Network connection reliability detection method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a network connection reliability detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining a dependency relationship between 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; according to the component sequence, constructing a connected ordered decision diagram containing each link component, and respectively constructing an ordered decision sub-diagram corresponding to the link state of each link component; according to the component sequence, sequentially combining the ordered decision sub-graphs corresponding to the link components to a connected ordered decision graph to obtain a target connected ordered decision graph of the target network; and obtaining a connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connectivity ordered decision diagram. By adopting the method, more accurate detection of the network communication reliability can be realized.

Description

Technical Field

[0001] The present application relates to the field of communication network technology, and in particular to a network connectivity reliability detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

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

[0003] In traditional technologies, the network connectivity reliability is usually calculated based on the assumption that the probability of link failures in the network is independent of each other. However, in actual network environments, different links may rely on the same low-level network components at the same time, or may be affected by the same influencing factors at the same time, so the probability of link failures is not independent of each other. Therefore, it is difficult to accurately evaluate the network connectivity reliability based on the assumption of link failure independence in traditional technologies. Summary of the invention

[0004] Based on this, it is necessary to provide a network connectivity reliability detection method, apparatus, computer equipment, computer-readable storage medium and computer program product to address the above technical issues.

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

[0006] Determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, the influencing component information and the component dependency information of the target network;

[0007] According to the dependency relationship, construct a component sequence of the target network; wherein the order of each component in the component sequence precedes the order of dependent components that depend on the component;

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

[0009] According to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network;

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

[0011] In one of the embodiments, constructing the component sequence of the target network according to the dependency relationship includes: obtaining a first ranking of each of the link components, and constructing a component sequence including the link components according to the first ranking; determining the impact dependency relationship between each of the impact components according to the dependency relationship, and performing a second ranking on each of the impact components according to the impact dependency relationship; judging in turn according to the dependency relationship whether each of the impact components that each of the link components in the component sequence depends on exists in the component sequence; if not, each of the impact components that the link component depends on is added in turn to the previous position of the link component in the component sequence according to the second ranking.

[0012] In one of the embodiments, according to the component sequence, the ordered decision subgraphs corresponding to each of the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network, including: taking the ordered decision subgraph of the first link component in the component sequence as the subgraph to be merged; according to the order of the connected ordered decision graph and the component nodes of the subgraph to be merged in the component sequence, sequentially merging the component nodes of the subgraph to be merged into the connected ordered decision graph to obtain an updated connected ordered decision graph; taking the ordered decision subgraphs of each non-first link component in the component sequence as the subgraph to be merged in turn, and according to the connected ordered decision graph updated last time and the order of the component nodes of the subgraph to be merged in the component sequence, sequentially merging the component nodes of the subgraph to be merged into the connected ordered decision graph updated last time to obtain a connected ordered decision graph updated this time; and obtaining the target connected ordered decision graph based on the latest connected ordered decision graph.

[0013] In one embodiment, the method of merging the component nodes of the subgraph to be merged into the connected ordered decision graph in sequence according to the order of the connected ordered decision graph and the order of the component nodes of the subgraph to be merged in the component sequence to obtain a new connected ordered decision graph includes: taking the root node of the connected ordered decision graph as the first node and the root node of the subgraph to be merged as the second node; comparing the order of the first node and the second node in the component sequence; if the order of the second node is earlier than the order of the first node, adding the second node as the parent node of the first node to the connected ordered decision graph, and adding the second node as the parent node of the first node to the connected ordered decision graph; The child node of the node in the ordered decision subgraph is used as a new second node; if the order of the second node is later than the order of the first node, the child node of the first node is used as a new first node; if the order of the first node and the second node is the same, the second node is merged into the first node, and the child node of the first node is used as a new first node, and the child node of the second node in the ordered decision subgraph is used as a new second node; repeatedly perform the step of comparing the order of the first node and the second node in the component sequence until all component nodes of the subgraph to be merged are merged into the connected ordered decision graph.

[0014] In one embodiment, obtaining the first ranking of each of the link components includes: determining each of the link components of the target network based on the link component information; and obtaining the first ranking of each of the link components using a search algorithm with the goal of minimizing the size of the target connected ordered decision graph of the target network.

[0015] In one embodiment, the component dependency information includes dependency constraints of the target network; according to the component sequence, the ordered decision subgraphs corresponding to each of the link components are merged into the connected ordered decision graph in sequence to obtain the target connected ordered decision graph of the target network, and further includes: if the target connected ordered decision graph contains an invalid path that does not comply with the dependency constraints of the target network, the invalid path is deleted from the target connected ordered decision graph.

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

[0017] In one of the embodiments, before constructing the component sequence of the target network according to the dependency relationship, it also includes: if there are multiple second impact components in the component sequence that have a probabilistic dependency relationship with the same dependent component, then construct a component power set corresponding to the multiple second impact components; use the impact probability of each element in the component power set as a third impact component; wherein each of the third impact components depends on the second impact component, and each of the third impact components is dependent on the dependent component; and update the dependency relationship according to the component dependency relationship between the third impact component, the second impact component and the dependent component.

