WO2023246081A1 - 一种光网络故障分析方法和装置 - Google Patents

一种光网络故障分析方法和装置 Download PDF

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WO2023246081A1
WO2023246081A1 PCT/CN2023/070894 CN2023070894W WO2023246081A1 WO 2023246081 A1 WO2023246081 A1 WO 2023246081A1 CN 2023070894 W CN2023070894 W CN 2023070894W WO 2023246081 A1 WO2023246081 A1 WO 2023246081A1
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node
link
service
network
optical network
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PCT/CN2023/070894
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English (en)
French (fr)
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何峰
邱晨
李玉
刘锦秋
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烽火通信科技股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H04Q2011/0079—Operation or maintenance aspects

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  • the present invention relates to the field of communication technology, and in particular to an optical network fault analysis method and device.
  • network failures often occur, such as node power outages, link interruptions, etc. These faults can basically be converted into link failures. For example, after a node is powered off, all links connected to the node are also unavailable. Therefore, a node failure can be converted into a failure of all links connected to the node.
  • link failure the probability of multiple link failures at the same time is relatively small, and after a failure occurs, the probability of another link failure occurring immediately is also relatively small.
  • link failure analysis in the entire network is that only one link in the entire network is disconnected at a time, and it is calculated whether the network path resources can be successfully allocated after the fiber disconnection.
  • the conventional method of analyzing link faults in the entire network is to generate a faulty network every time a link is disconnected.
  • this faulty network the broken fiber link resources are unavailable.
  • the service resources passing through the link are cleared and the faulty network is recalculated. business resources.
  • the calculation process of the corresponding fault analysis when each link is disconnected is independent of each other. That is, as many links as there are in the network topology, the fault analysis needs to be calculated separately. There are two problems with this calculation method: First, because different faulty networks appear at different times, the newly added relay resources are not affected by each other.
  • Relays generated in a certain faulty network can be used by other faulty networks, but existing Due to its independent calculation process, the calculation method cannot uniformly take the resources of different faulty networks into consideration, which greatly increases the generated relay resources and causes a waste of pre-allocated relay resources in the entire network.
  • the technical problem to be solved by the present invention is that the existing optical network fault analysis method cannot uniformly take the resources of different faulty networks into consideration, resulting in the need to generate a large number of relay resources, resulting in a waste of pre-allocated relay resources in the entire network.
  • the present invention provides an optical network fault analysis method, including:
  • the objective function is specifically:
  • the objective function is a weighted difference between the total number of restored services and the total number of relays.
  • the weight corresponding to the total number of restored services is sequentially decreased within the preset range and the value of the corresponding objective function is calculated until the value of the objective function is positive.
  • the corresponding optical network fault recovery path is the optimal optical network fault recovery path.
  • the one or more constraint functions specifically include:
  • One or more of the source and sink node constraint functions, path connectivity constraint functions, loss constraint functions, link wavelength constraint functions, and relay number constraint functions on the node are provided.
  • the source and sink node constraint functions are specifically:
  • the path connectivity constraint function is specifically:
  • the number of links (i, m) entering the node m under the service k is the same as the number of links (m, j) starting from the node m under the service k.
  • the loss constraint function is specifically:
  • the total loss of each sub-path h is less than or equal to the corresponding preset loss threshold.
  • the link wavelength constraint function is specifically:
  • the sum of the number of services k1 occupying the channel in the forward direction and the number of services k2 occupying the channel in the reverse direction on the link (i, j) is less than or equal to The preset channel number threshold corresponding to link (i, j).
  • the relay quantity constraint function on the node is specifically:
  • the number of services k corresponding to the node n is less than or equal to the preset maximum number of relays corresponding to the node n.
  • the present invention also provides an optical network fault analysis device for implementing the optical network fault analysis method described in the first aspect.
  • the device includes:
  • the present invention also provides a non-volatile computer storage medium, the computer storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors for completing the first step.
