph of the target network as small as possible, thereby effectively reducing the size of the decision graph and improving the calculation efficiency according to the first ordering.

[0128] In an exemplary embodiment, the component dependency information can include dependency constraints of the target network; after the ordered decision sub-graphs corresponding to the link components are merged into the connected ordered decision graph in turn according to the component sequence to obtain the target connected ordered decision graph of the target network, the method can further include: if the target connected ordered decision graph contains an invalid path that does not meet the dependency constraints of the target network, deleting the invalid path from the target connected ordered decision graph.

[0129] ​​The component dependency information may include dependency constraints in the target network, which may include constraints on the dependencies between components, as well as constraints on components and paths in the decision graph.

[0130] For example, for such Figure 2 The network shown may have the following dependency constraints: (1) if the optical link component set of an optical path is unavailable, then the optical path is unavailable; (2) an IP link is unavailable if and only if all optical paths carrying the IP link are unavailable; (3) the target connectivity ordered decision graph contains only the paths that make the target network connected.

[0131] Based on this, after merging the decision graphs to obtain the target connected ordered decision graph, invalid paths that do not meet the dependency constraints can be deleted, and only the paths that can make the target network connected can be retained.

[0132] In this embodiment, by deleting invalid paths that do not conform to dependency constraints in the target connected ordered decision graph, the size of the decision graph can be effectively reduced and the computational efficiency improved.

[0133] In an exemplary embodiment, after constructing the component sequence of the target network according to the dependency relationship, it may further include: if there are multiple first influencing components in the component sequence that are depended on by the same dependent component, and the component state of any first influencing component has the same effect on the dependent component, then a set of co-influencing components containing each first influencing component is established, and each first influencing component in the component sequence is replaced by the set of co-influencing components.

[0134] In cases where multiple influencing components in the target network may be simultaneously dependent on the same link component or influencing component, the dependent influencing component can be designated as the first influencing component, and the same link component or influencing component that depends on these first influencing components can be designated as the dependent component.

[0135] Among them, for multiple first-influenced components that the same dependent component depends on, if the component state of any one of these first-influenced components has the same impact on the dependent component, these first-influenced components can be combined into a set of components with the same influence, and each first-influenced component in the component sequence can be replaced by the set of components with the same influence.

[0136] For example, IP link e is composed of an optical path Bearing, optical path It can consist of multiple sets of sub-paths connected by multiple intermediate nodes. If it carries, then in each group of sub-paths, any sub-path Unavailability can render the entire group of subpaths unusable, thereby affecting the optical path. This impacts the availability of link e. Based on this, multiple sub-paths within the same group can be... As the first impact component of the light path , each group of sub-paths is combined into a same impact component set . Wherein any sub-path component in the same impact component set is unavailable, it can cause the corresponding same impact component set to be unavailable, and further affect the availability of the light path and the link e. Based on this, the same impact component set can be used to replace multiple sub-paths of the same group in the component sequence.

[0137] Exemplarily, after using the same impact component set to replace each first impact component, the connectivity probability calculation method of the link e can be represented as:

[0138]

[0139] Wherein, represents that the component state of the link e is connected, represents the component state of the light path , represents that the state of the light path is available, represents the component state of the same impact component set , represents the component state of the sub-path , represents the set of same impact component sets which have an impact on the light path .

[0140] In this embodiment, by constructing the same impact link set, multiple first impact components which have the same impact can be replaced by one component corresponding to the same impact link set, so that the size of the decision graph can be effectively reduced and the calculation efficiency can be improved.

[0141] In one exemplary embodiment, as shown in Figure 10 , before constructing the component sequence of the target network according to the dependency relationship, it can also include:

[0142] Step S1001, if there are multiple second impact components in the component sequence which have a probability dependency relationship with the same dependent component, a component power set corresponding to the multiple second impact components is constructed.

[0143] Wherein, the existence of the probability dependency relationship between the influence component and the dependent component can mean that in a certain state of the influence component, the state of the dependent component has a certain probability of being available and a certain probability of being unavailable. Exemplarily, the influence component can be geographical climate, natural disaster, emergency, network attack, electromagnetic interference, etc., and the dependent component can be a link, so when the state of the influence component is occurrence, the dependent component has a certain probability of being connected and a certain probability of being disconnected. Wherein, when the same dependent component and multiple second influence components in the component sequence exist a probability dependency relationship, the component power set corresponding to these second influence components can be constructed.