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

[0019] A dependency determination module, used to determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, influencing component information and component dependency information of the target network;

[0020] A component sequence construction module, used to construct a component sequence of the target network according to the dependency relationship; wherein the order of each component in the component sequence precedes the order of the dependent components that depend on the component;

[0021] A first decision graph construction module is used to construct a connected ordered decision graph including each of the link components according to the component sequence, and to respectively construct an ordered decision subgraph corresponding to the link state of each of the link components; the ordered decision subgraph includes the link component and each of the influencing components on which the link component depends;

[0022] A second decision graph construction module is used to merge the ordered decision subgraphs corresponding to each of the link components into the connected ordered decision graph in sequence according to the component sequence, so as to obtain a target connected ordered decision graph of the target network;

[0023] The structure acquisition module is used to obtain the 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, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0025] Determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, the influencing component information and the component dependency information of the target network;

[0026] According to the dependency relationship, construct a component sequence of the target network; wherein the order of each component in the component sequence precedes the order of dependent components that depend on the component;

[0027] According to the component sequence, a connected ordered decision graph including each of the link components is constructed, and an ordered decision subgraph corresponding to the link state of each of the link components is constructed respectively; the ordered decision subgraph includes the link component and each of the influencing components that the link component depends on;

[0028] According to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network;

[0029] According to the connectivity probability of each path in the target connectivity ordered decision graph, a connectivity reliability detection result of the target network is obtained.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0031] Determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, the influencing component information and the component dependency information of the target network;

[0032] According to the dependency relationship, construct a component sequence of the target network; wherein the order of each component in the component sequence precedes the order of dependent components that depend on the component;

[0033] According to the component sequence, a connected ordered decision graph including each of the link components is constructed, and an ordered decision subgraph corresponding to the link state of each of the link components is constructed respectively; the ordered decision subgraph includes the link component and each of the influencing components that the link component depends on;

[0034] According to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network;

[0035] According to the connectivity probability of each path in the target connectivity ordered decision graph, a connectivity reliability detection result of the target network is obtained.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0037] Determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, the influencing component information and the component dependency information of the target network;

[0038] According to the dependency relationship, construct a component sequence of the target network; wherein the order of each component in the component sequence precedes the order of dependent components that depend on the component;

[0039] According to the component sequence, a connected ordered decision graph including each of the link components is constructed, and an ordered decision subgraph corresponding to the link state of each of the link components is constructed respectively; the ordered decision subgraph includes the link component and each of the influencing components that the link component depends on;

[0040] According to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network;

[0041] According to the connectivity probability of each path in the target connectivity ordered decision graph, a connectivity reliability detection result of the target network is obtained.

[0042] The above-mentioned network connectivity reliability detection method, device, computer equipment, computer-readable storage medium and computer program product first determine the dependency relationship between each link component and each influencing component of the target network based on the link component information, influencing component information and component dependency information of the target network, and then construct the component sequence of the target network based on the dependency relationship, and make the order of each link component in the component sequence later than the order of each influencing component on which the link component depends, and then construct a connected ordered decision graph containing each link component and an ordered decision subgraph corresponding to the link state of each link component according to the component sequence, and then merge the ordered decision subgraphs 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, and finally obtain the connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connected ordered decision graph.

[0043] In view of the fact that the connectivity of links in actual networks is dependent on various influencing factors of the network, this solution takes each link and various influencing factors in the network as components, and constructs a component sequence according to the dependencies between them. Thus, according to the component sequence, a connectivity ordered decision graph containing each link component and an 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, it is possible to model the dependencies between each link component, each influencing component and various components in the network, and obtain a connectivity ordered decision graph that can represent the connectivity of the entire target network. Furthermore, according to the connectivity ordered decision graph, the connectivity reliability of the target network can be detected in combination with the dependencies of each component in the network without assuming that the link failure of the target network is independent, so that the connectivity reliability of complex networks such as multi-layer networks, virtual networks, and logical networks can be detected in a more practical scenario, and it can also be applied to scenarios such as common cause failure events, so that more accurate connectivity reliability detection results can be obtained. Moreover, since the connected ordered decision graph is obtained by merging the ordered decision subgraphs corresponding to each link component into the initial connected ordered decision graph, when solving the connectivity reliability test results, probability calculation can be performed based on the state superposition of the isomorphic subgraphs without having to save the state of the entire connected ordered decision graph, thereby effectively avoiding the explosion of the state space and the difficulty in obtaining connectivity reliability test results due to insufficient computing memory. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] FIG1 is a schematic diagram of a flow chart of a method for detecting network connectivity reliability in one embodiment;

[0046] FIG2 is a schematic diagram of the structure of a target network in one embodiment;

[0047] FIG3 is a schematic diagram of a connected ordered decision graph and ordered decision subgraphs of a network in one embodiment;

[0048] FIG4 is a schematic diagram of a target connectivity ordered decision graph of a network in one embodiment;

[0049] FIG5 is a schematic diagram of a flow chart of a component sequence for constructing a target network in one embodiment;

[0050] FIG6 is a schematic diagram of a process of obtaining a target connected ordered decision graph of a target network in one embodiment;

[0051] FIG7 is a schematic diagram of a decision graph merging process in one embodiment;

[0052] FIG8 is a schematic diagram of a process for obtaining a new connected ordered decision graph in one embodiment;

[0053] FIG9 is a schematic diagram of a merging rule of component nodes in a decision graph merging process according to an embodiment;

[0054] FIG10 is a schematic diagram of a process for updating a dependency relationship in one embodiment;

[0055] FIG11 is a schematic diagram of an ordered decision subgraph of a link component in one embodiment;

[0056] FIG12 is a structural block diagram of a network connectivity reliability detection device in one embodiment;

[0057] FIG. 13 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] 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.

[0059] In one embodiment, as shown in FIG1 , a method for detecting network connectivity reliability is provided. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0060] Step S101 : Determine the dependency relationship between each link component and each influencing component of the target network according to the link component information, influencing component information and component dependency information of the target network.

[0061] The target network may be, but is not limited to, a multi-layer network, a virtual network, a logical network, and the like. The link component information may include information about each link in the target network that needs to participate in the network connectivity reliability assessment, and all link components corresponding to the target network may be determined based on the link component information. The influencing component information may include information about various influencing factors that may affect the connectivity of the target network, and the influencing components corresponding to the target network may be determined based on the influencing component information. Exemplarily, the influencing components of the target network may include various things that may affect the connectivity of the network and may have a binary state (e.g., available or unavailable, occurring or not occurring, etc.), such as links, nodes, paths of the underlying network, various devices and optical cables in the physical network, etc., as well as natural disasters, network attacks, and other events, and may also be a customized set of multiple influencing factors. Based on the given link components and the states of the influencing components, it may be determined whether the entire network meets the connectivity of multiple terminals.