  • This invention establishes a fault network when a single link is disconnected, and establishes a mathematical model based on all fault networks in the network topology, so that relay resources in different fault networks are included in the calculation process, so that in different fault networks
  • the resources can be reused, and the mathematical model is solved to obtain the optimal optical network fault recovery path, so that when the service is restored after the link is disconnected, the minimum number of added relays is ensured, thereby reducing network resource consumption.
  • Figure 1 is a flow chart of an optical network fault analysis method provided by an embodiment of the present invention.
  • Figure 2 is a flow chart of an optical network fault analysis method provided by an embodiment of the present invention.
  • Figure 3 is a schematic diagram of an optical network topology provided by an embodiment of the present invention.
  • Figure 4 is a schematic diagram of a service path under an optical network topology provided by an embodiment of the present invention.
  • Figure 5 is a flow chart of an optical network fault analysis method provided by an embodiment of the present invention.
  • Figure 7 is a schematic architectural diagram of an optical network fault analysis device provided by an embodiment of the present invention.
  • Embodiment 1 of the present invention provides an optical network fault analysis method, as shown in Figure 1, which specifically includes:
  • step 202 an objective function is generated based on the established faulty network and the services that need to be restored in the faulty network, and one or more constraint functions are generated for each faulty network.
  • the embodiment of the present invention establishes a fault network when a single link is disconnected, and establishes a mathematical model based on all fault networks in the network topology, so that relay resources in different fault networks are included in the calculation process, so that different faults can Resources in the network can be reused, and the mathematical model is solved to obtain the optimal optical network fault recovery path. This ensures that the minimum number of relays is added when business is restored after a link is disconnected, thereby reducing network resource consumption.
  • step 301 when the nodes are located in different faulty networks, the maximum number of relays added on the node is used as the number of node relays, and the number of node relays of each node in each faulty network is added to obtain the Total number of relays.
  • the objective function is a weighted difference between the total number of restored services and the total number of relays.
  • the weight of the total number of relays and the weight of the total number of restored services are analyzed by those skilled in the art based on the network scale.
  • the objective function is expressed in the form of a mathematical formula as:
  • the first decision variable is specifically:
  • variables k and e are used to limit or indicate t.
  • variable e refers to the faulty network.
  • variable k refers to the services that need to be restored in the faulty network
  • k ⁇ T e the set of services that need to be restored in the faulty network.
  • Decision variables Represents the recovery status of service k that needs to be restored under the faulty network e. If service k is successfully restored, then The value is 1, otherwise, The value is 0.
  • the second decision variable is specifically:
  • variables k, h, a, b, i and j are all used to limit or indicate x.
  • variables i and j refer to the source node and sink node of the link respectively, that is, specifying a link (i, j), where i ⁇ V, j ⁇ V, and V are all nodes in the network topology under the corresponding fault network e
  • the set of link (i, j) ⁇ E, E is the set of all unidirectional links in the fault network.
  • variable k refers to the service and is used to indicate whether the link (i, j) is used by the service k, k ⁇ T e , and T e is the set of services that need to be restored in the faulty network.
  • variable h refers to the sub-path and is used to indicate whether link (i, j) is the link under the h-th sub-path under service k. h is greater than or equal to 0 and less than or equal to H. H is the maximum allowed in a service. The number of subpaths.
  • variable a refers to the first link and is used to indicate whether link (i, j) is the first link under the h-th sub-path under service k.
  • the value of a is 0 or 1.
  • variable b refers to the last link and is used to indicate whether link (i, j) is the last link under the h-th sub-path under service k.
  • the value of b is 0 or 1.
  • the embodiment of the present invention also provides a corresponding network topology schematic diagram to illustrate the relationship between network elements such as services, source nodes, sink nodes, sub-paths, and relay nodes.
  • service k has been established from node 1 to node 6 and the path of service k is: the service passes through Source node 1, node 7, node 4, node 5 finally reach node 6.