[0144] Exemplarily, taking the network as shown in Figure 2 , the occurrence of events such as network attack, natural disaster, machine room failure, etc. can affect different components of the network, thereby forming common cause failures, and each occurrence of a common cause failure event has a certain probability of leading to network disconnection. Wherein, when multiple common cause failures can all cause component x of the network to be unavailable with a certain probability, component x can be taken as the dependent component of these events, and these common cause failure events can be taken as the second influence component existing a probability dependency relationship with the dependent component x. Wherein, the second influence component set can be defined as all the second influence components affecting the dependent component x, and the power set of the second influence component set for solving is defined as , and C represents the second influence component set.

[0145] Wherein, it is assumed that the common cause failure event affects the connectivity of the link component , , and the common cause failure event affects the connectivity of the link component , then , are the second influence components affecting the link component , that is , and the corresponding component power set . Wherein, corresponds to , none of them occurs, corresponds to occurs and does not occur, corresponds to occurs and does not occur, corresponds to , occur at the same time.

[0146] Step S1002, the influence probability of each element in the component power set is respectively taken as a third influence component.

[0147] Wherein, for the component power set corresponding to the plurality of second influence components, the influence probability of each element therein is respectively taken as a third influence component. Wherein, the influence probability of each element can refer to the probability that the state of the dependent component is available when the element occurs. Wherein, each third influence component depends on the second influence component, and each third influence component is dependent on the dependent component.

[0148] Exemplarily, for the aforementioned component power set , Let the probability that the state of the link component is available (i.e. connected) when the element C occurs be represented as , , , , then the third influence component can include: , , . Wherein, each third influence component depends on the second influence component ,

[0149] , and is dependent on the dependent component .

[0150] Step S1003, according to the component dependency relationship between the third influence component and the second influence component and the dependent component, updating the dependency relationship.

[0151] Wherein, according to the component dependency relationship between the third influence component and the second influence component and the dependent component, the dependency relationship between each component in the target network can be updated, so that in subsequent processing, the component sequence of the target network can be constructed according to the new dependency relationship, and further, the ordered decision graph containing the third influence component and the corresponding dependency relationship can be constructed. Exemplarily, taking the third influence component including: , , Figure 11 as an example, the ordered decision subgraph of the link component can be constructed as shown in . Wherein, the connectivity probability of the link component

[0152] can be represented as:

[0153] Wherein, represents that the component state of the link component is connected, represents the probability that the link component remains connected when each event in the component power set of the second influence component occurs.

[0154] In this embodiment, for multiple second influence components having a probabilistic dependency relationship with the same dependent component in the component sequence, by constructing a corresponding component power set thereof, and taking the influence probability of each element in the component power set on the dependent component as a third influence component to participate in the connectivity reliability detection of the target network, more accurate modeling of the influence on the network components can be achieved, which is conducive to more accurate detection of the connectivity reliability of the target network.

[0155] It should be understood that, although each step in the flowchart involved in the above embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in the order indicated by 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 orders. Moreover, at least part of the steps in the flowchart involved in the above embodiments can include multiple steps or stages, which 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 sequential, but can be alternately executed with at least part of other steps or stages or steps or stages in other steps.

[0156] Based on the same inventive concept, the embodiments of the present application also provide a network connectivity reliability detection device for implementing the above-mentioned network connectivity reliability detection method. 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 connectivity reliability detection device embodiments provided below can refer to the limitations of the network connectivity reliability detection method in the above text, which will not be repeated here.

[0157] In one exemplary embodiment, as shown in Figure 12 a network connectivity reliability detection device 1200 is provided, comprising:

[0158] a dependency relationship determination module 1201 configured to determine the dependency relationship between each link component and each influence component of the target network according to the link component information, the influence component information and the component dependency information of the target network;

[0159] a component sequence construction module 1202 configured to construct a component sequence of the target network according to the dependency relationship; wherein the order of each component in the component sequence is prior to the order of the dependent component that depends on the component;

[0160] The first decision graph construction module 1203 is configured to construct a connected ordered decision graph containing each link component according to the component sequence, and construct an ordered decision subgraph corresponding to a link state of each link component; the ordered decision subgraph contains the link component and each influence component relied on by the link component;

[0161] The second decision graph construction module 1204 is configured to sequentially merge the ordered decision subgraph corresponding to each link component into the connected ordered decision graph according to the component sequence, to obtain a target connected ordered decision graph of the target network.

[0162] The structure acquisition module 1205 is configured to obtain a connected reliability detection result of the target network according to a connected probability of each path in the target connected ordered decision graph.