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

[0063] For example, the network shown in FIG2 is used as an example for explanation. The network includes an IP layer and an optical layer, which includes an IP link , each link By the corresponding light path For example, the link By light path Bearer, Link By light path Bearer, Link By light path The optical path By optical link Composition, light path By optical link Composition, light path By optical link Composition, light path By optical link Based on this, the link , , As the link components of the network, and the optical path , , , , and optical links , , , , , , As the influencing components of the network, the dependencies between the components can be determined. Dependencies affecting components , , , , , and the impact component Depends on the impact component , , , .

[0064] Optionally, in order to reduce the scale of the ordered decision graph constructed subsequently, in the example of the network shown in FIG. 2 , an optical link component set may be constructed according to each optical link: , and use each optical link component set as the component element of the decision diagram. Among them, in each optical link component set, if the status of any element is unavailable, it can cause the status of the component element represented by the optical link component set to be unavailable. If the status of the component element represented by the optical link component set is available, it requires that all elements in the component set are available. For example, Components in or If any of them is unavailable, The status is unavailable, so the component or If any of the states is unavailable, the component and The status of the link component is unavailable, causing and The connectivity status is disconnected.

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

[0066] Step S102: construct a component sequence of the target network according to the dependency relationship.

[0067] According to the dependency relationship between each link component and each influencing component of the target network, each link component and each influencing component can be sorted to construct a component sequence of the target network. In the component sequence of the target network, the order of the dependent component precedes the order of the dependent component of the component. The dependent component corresponding to a component can be a link component or an influencing component that depends on the component.

[0068] For example, still taking the network shown in FIG. 2 as an example, the corresponding link components Dependencies affecting components , , , then in the component sequence corresponding to the network, the component The order is after the component , , Similarly, link components Dependencies affecting components , , , then in the component sequence The order is after the component , , Moreover, due to the impact of components Depends on , , Depends on , , so in the component sequence The order is after the component , order, and components The order is after the component , Based on this, the component sequence corresponding to the network shown in Figure 2 can be exemplarily expressed as .

[0069] Among them, by sorting the components according to the dependencies between the components to construct a component sequence, and then subsequently constructing an ordered decision graph according to the sequence, it is beneficial to reduce the scale of the decision graph.

[0070] Step S103: construct a connected ordered decision graph including each link component according to the component sequence, and construct an ordered decision subgraph corresponding to the link state of each link component.

[0071] Among them, since the component sequence contains the dependency relationship between the components of the target network, the connected ordered decision graph and each ordered decision subgraph can be efficiently constructed according to the component sequence. Among them, the ordered decision subgraph corresponding to each link component contains the link component and each influencing component that the link component depends on.

[0072] Among them, the connectivity ordered decision diagram can be an ordered binary decision diagram (OBDD) constructed according to the order of each link component in the component sequence to represent the overall connectivity of the target network. When the status of each link component is determined, the connectivity status of the target network can be determined according to the connectivity ordered decision diagram.

[0073] The ordered decision subgraph corresponding to the link component can be an ordered binary decision graph constructed according to the influence components that the link component depends on in the component sequence and the dependency relationship between these influence components to represent the connectivity of the link component. When the status of each influence component that the link component depends on is determined, the connectivity status of the corresponding link can be determined according to the ordered decision subgraph.

[0074] For example, still taking the network shown in FIG. 2 as an example, its corresponding connected ordered decision graph can be as shown in FIG. 3 As shown, and the link components in the network , , The corresponding ordered decision subgraphs can be shown in Figure 3. , , As shown. In the connected ordered decision graph and ordered decision subgraph shown in FIG3, non-terminal nodes (circular nodes) represent components, solid arcs represent the available state (or occurrence state) of components, dotted arcs represent the unavailable state (or non-occurrence state) of components, and terminal nodes (square nodes) correspond to the connectivity state of the network or link, where a terminal node of 1 corresponds to a connected network or link, and a terminal node of 0 corresponds to a disconnected network or link.

[0075] Step S104, according to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into a connected ordered decision graph to obtain a target connected ordered decision graph of the target network.

[0076] Among them, based on the connected ordered decision graph of the target network and the ordered decision subgraphs corresponding to each link component, the corresponding ordered decision subgraphs can be merged into the connected ordered decision graph in sequence according to the order of each link component in the component sequence, until all the ordered decision subgraphs are merged, and the target connected ordered decision graph of the target network can be obtained.

[0077] In the process of merging the ordered decision subgraphs into the connected ordered decision graph, the merging can be performed in combination with the dependencies between components presented in the component sequence, so that the merged connected ordered decision graph can still accurately reflect the dependencies between the components.

[0078] For example, still taking the network shown in FIG. 2 and the connected ordered decision graph and ordered decision subgraph shown in FIG. 3 as examples, the link components can be sequentially connected according to the order of each link component in the component sequence. , , The corresponding ordered decision subgraph , , Merge to connected ordered decision graph , thus we can get the target connected ordered decision graph as shown in Figure 4 .

[0079] Step S105, obtaining a connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connectivity ordered decision graph.

[0080] Among them, each path in the target connected ordered decision graph can correspond to a set of feasible component states that can make the target network connected. Based on this, the connectivity probability of the target network can be calculated according to the connectivity probability of each path:

[0081]

[0082] in, is the connectivity probability of the target network H, It means that the target network is connected given the component state X, X is the path in the target connected ordered decision graph (i.e., the set of feasible component states), and x is the state of each component on path X.