  • the service passes from source node 1 to node 6 through node 2, node 3, node 4, and node 5 in sequence.
  • the service passes through node 7 and node 8 from source node 1 to sink node 6 in sequence.
  • the service passes from source node 1 to node 6 through node 2, node 9, node 10, and node 11 in sequence.
  • the path Take the path from source node 1 to sink node 6 through node 2, node 3, node 4, and node 5 as an example. Assume that there are three local groups in each node. Under this path, node 3 and node 4 is a relay node, then the path includes three sub-paths, namely sub-path 1 between source node 1 and node 3, sub-path 2 between node 3 and node 4, and sub-path 2 between node 4 and sink node 6. Subpath 3. That is, the source node, relay node and sink node are the dividing points of the sub-path under the service. When there is no relay node in the path where the service is located, the service only has one sub-path from the source node to the sink node.
  • link (3, 4) since node 3 and node 4 are adjacent and both are relay nodes, the formed subpath 2 only contains link (3, 4), so link (3 , 4) It is both the first link of sub-path 2 and the last link of sub-path 2, so and The values are all 1.
  • the decision variables The variables k, b, i and j in are all accumulated, and the value range of each variable during accumulation is its default range, and the value of variable a is specified as 1.
  • the value Refers to whether link (i, j) is the first link under the h-th sub-path of service k, when link (i, j) is the first link under the h-th sub-path of service k , The value is 1, otherwise, The value is 0.
  • node n is the source node of link (n, j)
  • the actual judgment is is whether node n is a relay node in service k, and since k is cumulatively traversed, and when node n is a relay node in service k, The value of is 1, so what is actually searched is the number of services with node n as the relay node in the faulty network e, that is, the number of relays added on node n.
  • Max e () represents the maximum value among the number of relays of node n corresponding to all faulty network e in the network topology, that is, the number of node relays is obtained; because in actual situations, multiple links in the network fail at the same time The probability is small, that is, a single link failure usually occurs, that is, there is usually only one failed network in the network at the same time.
  • multiple faulty networks all contain the same node n, for example, three faulty networks e 1 , e 2 and e 3 are generated for three links respectively, and e 1 , e 2 and e 3 all contain node n.
  • each service needs to be set up with a relay on node n.
  • 5 relays are set on node n.
  • the fault network e 2 there are 4 relays on node n.
  • the fault network e 3 there are 6 relays on node n. Since there is usually only one fault network in the network at the same time, node n The relays set on can be reused, that is, there are up to 6 relays on node n, which is the number of node relays. Then, by traversing and accumulating node n, the sum of the number of relays of all nodes in all faulty networks is obtained, that is, the total number of relays is obtained.
  • the value of t k is 1, otherwise, the value of t k is 0.
  • the number of restored services is obtained by adding the number of successfully restored services under all faulty networks e.
  • M and N are the weight of the total number of restored services and the weight of the total number of relays respectively.
  • M and N are fixed values obtained by those skilled in the art based on the analysis of network topology and user requirements.
  • This embodiment uses a weighted difference between the minimum number of relays and the total number of restored services to control the proportions of the two goals, thereby maintaining a relative balance of goals according to the topological network or user needs.
  • the value of M needs to be greater than the product of N and the maximum total number of relays.
  • This preferred implementation limits the ratio of M to N so that the weight of the total number of restored services is much greater than the weight of the total number of relays, thereby ensuring that the total number of restored services is the first target at most and the total number of relays is the minimum. for the second goal.
  • step 401 if the value of the objective function is negative, then the weight corresponding to the total number of restored services is sequentially decreased within the preset range and the value of the corresponding objective function is calculated until the objective function is obtained. The value is positive.
  • step 402 when the objective function is positive, the corresponding optical network fault recovery path is the optimal optical network fault recovery path.
  • This preferred implementation method adjusts the weight of the number of restored services to make the calculated objective function a positive value to facilitate calculation.