[0163] In an exemplary embodiment, the component sequence construction module 1202 is further configured to: acquire a first order of each link component, construct a component sequence containing the link components according to the first order; determine an influence dependency relationship between each influence component according to the dependency relationship, and secondly order each influence component according to the influence dependency relationship; and sequentially determine whether each influence component relied on by each link component in the component sequence exists in the component sequence according to the dependency relationship; if not, each influence component relied on by the link component is sequentially added to a previous position of the link component in the component sequence according to the second order.

[0164] In an exemplary embodiment, the component sequence construction module 1202 is further configured to: take the ordered decision subgraph of the first link component in the component sequence as a to-be-merged subgraph; sequentially merge each component node of the to-be-merged subgraph into the connected ordered decision graph according to the order of each component node in the component sequence, to obtain an updated connected ordered decision graph; take the ordered decision subgraph of each non-first link component in the component sequence as the to-be-merged subgraph, and sequentially merge each component node of the to-be-merged subgraph into the connected ordered decision graph according to the order of each component node in the component sequence, to obtain a connected ordered decision graph updated at this time; and obtain the target connected ordered decision graph according to the latest connected ordered decision graph.

[0165] In an exemplary embodiment, the component sequence construction module 1202 is further configured to: take a root node of the connected ordered decision graph as a first node, and take a root node of the to-be-merged subgraph as a second node; compare the order of the first node and the second node in the component sequence; if the order of the second node precedes the order of the first node, add the second node as a parent node of the first node to the connected ordered decision graph, and take a child node of the second node in the ordered decision subgraph as a new second node; if the order of the second node lags behind the order of the first node, take a child node of the first node as a new first node; if the orders of the first node and the second node are the same, merge the second node to the first node, and take a child node of the first node as a new first node, and take a child node of the second node in the ordered decision subgraph as a new second node; repeat the step of comparing the order of the first node and the second node in the component sequence until all component nodes of the to-be-merged subgraph are merged to the connected ordered decision graph.

[0166] In an exemplary embodiment, the component sequence construction module 1202 is further configured to: determine each link component of the target network according to the link component information; and obtain the first order of each link component by using a search algorithm, with the objective of minimizing the size of the target connected ordered decision graph of the target network.

[0167] In an exemplary embodiment, the component dependency information comprises dependency constraints of the target network; and the apparatus further comprises a path deletion module configured to delete an invalid path from the target connected ordered decision graph if the target connected ordered decision graph contains the invalid path that does not comply with the dependency constraints of the target network.

[0168] In an exemplary embodiment, the apparatus further comprises a first component adjustment module configured to, if there are multiple first impact components in the component sequence that are dependent on a same dependency component, and the impact of the component state of any first impact component on the dependency component is the same, establish a same-impact component set comprising each first impact component, and replace each first impact component in the component sequence with the same-impact component set.

[0169] In an exemplary embodiment, the apparatus further comprises: a component power set construction module, configured to, if there are a plurality of second influence components having a probabilistic dependency relationship with the same dependent component in the component sequence, construct a component power set corresponding to the plurality of second influence components; a second component adjustment module, configured to take the influence probability of each element in the component power set as a third influence component respectively; wherein each third influence component depends on the second influence component, and each third influence component is dependent on the dependent component; and a dependency relationship updating module, configured to update the dependency relationship according to the component dependency relationship between the third influence component and the second influence component and the dependent component.

[0170] Each of the above network connectivity reliability detection apparatuses can be implemented wholly or partially by software, hardware, and combinations thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform operations corresponding to each of the above modules.

[0171] In an exemplary embodiment, a computer device is provided, which can be a server, and an 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 is configured to store link component information, influence component information, and component dependency information, etc. 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 network connection. The computer program is executed by the processor to implement a network connectivity reliability detection method.

[0172] Those skilled in the art can understand that Figure 13 The structure shown in the above

[0173] In an embodiment, a computer device is also provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0174] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0175] In an embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0177] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to 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. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or 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 the embodiments provided in 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., and is not limited thereto. The processor involved in the embodiments provided in 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., and is not limited thereto.

[0178] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the range disclosed in the present application.

[0179] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which 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 reachability detection method, characterized by, The method comprises: determining the dependency relationship between each link component and each influence component of the target network according to the link component information, the influence component information and the component dependency information of the target network; constructing a component sequence of the target network according to the dependency relationship; wherein the order of each component in the component sequence is prior to the order of the dependent component that depends on the component; constructing a connected ordered decision graph containing each link component according to the component sequence, and constructing an ordered decision subgraph corresponding to the link state of each link component; the ordered decision subgraph contains the link component and each influence component that the link component depends on; merging the ordered decision subgraph corresponding to each link component into the connected ordered decision graph in sequence according to the component sequence to obtain a target connected ordered decision graph of the target network; obtaining the connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connected ordered decision graph.