[0083] Based on this, the connectivity probability corresponding to the path can be obtained by calculating the product of the state probability values ​​corresponding to the states of each component on the path, and then the connectivity probability of each path can be summed to obtain the connectivity probability of the target network. Then, the connectivity reliability test result of the target network can be obtained based on the connectivity probability of the target network.

[0084] Among them, since the target connected ordered decision graph is an ordered binary decision graph, according to the characteristics of the ordered binary decision graph, the probability of each path can be calculated in parallel, and there is no need to calculate the probability after generating the complete target connected ordered decision graph. It is only necessary to calculate the probability based on the state superposition of the isomorphic subgraphs, and there is no need to save the state of the entire target connected ordered decision graph. This can alleviate the problem of difficulty in obtaining calculation results due to state space explosion and insufficient computing memory.

[0085] It can be understood that when the target network is a multi-layer network, the above formula can be expanded to obtain the connectivity probability expression of the link of the next layer of the target network, and then the same modeling method can be referred to for iterative modeling to obtain the expression corresponding to the multi-layer network. In addition, all components in the multi-layer network and their dependencies can be directly enumerated, and a target connectivity ordered decision graph containing these components and dependencies can be established to calculate the connectivity probability of the target network.

[0086] In the above network connectivity reliability detection method, in view of the fact that the connectivity of links in the actual network is dependent on various influencing factors of the network, each link and various influencing factors in the network are taken as components, and a component sequence is constructed according to the mutual dependency between them, so that according to the component sequence, a connectivity ordered decision graph containing each link component and an 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 sequence according to the order of the component sequence, it is possible to model the dependency between each link component, each influencing component and various components in the network, and obtain a connectivity ordered decision graph that can represent the connectivity of the entire target network. Furthermore, according to the connectivity ordered decision graph, the connectivity reliability of the target network can be detected in combination with the dependency of each component in the network without assuming that the link failure of the target network is independent, so that the connectivity reliability of complex networks such as multi-layer networks, virtual networks, and logical networks can be detected in a more practical scenario, and it can also be applied to scenarios such as common cause failure events, so that more accurate connectivity reliability detection results can be obtained. Moreover, since the connected ordered decision graph is obtained by merging the ordered decision subgraphs corresponding to each link component into the initial connected ordered decision graph, when solving the connectivity reliability test results, probability calculation can be performed based on the state superposition of the isomorphic subgraphs without having to save the state of the entire connected ordered decision graph, thereby effectively avoiding the explosion of the state space and the difficulty in obtaining connectivity reliability test results due to insufficient computing memory.

[0087] In an exemplary embodiment, as shown in FIG5 , constructing a component sequence of a target network according to the dependency relationship may include:

[0088] Step S501: obtain a first ranking of each link component, and construct a component sequence including the link component according to the first ranking.

[0089] The first sorting of link components in the target network may be a pre-set arrangement order for each link component. Exemplarily, the first sorting may be a sorting of link components that can minimize the size of the target connected ordered decision graph of the target network. In this step, an initial component sequence corresponding to the target network may be constructed based on the first sorting. For example, taking the network shown in FIG. 2 as an example, if the first sorting of link components is , , , you can build a component sequence .

[0090] Step S502: determining the impact dependency relationship between the impact components according to the dependency relationship, and performing a second sorting on the impact components according to the impact dependency relationship.

[0091] According to the dependency relationship of each component of the target network, the mutual influence dependency relationship between each influencing component can be determined, and then the influencing components are sorted in a second order according to the influence dependency relationship.

[0092] For example, taking the network shown in Figure 2 as an example, the influencing components may include , , , , , , , , and their mutual influence dependencies may include: Depends on , , Depends on , , Depends on , , Depends on Based on this, the second sorting of the impact components can be performed according to the impact dependency relationship, and the dependent impact components are arranged before their corresponding dependent components. Exemplarily, the second sorting of the impact components can be: , , , , , , , .

[0093] Step S503, based on the dependency relationship, determine in turn whether each impact component that each link component in the component sequence depends on exists in the component sequence; if not, each impact component that the link component depends on is added to the previous position of the link component in the component sequence in sequence according to the second sorting.

[0094] According to the dependency relationship, the influence components that each link component depends on can be determined respectively. For each link component in the component sequence, it can be judged in turn whether the influence component it depends on already exists in the component sequence. If not, each influence component that the link component depends on and does not exist in the component sequence can be inserted one by one into the previous position of the link component in the component sequence according to its order in the second sorting. When the influence component that the link component depends on already exists in the component sequence, it can be judged on the next link component.

[0095] For example, still taking the network shown in FIG2 as an example, assuming that the initial component sequence is , the second ordering of the components is affected by: , , , , , , , , we can first Make a judgment. Among them, in judging the link component Dependent impact components , , Does not exist after the component sequence, can be based on the affected component , , The order in the second sort is added to the component sequence. The previous position of , get the new component sequence Then, the link components can be Make a judgment and then judge the link component Dependent impact components , Does not exist after the component sequence, can be based on the affected component , The order in the second sort is added to the component sequence. The previous position of , get the new component sequence Similarly, the link components can be Perform the same operation to affect the components that it depends on and does not exist in the component sequence and Insert into link components in second order Before, we end up with the component sequence .

[0096] In this embodiment, by constructing a component sequence according to a first sorting of link components, and then performing a second sorting of the influencing components according to the impact dependency relationship between the influencing components, and inserting the influencing components that each link component depends on into the component sequence in turn according to the dependency relationship, the components can be sorted efficiently, and a component sequence that can reflect the dependency relationship between components can be quickly constructed, thereby improving the overall detection efficiency of network connectivity reliability.