  • the one or more constraint functions specifically include:
  • One or more of the source and sink node constraint functions, path connectivity constraint functions, loss constraint functions, link wavelength constraint functions, and relay number constraint functions on the node are provided.
  • the source and sink node constraint function is used to constrain that the transmission direction of the service is unique between the source node and the sink node of the successfully restored service.
  • the path connectivity constraint function is used to constrain that each link on the path of successfully restored service transmission can be used by the service, so that the service can reach the sink node from the source node along the corresponding path.
  • the loss constraint function is used to constrain the loss of each sub-path not to exceed the corresponding loss allowable range.
  • the link wavelength constraint function is used to constrain the number of channels occupied on the link to not exceed the number of channels allowed by the link.
  • the relay quantity constraint function on the node is used to constrain the number of relays added on a node to not exceed the corresponding range of the number of relays allowed to be added.
  • the type, quantity, and form of the constraint functions used above are not completely fixed. Instead, one or more of them can be selected for corresponding constraints according to the network fault recovery requirements of the topological network.
  • the source and sink node constraint functions and path connectivity constraint functions can constrain the path uniqueness and integrity of the determined service, and the loss constraint function, link wavelength constraint function and relay number constraint function on the node can constrain the link.
  • the number of channels used and the loss when reaching the relay node ensure the normal arrival of services.
  • the objective function is then used to obtain the optimal optical network fault recovery path.
  • the source and sink node constraint functions are specifically:
  • s is the source node of business k
  • d is the sink node of business k.
  • link (s, j) must be the first link under the first sub-path of service k. Therefore, the meaning represented by formula 1 is: for any one of the faulty networks e that needs to be restored All services k should meet the conditions: when service k is successfully restored, there is only one link used by service k from the source node s of service k, that is, the direction of service k when starting from the source node s is unique; When k is not restored, service k does not use any link after starting from source node s.
  • the decision variables The value of b is limited to 1, and the value of j is limited to d.
  • link (i, d) is the last link under the h-th sub-path of service k.
  • link (i, d) is the last link under the h-th sub-path of service k, The value is 1, otherwise, The value is 0.
  • the source and sink node constraint functions jointly constrain the services that need to be restored in the faulty network e through Formula 1 and Formula 2, so that the direction of service departure and the direction of arrival are unique, thereby ensuring that the transmission direction of the service is unique.
  • the path connectivity constraint function is specifically:
  • the link (i, n) entering the node n under the service k and the link starting from the node n under the service k (n, The quantity of j) is the same.
  • the number of links (i, m) entering the node m under the service k is the same as the number of links (m, j) starting from the node m under the service k.
  • the path connectivity constraint function is expressed in the form of mathematical formulas as Formula 3 and Formula 4, where Formula 3 is:
  • the decision variable The value of b is limited to 0.
  • link (i, n) is the last link under the h-th sub-path of service k
  • the value is 0, otherwise, when link (i, n) is the link under the h-th sub-path of service k, and link (i, n) is not the last link under the h-th sub-path of service k hour, The value is 1.
  • link (i, n) and link (n, j) are links with node n as the sink node and node n as the source node respectively.
  • the meaning represented by formula 3 is: for any service k that needs to be restored in the faulty network e, among all the links in and out of the node n under the service k, the link (i, n) entering the node n is the same as the slave node The number of links (n, j) originating from n is the same.
  • the decision variable The value of b is limited to 1. In this case, Refers to whether link (i, n) is the last link under the h-th sub-path of service k. When link (i, n) is the last link under the h-th path of service k, The value is 1, otherwise, The value is 0.
  • the decision variable The value of b is limited to 1.
  • link (n, j) is the last link under the h+1th path of service k On the way, The value is 1, otherwise, The value is 0.
  • node n is the relay node of service k
  • the left side of the equal sign in formula 4 represents the number of links with node n as the sink node used by service k
  • the right side of the equal sign represents the number of links with node n used by service k.
  • the number of links is the same as the number of links originating from relay node n in service k.