2. The method of claim 1, wherein, The method comprises: obtaining a first ranking of each link component, and constructing a component sequence containing the link component according to the first ranking; determining the influence dependency relationship between each influence component according to the dependency relationship, and performing a second ranking on each influence component according to the influence dependency relationship; sequentially judging whether each influence component that each link component in the component sequence depends on exists in the component sequence according to the dependency relationship; if not, each influence component that the link component depends on is sequentially added to the position before the link component in the component sequence according to the second ranking.

3. The method of claim 2, wherein, The method comprises: taking the ordered decision subgraph of the first link component in the component sequence as a to-be-merged subgraph; sequentially merging each component node of the to-be-merged subgraph into the connected ordered decision graph according to the order of each component node of the to-be-merged subgraph and the connected ordered decision graph in the component sequence to obtain an updated connected ordered decision graph; taking the ordered decision subgraph of each non-first link component in the component sequence as the to-be-merged subgraph, and sequentially merging each component node of the to-be-merged subgraph into the connected ordered decision graph according to the order of each component node of the to-be-merged subgraph and the connected ordered decision graph in the component sequence to obtain a connected ordered decision graph updated at this time; obtaining the target connected ordered decision graph according to the latest connected ordered decision graph.

4. The method of claim 3, wherein, The method comprises: taking a root node of the connected ordered decision graph as a first node, and taking a root node of the to-be-merged subgraph as a second node; comparing orders of the first node and the second node in the component sequence; if the order of the second node precedes the order of the first node, adding the second node as a parent node of the first node to the connected ordered decision graph, and taking a child node of the second node in the ordered decision subgraph as a new second node; if the order of the second node succeeds the order of the first node, taking a child node of the first node as a new first node; if the orders of the first node and the second node are the same, merging the second node into the first node, and taking a child node of the first node as a new first node, and taking a child node of the second node in the ordered decision subgraph as a new second node; repeating the step of comparing the orders of the first node and the second node in the component sequence until all component nodes of the to-be-merged subgraph are merged into the connected ordered decision graph.

5. The method of claim 2, wherein, The obtaining the first ordering of each link component comprises: determining each link component of the target network according to the link component information; minimizing the size of the target connected ordered decision graph of the target network as an objective, and obtaining the first ordering of each link component by using a search algorithm.

6. The method of claim 1, wherein, The component dependency information comprises dependency constraints of the target network; After the merging each ordered decision subgraph corresponding to each link component into the connected ordered decision graph of the target network according to the component sequence to obtain a target connected ordered decision graph of the target network, the method further comprises: if the target connected ordered decision graph contains an invalid path that does not conform to the dependency constraints of the target network, deleting the invalid path from the target connected ordered decision graph.

7. The method of claim 1, wherein, After the constructing the component sequence of the target network according to the dependency relationship, the method further comprises: if there are multiple first influence components that are dependent on the same dependent component in the component sequence, and the influence of the component state of any first influence component on the dependent component is the same, establishing a same-influence component set containing each first influence component, and replacing each first influence component in the component sequence with the same-influence component set.

8. The method according to any one of claims 1 to 7, characterized in that, Before the constructing the component sequence of the target network according to the dependency relationship, the method further comprises: if there are multiple second influence components that have a probabilistic dependency relationship with the same dependent component in the component sequence, constructing a component power set corresponding to each second influence component; taking the influence probability of each element in the component power set as a third influence component, respectively; wherein each third influence component is dependent on the second influence component, and each third influence component is dependent on the dependent component; updating the dependency relationship according to the component dependency relationship between the third influence component and the second influence component and the dependent component.

9. A network reachability detection apparatus, comprising: The device comprises: A dependency relationship determining module is configured to determine dependency relationships between link components and influence components of the target network according to link component information, influence component information and component dependency information of the target network; A component sequence constructing module is configured to construct a component sequence of the target network according to the dependency relationships; wherein the order of each component in the component sequence is prior to the order of a dependent component dependent on the component; A first decision graph constructing module is configured to construct a connected and ordered decision graph containing each link component according to the component sequence, and construct an ordered decision sub-graph corresponding to a link state of each link component; the ordered decision sub-graph contains the link component and each influence component dependent on the link component; A second decision graph constructing module is configured to merge the ordered decision sub-graphs corresponding to each link component into the connected and ordered decision graph in sequence according to the component sequence, to obtain a target connected and ordered decision graph of the target network; A structure obtaining module is configured to obtain a connectivity reliability detection result of the target network according to connectivity probabilities of each path in the target connected and ordered decision graph. 10.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 1-9. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 7.

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

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

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