[0097] In an exemplary embodiment, as shown in FIG6 , according to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into a connected ordered decision graph to obtain a target connected ordered decision graph of the target network, which may include:

[0098] Step S601: taking the ordered decision subgraph of the first link component in the component sequence as the subgraph to be merged.

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

[0100] Step S603, take the ordered decision subgraphs of each non-first link component in the component sequence as the subgraphs to be merged in turn, and merge the component nodes of the subgraphs to be merged into the connected ordered decision graph updated in the last time in turn according to the order of the connected ordered decision graph updated in the last time and the order of the component nodes of the subgraphs to be merged in the component sequence, so as to obtain the connected ordered decision graph updated this time.

[0101] Step S604, obtaining a target connected ordered decision graph according to the latest connected ordered decision graph.

[0102] Specifically, in the process of sequentially merging the ordered decision subgraphs into the connected ordered decision graph, the decision graphs can be merged sequentially 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 first used as the subgraph to be merged, and in step S602, the subgraph to be merged is merged with the connected ordered decision graph to obtain an updated connected ordered decision graph. Among them, in the process of merging the subgraph to be merged with the connected 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 two decision graphs in the component sequence, and then according to the dependency relationship, the component nodes and corresponding paths in the subgraph to be merged can be merged into the connected ordered decision graph.

[0103] Among them, based on the merging of the connected ordered decision graph and the ordered decision subgraph of the first link component, a connected ordered decision graph is obtained. In step S603, the ordered decision subgraph corresponding to each non-first link component can be used as a subgraph to be merged in turn according to the order of the link components in the component sequence, and then merged into the connected ordered decision graph updated last time in the same way as in S602, until the merging of the ordered decision subgraphs corresponding to all link components is completed, and the latest connected ordered decision graph is obtained. Then, in step S604, the latest connected ordered decision graph can be used as the target decision graph of the target network.

[0104] For example, assume that the connected ordered decision graph of the target network is , each link of the target network , corresponding to the ordered decision subgraph , then the merging process of the above decision graphs can be expressed as: ,in is the target connected ordered decision graph of the target network, m is the total number of link components of the target network, It is the merging rule of component nodes followed in the process of merging each subgraph to be merged with the connected ordered decision graph.

[0105] For example, taking the network shown in FIG. 2 and the connected ordered decision graph and ordered decision subgraph shown in FIG. 3 as examples, assuming that the component sequence is , then the decision graph merging process can be shown in Figure 7. Among them, for the connected ordered decision graph and ordered decision subgraphs , , , you can first Merge to Get the first updated connected ordered decision graph, and then Combined with the first updated connected ordered decision graph, the second updated connected ordered decision graph is obtained. Merge it into the connected ordered decision graph updated for the second time, and you can get the target connected ordered decision graph of the target network. .

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

[0107] In an exemplary embodiment, as shown in FIG8 , according to the order of each component node of the connected ordered decision graph and the subgraph to be merged in the component sequence, each component node of the subgraph to be merged is merged into the connected ordered decision graph in turn to obtain a new connected ordered decision graph, which may include:

[0108] Step S801, taking the root node of the connected ordered decision graph as the first node, and taking the root node of the subgraph to be merged as the second node.

[0109] Step S802: compare the order of the first node and the second node in the component sequence.

[0110] Step S803, if the order of the second node is earlier than the order of the first node, the second node is added to the connected ordered decision graph as the parent node of the first node, and the child node of the second node in the ordered decision subgraph is used as a new second node.

[0111] Step S804: if the order of the second node is later than the order of the first node, the child node of the first node is used as the new first node.

[0112] Step S805, if the order of the first node and the second node is the same, merge the second node into the first node, and use the child node of the first node as the new first node, and use the child node of the second node in the ordered decision subgraph as the new second node.

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

[0114] In the process of merging the subgraphs to be merged into the connected ordered decision graph, the positions of the component nodes in the subgraphs to be merged that should be inserted into the connected ordered decision graph can be determined by comparing the orders 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 used as the first node, and the root node of the subgraph to be merged can be used as the 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] Among them, if the order of the second node is earlier than the order of the first node, then go to execute step S803, add the second node as the parent node of the first node to the connected ordered decision graph, keep the first node unchanged, and use the child node of the second node in the ordered decision subgraph as the new second node, and then go to execute step S806.

[0117] If the order of the second node is later than the order of the first node, the process proceeds to step S804, keeps the second node unchanged, and uses the child node of the first node as the new first node, and then proceeds to step S806.

[0118] Among them, if the orders of the first node and the second node are the same, the first node and the second node correspond to the same component, and thus it is possible to proceed to execute step S805 to merge the first node and the second node, use the child nodes of the original first node as the new first node, and use the child nodes 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 operations in step S802 can be re-executed until all the component nodes in the subgraph to be merged are merged into the connected ordered decision diagram, thereby completing this update of the connected ordered decision diagram.

[0120] Exemplarily, please refer to FIG. 9. Assume that is a connected ordered decision diagram, is a 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 being merged into 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 the child nodes of respectively; is the root node of H, and its order in the component sequence is j. and are the child nodes of respectively.

[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 shown in FIG. 9(a), when 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. When i < j, as shown in FIG. 9(b), x can be used as the root node of the merged decision diagram, and the operation is applied between the node and the node y. Similarly, when i > j, as shown in FIG. 9(c), y can be used as the root node of the merged decision diagram, and the operation is applied between the node x and the node , Based on this, we can recursively compare the nodes in the two decision graphs and All nodes in are merged into .

[0124] In this embodiment, during the merging process of decision graphs, the specific merging operation on the nodes is determined by comparing the order of the nodes in the decision graphs to be merged in the component sequence. This can simplify the complex merging process into multiple judgments and processing of the nodes, thereby helping to improve the computational efficiency of the merging process.