  • the total loss of each sub-path h is less than or equal to the corresponding preset loss threshold.
  • the loss constraint function is expressed in the form of a mathematical formula as:
  • lf ij represents the loss of link (i, j)
  • Th represents the preset loss threshold
  • the preset loss threshold sum is set by those skilled in the art according to the OSNR requirements of the user or network topology.
  • the same preset loss threshold can be set for all links, or a corresponding preset loss threshold can be preset for each link.
  • h represents any sub-path under any service that needs to be restored in the failed network e, and should satisfy the corresponding formula.
  • the meaning represented by the above formula is: for any sub-path h under any service that needs to be restored in the faulty network e, add the loss of each link under the sub-path h to get the total loss of the sub-path, and get The loss of the subpath should be less than or equal to the corresponding preset loss threshold.
  • the link wavelength constraint function is specifically:
  • the sum of the number of services k1 occupying the channel in the forward direction and the number of services k2 occupying the channel in the reverse direction on the link (i, j) is less than or equal to The preset channel number threshold corresponding to link (i, j).
  • the link wavelength constraint function is expressed in the form of a mathematical formula as:
  • W ij represents the maximum number of channels that can be supported on the link, that is, the preset channel number threshold, where W ij represents the set of channels that can be supported on the link, and
  • the preset channel number threshold is set by those skilled in the art based on analysis of topological network requirements. w ⁇ W means that any link in the faulty network e should satisfy the corresponding formula.
  • the decision variables Represents whether service k is forwardly using link (i, j). Since the service must occupy a channel when using the link, the decision variable It can represent whether service k is forwardly occupying the channel on link (i, j). When service k is forwardly occupying the channel on link (i,j), The value is 1, otherwise, The value is 0.
  • Decision variables Represents whether service k uses link (i, j) in reverse. Since the service must occupy a channel when using the link, the decision variable It can represent whether service k occupies the channel in the reverse direction on link (i, j). When service k occupies the channel in the reverse direction on link (i, j), The value is 1, otherwise, The value is 0.
  • the meaning represented by the left side of the above formula is: the number of all services occupying the channel in the forward direction on link (i, j) plus the number of services occupying the channel in the reverse direction on link (i, j).
  • Quantity the overall meaning of the above formula is: the number of forward-occupied channels and reverse-occupied channels on link (i, j) does not exceed the preset channel quantity threshold.
  • the relay number constraint function on the node is specifically:
  • the number of services k corresponding to the node n is less than or equal to the preset maximum number of relays corresponding to the node n.
  • the relay quantity constraint function on the node is expressed in the form of a mathematical formula as:
  • R n represents the maximum number of relays that can be set on node n, that is, the preset maximum number of relays, where R n is the set of relays that can be set on node n,
  • the decision variable where a is limited to 1, and the accumulation range of h starts from 2 then Used to refer to whether link (n, j) is the first link under any subpath except the first subpath of service k.
  • link (n, j) is the first link of service k except the first
  • the value is 1, otherwise, The value is 0.
  • the meaning represented by the left side of the above formula is similar to the total number of relays in the objective function.
  • the meaning represented by the above formula is: the number of relays set on node n.
  • the overall meaning of the above formula is: for each faulty network e, the number of relays set on node n is less than or equal to the preset maximum number of relays
  • Convert the objective function into a linear objective function and the corresponding constraint function specifically: introduce a new decision variable x n to represent the number of relays for each node n, and replace the corresponding variable in the objective function with this decision variable Position, introduces a second constraint function on the number of relays on the node, which is used to constrain the number of relays on node n to be greater than or equal to the number of relays added when node n is located in any faulty network.
  • the obtained new objective function is expressed in the form of a mathematical formula as:
  • the decision variable x n represents the number of relays of node n.
  • a specific implementation method for solving the mathematical model includes:
  • the objective function is substituted to calculate the value of the objective function corresponding to the various optical network path planning situations after the restriction. If If the value of the objective function is positive, then the optical network fault recovery path corresponding to the objective function is selected as the optimal optical network fault recovery path.