[0125] In an exemplary embodiment, obtaining a first ranking of each link component may include: determining each link component of the target network based on the link component information; and obtaining a first ranking of each link component using a search algorithm with the goal of minimizing the size of a target connectivity ordered decision graph of the target network.

[0126] The first ranking of each link component in the target network may be a ranking of the link components that can minimize the size of the target connected ordered decision graph of the target network. Specifically, after determining each link component of the target network according to the link component information, a breadth-first search algorithm or a search algorithm such as a heuristic algorithm may be used to search for possible permutations of each link component, and the permutation sequence that can minimize the size of the target connected ordered decision graph is selected as the first ranking of each link component in the target network.

[0127] In this embodiment, by exploring the order of each link component through a search algorithm, a first order of link components that can minimize the scale of the target connectivity ordered decision graph of the target network can be quickly obtained, thereby effectively reducing the scale of the decision graph and improving computational efficiency based on the first order.

[0128] In an exemplary embodiment, component dependency information may include dependency constraints of a target network; according to a component sequence, ordered decision subgraphs corresponding to each link component are sequentially merged into a connected ordered decision graph to obtain a target connected ordered decision graph of the target network. This may also include: if the target connected ordered decision graph contains an invalid path that does not comply with the dependency constraints of the target network, the invalid path is deleted from the target connected ordered decision graph.

[0129] The component dependency information may include dependency constraints in the target network, wherein the dependency constraints may include constraints on dependency relationships between components, and constraints on components and paths in a decision graph.

[0130] Exemplarily, for the network shown in FIG. 2 , its dependency constraints may include: (1) if the optical link component set of the 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 only contains paths that connect the target network.

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

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

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

[0134] Among them, in view of the situation that multiple influencing components that may exist in the target network are simultaneously dependent on the same link component or influencing component, the dependent influencing component can be taken as the first influencing component, and the same link component or influencing component that depends on these first influencing components can be taken as the dependent component.

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

[0136] For example, the IP link e consists of an optical path Bearing, optical path Can consist of multiple groups of subpaths connected by multiple intermediate nodes Carrying, then in each group of sub-paths, any sub-path If the sub-paths are unavailable, the entire group of sub-paths will be unavailable, which will affect the optical path. Based on this, multiple sub-paths of the same group can be As a light path The first impact component is to divide each group of subpaths Grouped into same impact component sets . The same impact component set Any subpath component Unavailable, can lead to the corresponding set of components with the same impact Unavailable, and thus the optical path Based on this, the same impact component set can be used Replace multiple subpaths of the same group in a component sequence .

[0137] Exemplarily, after replacing each first influencing component with the same influencing component set, the connectivity probability calculation method of link e can be expressed as:

[0138]

[0139] in, Indicates that the component status of link e is connected, Indicates the optical path The component status ( Indicates the optical path status is Available), Represents a set of components that have the same impact The component status, Represents a subpath The component status, Indicates the optical path The set of components that have an impact A collection of .

[0140] In this embodiment, by constructing the same-influence link set, multiple first-influence components that produce the same influence can be replaced with one component corresponding to the same-influence link set, thereby effectively reducing the scale of the decision graph and improving calculation efficiency.

[0141] In an exemplary embodiment, as shown in FIG10 , before constructing the component sequence of the target network according to the dependency relationship, the following may also be included:

[0142] Step S1001: If there are multiple second influencing components in the component sequence that have a probabilistic dependency relationship with the same dependent component, then component power sets corresponding to the multiple second influencing components are constructed.

[0143] Among them, there is a probabilistic dependency relationship between the influencing component and the dependent component, which may mean that in a certain state of the influencing component, the state of the dependent component has a certain probability of being available, and a certain probability of being unavailable. Exemplarily, the influencing component may be geographical climate, natural disasters, emergencies, network attacks, electromagnetic interference, etc., and the dependent component may be a link. Then, when the state of the influencing component is occurring, the dependent component has a certain probability of being connected, and also has a certain probability of being disconnected. Among them, when there is a probabilistic dependency relationship between the same dependent component and multiple second influencing components in the component sequence, the component power set corresponding to these second influencing components can be constructed.

[0144] For example, taking the network shown in Figure 2 as an example, network attacks, natural disasters, computer room failures and other events can affect different components of the network, thereby forming common cause failures. Each common cause failure event has a certain probability of causing network disconnection. Among them, when multiple common cause failures can cause component x of the network to be unavailable with a certain probability, component x can be used as the dependent component of these events, and these common cause failure events are respectively used as the second influencing components that have a probability dependency relationship with the dependent component x. Among them, it can be defined , represents the set of second impact components that affect the dependent component x, and defines , the power set of the second influencing component set used to solve, that is , C is used to represent the second influencing component set.

[0145] Among them, it is assumed that there is a common cause failure event Impact link components , connectivity, and the presence of common cause failure events Impact link components The connectivity of , The components that affect the link The second influencing component, namely , and its corresponding component power set .in, Corresponds to , None of them happen. Corresponds to Occurrence Not happening, Corresponds to Occurrence Not happening, Corresponds to , Happen at the same time.

[0146] Step S1002: taking the influence probability of each element in the component power set as a third influence component.

[0147] For the component power set corresponding to multiple second impact components, the impact probability of each element therein can be used as a third impact component. The impact probability of each element can refer to the probability that the state of the dependent component is available when the element occurs. Each third impact component depends on the second impact component, and each third impact component is depended on by the dependent component.

[0148] For example, for the aforementioned component power set ,use Indicates the link component when element C occurs The probability that the state is available (ie connected), then the third impact component can be obtained: , , , Among them, each third impact component depends on the second impact component , , and are all dependent components Depends on.

[0149] Step S1003: updating the dependency relationship according to the component dependency relationship between the third impact component and the second impact component and the dependent component.

[0150] Among them, according to the component dependency relationship between the third influencing component and the second influencing component and the dependent component, the dependency relationship between the components 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 then an ordered decision graph containing the third influencing component and the corresponding dependency relationship can be constructed.

[0151] Exemplarily, the third impact component includes: , , , For example, you can build a link component The ordered decision subgraph of is shown in Figure 11. Among them, the link components The connectivity probability can be expressed as:

[0152]

[0153] in, Represents link components The component status is connected, The link component when each event occurs in the component power set of the second influencing component The probability of staying connected.

[0154] In this embodiment, for multiple second influencing components that have a probabilistic dependency relationship with the same dependent component in a component sequence, by constructing their corresponding component power sets, and using the influence probability of each element in the component power set on the dependent component as a third influencing component to participate in the connectivity reliability detection of the target network, more precise modeling of the impact on 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 the steps in the flowcharts involved in the above 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 is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may 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 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.

[0156] Based on the same inventive concept, the embodiment of the present application also provides a network connectivity reliability detection device for implementing the network connectivity reliability detection 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 the embodiments of one or more network connectivity reliability detection devices provided below can refer to the limitations of the network connectivity reliability detection method above, and will not be repeated here.

[0157] In an exemplary embodiment, as shown in FIG12 , a network connectivity reliability detection device 1200 is provided, comprising:

[0158] The dependency determination module 1201 is used to determine the dependency relationship between each link component and each impact component of the target network according to the link component information, impact component information and component dependency information of the target network;

[0159] A component sequence construction module 1202 is used to construct a component sequence of the target network according to the dependency relationship; wherein the order of each component in the component sequence precedes the order of the dependent components that depend on the component;

[0160] The first decision graph construction module 1203 is used to construct a connected ordered decision graph including each of the link components according to the component sequence, and to construct an ordered decision subgraph corresponding to the link state of each of the link components respectively; the ordered decision subgraph includes the link component and each of the influencing components that the link component depends on;

[0161] A second decision graph construction module 1204 is used to sequentially merge the ordered decision subgraphs corresponding to the link components into the connected ordered decision graph according to the component sequence, so as to obtain a target connected ordered decision graph of the target network;

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

[0163] In an exemplary embodiment, the component sequence construction module 1202 is also used to: obtain a first ranking of each of the link components, and construct a component sequence containing the link components according to the first ranking; determine the impact dependency relationship between each of the impact components according to the dependency relationship, and perform a second ranking on each of the impact components according to the impact dependency relationship; and determine in turn, according to the dependency relationship, whether each of the impact components that each of the link components in the component sequence depends on exists in the component sequence; if not, each of the impact components that the link component depends on is added in turn to the previous position of the link component in the component sequence according to the second ranking.

[0164] In an exemplary embodiment, the component sequence construction module 1202 is also used to: use the ordered decision subgraph of the first link component in the component sequence as the subgraph to be merged; merge the component nodes of the subgraph to be merged into the connected ordered decision graph in turn according to the order of the connected ordered decision graph and the component nodes of the subgraph to be merged in the component sequence to obtain an updated connected ordered decision graph; use the ordered decision subgraphs of each non-first link component in the component sequence as the subgraph to be merged in turn, and merge the component nodes of the subgraph to be merged into the connected ordered decision graph updated last time according to the connected ordered decision graph updated last time and the order of the component nodes of the subgraph to be merged in the component sequence to obtain the connected ordered decision graph updated this time; obtain the target connected ordered decision graph based on the latest connected ordered decision graph.

[0165] In an exemplary embodiment, the component sequence construction module 1202 is also used to: use the root node of the connected ordered decision graph as the first node, and use the root node of the subgraph to be merged as the second node; compare the order of the first node and the second node in the component sequence; if the order of the second node is earlier than the order of the first node, then add the second node as the parent node of the first node to the connected ordered decision graph, and use the child node of the second node in the ordered decision subgraph as a new second node; if the order of the second node is later than the order of the first node, then use the child node of the first node as a new first node; if the order of the first node and the second node is the same, then merge the second node into the first node, and use the child node of the first node as a new first node, and use the 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 subgraph to be merged are merged into the connected ordered decision graph.

[0166] In an exemplary embodiment, the component sequence construction module 1202 is also used to: determine each of the link components of the target network based on the link component information; and use a search algorithm to obtain the first ranking of each of the link components with the goal of minimizing the size of the target connected ordered decision graph of the target network.

[0167] In an exemplary embodiment, the component dependency information includes dependency constraints of the target network; the device also includes: a path deletion module, which is used to delete the invalid path from the target connected ordered decision graph if the target connected ordered decision graph contains an invalid path that does not comply with the dependency constraints of the target network.

[0168] In an exemplary embodiment, the device also includes: a first component adjustment module, which is used to establish a same-impact component set including each of the first-impact components if there are multiple first-impact components in the component sequence that are dependent on the same dependent component, and the component state of any of the first-impact components has the same impact on the dependent component, and replace each of the first-impact components in the component sequence with the same-impact component set.

[0169] In an exemplary embodiment, the device also includes: a component power set construction module, which is used to construct component power sets corresponding to multiple second influencing components if there are multiple second influencing components in the component sequence that have a probabilistic dependency relationship with the same dependent component; a second component adjustment module, which is used to use the influence probability of each element in the component power set as a third influencing component; wherein each of the third influencing components depends on the second influencing component, and each of the third influencing components is dependent on the dependent component; and a dependency updating module, which is used to update the dependency relationship according to the component dependency relationship between the third influencing component, the second influencing component and the dependent component.

[0170] Each module in the above network connectivity reliability detection device 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.

[0171] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG13. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as 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 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 is used to store data such as link component information, impact component information and component dependency information. 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 connectivity reliability detection method is implemented.