  • the value of the objective function is negative, adjust the weight corresponding to the total number of restored businesses in a decreasing manner, and then calculate the value of the objective function until the objective function is positive, and finally obtain the objective function with a positive value.
  • the corresponding optical network fault recovery path is used as the optimal optical network fault recovery path.
  • Another specific implementation method is to solve it by integer programming, and the algorithms that can be used include branch and bound method, cutting plane method, etc.
  • integer programming solvers are often used for solution, such as Cplex, Gurobi, etc.
  • the total number of relays can be minimized and the total number of restored services can be maximized on the premise that the constraint function is satisfied.
  • the present invention is based on the method described in Embodiment 1, combined with specific application scenarios, and uses technical expressions in relevant scenarios to illustrate the implementation process of the present invention in characteristic scenarios.
  • Figure 6 shows an optical transmission network.
  • this optical transmission network there are 7 nodes A, B, C, D, E, F and G.
  • links respectively (A, B), (A, C), (B, D), (B, E), (C, D), (C, F), (D, E), (E , G) and (F, G)
  • the 1-80 channels corresponding to the 1-80 wavelengths on each link can be used, that is, the corresponding preset channel number thresholds on each link are 80.
  • each planned service and the corresponding paths and channels used are as follows:
  • the value of the weight N corresponding to the total number of relays to 1.
  • the value of the weight M corresponding to the total number of restored services must be greater than the product of N and the maximum total number of relays.
  • the network The number of nodes in the node is calculated, and the number of possible relays on the node is calculated to set the value of the weight M.
  • the value of M is set to 11520.
  • Set the maximum number H of sub-paths allowed in a service to 3.
  • a corresponding fault network is established. For example, when link (A, B) is interrupted, the business passing through link (A, B) is s 1 - s 20 is interrupted, that is, the set of business T AB that needs to be repaired in the corresponding faulty network is s 1 -s 20 ; similarly, when the link (B, E) is disconnected, the corresponding set of T BE is s 1 - s 20 and s 31 - s 40 ; the set of faulty networks consisting of 9 disconnected links is ⁇ e AB , e AC , e BD , e BE , e CD , e CF , e DE , e EG , eFG ⁇ .
  • the optimal optical network fault recovery path is obtained.
  • the optical network fault recovery path corresponding to the minimum value of the objective function is selected as the optimal optical network fault recovery path.
  • the adjustable range and adjustment gradient of the weight M are formulated according to the scale of the topological network.
  • the adjustable range of the weight M is set to [6000, 11520], and the adjustment gradient is is 500, decrease the weight M by 500 from 11520, and recalculate the value of the objective function every time it decreases. Stop decreasing until the value of the objective function is positive. The optical network fault corresponding to the positive value of the objective function is restored. path as the optimal optical network failure recovery path.
  • FIG. 7 it is a schematic architectural diagram of an optical network fault analysis device according to an embodiment of the present invention.
  • the optical network fault analysis device of this embodiment includes one or more processors 21 and a memory 22 .
  • a processor 21 is taken as an example in FIG. 7 .
  • the processor 21 and the memory 22 may be connected through a bus or other means.
  • the connection through a bus is taken as an example.
  • the memory 22 can be used to store non-volatile software programs and non-volatile computer executable programs, such as the optical network fault analysis method in Embodiment 1.
  • the processor 21 executes the optical network fault analysis method by running non-volatile software programs and instructions stored in the memory 22 .
  • Memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the memory 22 optionally includes memory located remotely relative to the processor 21, and these remote memories may be connected to the processor 21 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • the program instructions/modules are stored in the memory 22, and when executed by the one or more processors 21, the optical network fault analysis method in the above-mentioned Embodiment 1 and Embodiment 2 is executed, for example, the above description is executed.
  • the program can be stored in a computer-readable storage medium.