[0172] Those skilled in the art will understand that the structure shown in FIG. 13 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0173] 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 method embodiments when executing the computer program.

[0174] 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.

[0175] 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.

[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 used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0177] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments 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 this 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.

[0178] 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.

[0179] The above 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 method for detecting network connectivity reliability, characterized in that: The method comprises: Determining dependency relationships between link components and impact components of the target network based on link component information, impact component information, and 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 precedes the order of dependent components that depend on the component; According to the component sequence, a connected ordered decision graph including each of the link components is constructed, and an ordered decision subgraph corresponding to the link state of each of the link components is constructed respectively; the ordered decision subgraph includes the link component and each of the influencing components on which the link component depends; According to the component sequence, the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph to obtain a target connected ordered decision graph of the target network; A connectivity reliability detection result of the target network is obtained according to the connectivity probability of each path in the target connectivity ordered decision graph.

2. The method according to claim 1, characterized in that The constructing the component sequence of the target network according to the dependency relationship includes: Obtaining a first ranking of each of the link components, and constructing a component sequence including the link components according to the first ranking; Determining, according to the dependency relationships, an impact dependency relationship between the impact components, and performing a second sorting on the impact components according to the impact dependency relationship; According to the dependency relationship, it is determined in turn whether the impact components that each link component in the component sequence depends on exist in the component sequence; if not, the impact components that the link component depends on are added to the previous position of the link component in the component sequence in accordance with the second sorting.

3. The method according to claim 2, characterized in that The step of sequentially merging the ordered decision subgraphs corresponding to the link components into the connected ordered decision graph according to the component sequence to obtain a target connected ordered decision graph of the target network includes: Taking the ordered decision subgraph of the first link component in the component sequence as the subgraph to be merged; Merging the component nodes of the subgraph to be merged into the connected ordered decision graph in sequence according to the connected ordered decision graph and the order of the component nodes of the subgraph to be merged in the component sequence to obtain an updated connected ordered decision graph; Taking the ordered decision subgraphs of each non-first link component in the component sequence as the subgraphs to be merged in sequence, and merging the component nodes of the subgraphs to be merged into the connected ordered decision graph updated in the previous update according to the order of the connected ordered decision graph updated in the previous update and the order of the component nodes of the subgraphs to be merged in the component sequence, to obtain the connected ordered decision graph updated in the current update; The target connected ordered decision graph is obtained according to the latest connected ordered decision graph.

4. The method according to claim 3, characterized in that The step of sequentially merging the component nodes of the subgraph to be merged into the connected ordered decision graph according to the connected ordered decision graph and the order of the component nodes of the subgraph to be merged in the component sequence to obtain a new connected ordered decision graph includes: The root node of the connected ordered decision graph is used as the first node, and the root node of the subgraph to be merged is used as the second node; comparing the order of the first node and the second node in the component sequence; If the order of the second node is earlier than the order of the first node, the second node is added to the connected ordered decision graph as the parent node of the first node, and the child node of the second node in the ordered decision subgraph is used as a new second node; If the order of the second node is later than that of the first node, the child node of the first node is used as the new first node; If the order of the first node and the second node is the same, merging the second node into the first node, and using the child node of the first node as a new first node, and using the child node of the second node in the ordered decision subgraph as a new second node; 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 subgraph to be merged are merged into the connected ordered decision graph.

5. The method according to claim 2, characterized in that The obtaining of the first ranking of the link components includes: determining each of the link components of the target network according to the link component information; With the goal of minimizing the size of the target connected ordered decision graph of the target network, the first ranking of each of the link components is obtained using a search algorithm.

6. The method according to claim 1, characterized in that The component dependency information includes dependency constraints of the target network; After the ordered decision subgraphs corresponding to the link components are sequentially merged into the connected ordered decision graph according to the component sequence to obtain the target connected ordered decision graph of the target network, the method further includes: If the target connected ordered decision graph includes an invalid path that does not meet the dependency constraint of the target network, the invalid path is deleted from the target connected ordered decision graph.

7. The method according to claim 1, characterized in that After constructing the component sequence of the target network according to the dependency relationship, the method further includes: If there are multiple first-impact components in the component sequence that are dependent on the same dependent component, and the component state of any first-impact component has the same impact on the dependent component, then a same-impact component set including each first-impact component is established, and each first-impact component in the component sequence is replaced with the same-impact component set.

8. The method according to any one of claims 1 to 7, characterized in that Before constructing the component sequence of the target network according to the dependency relationship, the method further includes: If there are multiple second influencing components in the component sequence that have a probabilistic dependency relationship with the same dependent component, construct component power sets corresponding to the multiple second influencing components; Taking the influence probability of each element in the component power set as a third influencing component; wherein each of the third influencing components depends on the second influencing component, and each of the third influencing components is depended on by the dependent component; The dependency relationship is updated according to the component dependency relationship between the third impact component, the second impact component, and the dependent component.

9. A network connectivity reliability detection device, characterized in that: The device comprises: A dependency determination module, configured to determine the dependency relationships between the link components and the impact components of the target network based on the link component information, impact component information, and component dependency information of the target network; 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 precedes the order of dependent components that depend on the component; A first decision graph construction module is configured to construct, based on the component sequence, a connected ordered decision graph including each of the link components, and to construct an ordered decision subgraph corresponding to each link state of each of the link components; the ordered decision subgraph includes the link component and each of the impact components on which the link component depends; A second decision graph construction module is configured to sequentially merge the ordered decision subgraphs corresponding to the link components into the connected ordered decision graph according to the component sequence, so as to obtain a target connected ordered decision graph of the target network; The structure acquisition module is used to obtain the connectivity reliability detection result of the target network according to the connectivity probability of each path in the target connectivity ordered decision graph.

10. 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 7 are implemented.

11. 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.

12. 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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