  • the storage medium can include: Read memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

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Abstract

本发明涉及通信技术领域，提供了一种光网络故障分析方法和装置。其中所述方法包括：以网络拓扑中每一根链路分别断开为基础，建立多个故障网络；根据建立的故障网络和故障网络中需要恢复的业务，生成目标函数，并针对每一个故障网络，生成一个或多个约束函数；结合目标函数和约束函数，建立以总中继数量最少、恢复的业务总数量最多为目标的数学模型；求解数学模型，得到最优的光网络故障恢复路径。本发明通过将不同故障网络中的中继资源均被纳入计算过程中，使在不同故障网络中的资源能够被复用，从而在链路断开后进行业务恢复时，保证添加的中继数最少，进而减少网络资源消耗。

Description

一种光网络故障分析方法和装置 技术领域
本发明涉及通信技术领域，特别是涉及一种光网络故障分析方法和装置。
背景技术
在光网络的运行过程中，经常会出现网络故障情况，比如节点掉电，链路中断等情况。这些故障基本上都可以转换为链路故障，比如节点掉电后，所有与该节点相连通的链路也不可用，因此节点故障可以转换为与节点相连的所有链路的故障。在链路故障中，多个链路同时发生故障的概率比较小，而且一次故障发生后，立即再次发生别的链路故障的概率也比较小。一般会有足够的时间抢修单链路故障，这样全网只需要分析单链路故障后业务的路径资源恢复问题。全网链路次故障分析问题是全网每次只断一根链路，计算该次断纤后该网络路径资源是否能够分配成功。
全网链路故障分析的常规做法是每断开一根链路，生成一个故障网络，该故障网络下断纤链路资源不可用，清理经过该链路的业务资源并重新计算该故障网络的业务资源。每一根链路断开时所对应的故障分析的计算过程是相互独立的，即网络拓扑中存在多少条链路，就需要单独计算多少次故障分析。该计算方法有两个的问题：一是由于不同的故障网络不同时出现，故新增的中继资源相互不受影响，在某故障网络生成的中继可以被其它故障网络使用，但现有的计算方法由于其计算过程独立，使得无法将不同故障网络的资源统一纳入考量，从而使生成的中继资源大大增加，造成全网预分配中继资源的浪费。二是当在将前面故障网络生成的中继作为共享资源供后续故障网络使用时，需要考虑到网络断纤的顺序性，对于较复杂的拓扑网络，其中包含的链路数量庞大，继纤顺序的排列组合就是天文数字，导致故障分析的计算量巨大，单凭现有的 计算体系无法得到最优的光网络故障恢复路径。
鉴于此，克服该现有技术所存在的缺陷是本技术领域亟待解决的问题。
发明内容
本发明要解决的技术问题是现有的光网络故障分析方法无法将不同故障网络的资源统一纳入考量，从而导致需生成大量的中继资源，造成全网预分配中继资源的浪费。
本发明采用如下技术方案：
第一方面，本发明提供了一种光网络故障分析方法，包括：
以网络拓扑中每一根链路分别断开为基础，建立多个故障网络；
根据建立的故障网络和故障网络中需要恢复的业务，生成目标函数，并针对每一个故障网络，生成一个或多个约束函数；
结合目标函数和约束函数，建立以总中继数量最少、恢复的业务总数量最多为目标的数学模型；
求解数学模型，得到最优的光网络故障恢复路径。
优选的，所述目标函数具体为：
以节点位于不同故障网络中时，节点上所添加的最大的中继数量作为节点中继数量，将每个故障网络中的每个节点的节点中继数量相加得到所述总中继数量；
将每个故障网络中成功恢复的业务数量相加得到所述恢复的业务总数量；
所述目标函数为所述恢复的业务总数量与所述总中继数量的加权求差。
优选的，所述求解数学模型，得到最优的光网络故障恢复路径，具体包括：
若求解得到目标函数的值为负值，则将所述恢复的业务总数量所对应的权重在预设范围内依次递减并计算对应的目标函数的值，直至求解得到目标函数的值为正值，在目标函数为正值时所对应的光网络故障恢复路径即为最优的光网络故障恢复路径。
优选的，所述一个或者多个约束函数，具体包括：
源宿节点约束函数、路径连通性约束函数、损耗约束函数、链路波长约束函数和节点上的中继数量约束函数中的一种或多种。
优选的，所述源宿节点约束函数具体为：
针对故障网络e中的每一条需要恢复的业务k，在被业务k所使用的所有链路中，找到从业务k的源节点s出发的链路(s，j)和到达业务k的宿节点d的链路(i，d)；
当业务k被成功恢复时，所述业务k所对应的链路(s，j)有且仅有一条，所对应的链路(i，d)有且仅有一条；
当业务k未被恢复时，所述业务k不存在对应的链路(s，j)和链路(i，d)。
优选的，所述路径连通性约束函数具体为：
针对故障网络e中每一条需要恢复的业务k下的每一个非中继节点n，业务k下进入节点n的链路(i，n)与业务k下从节点n出发的链路(n，j)的数量相同；
针对每一条业务k下的每一个中继节点m，业务k下进入节点m的链路(i，m)与从业务k下节点m出发的链路(m，j)的数量相同。
优选的，所述损耗约束函数具体为：
针对故障网络e中每一条需要恢复的业务k下的每一条子路径h，将子路径h下每一条链路(i，j)的损耗相加得到子路径h的总损耗；
每一条子路径h的总损耗小于等于相应的预设损耗阈值。
优选的，所述链路波长约束函数具体为：
针对故障网络e下的每一条链路(i，j)，在链路(i，j)上正向占用波道的业务k1的数量和反向占用波道的业务k2的数量的和小于等于链路(i，j)所对应的预设波道数量阈值。
优选的，所述节点上的中继数量约束函数具体为：
针对故障网络e中的每一个节点n，找到故障网络e中以节点n为中继节点的业务k，节点n所对应的业务k的数量小于等于节点n所对应的预设最大中继 数量。
第二方面，本发明还提供了一种光网络故障分析装置，用于实现第一方面所述的光网络故障分析方法，所述装置包括：
至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述处理器执行，用于执行第一方面所述的光网络故障分析方法。
第三方面，本发明还提供了一种非易失性计算机存储介质，所述计算机存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，用于完成第一方面所述的光网络故障分析方法。
本发明通过对单根链路断开的情况，建立故障网络，根据网络拓扑中所有故障网络建立数学模型，使不同故障网络中的中继资源均被纳入计算过程中，使在不同故障网络中的资源能够被复用，对数学模型求解从而得到最优的光网络故障恢复路径，从而在链路断开后进行业务恢复时，保证添加的中继数最少，进而减少网络资源消耗。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对本发明实施例中所需要使用的附图作简单地介绍。显而易见地，下面所描述的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的一种光网络故障分析方法的流程图；
图2是本发明实施例提供的一种光网络故障分析方法的流程图；
图3是本发明实施例提供的一种光网络拓扑示意图；
图4是本发明实施例提供的一种光网络拓扑下的一条业务路径的示意图；
图5是本发明实施例提供的一种光网络故障分析方法的流程图；
图6是本发明实施例提供的一种光网络拓扑示意图；
图7是本发明实施例提供的一种光网络故障分析装置的架构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
在本发明的描述中，术语“内”、“外”、“纵向”、“横向”、“上”、“下”、“顶”、“底”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明而不是要求本发明必须以特定的方位构造和操作，因此不应当理解为对本发明的限制。
此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
实施例1:
本发明实施例1提供了一种光网络故障分析方法，如图1所示，具体包括：
在步骤201中，以网络拓扑中每一根链路分别断开为基础，建立多个故障网络。
在步骤202中，根据建立的故障网络和故障网络中需要恢复的业务，生成目标函数，并针对每一个故障网络，生成一个或多个约束函数。
在步骤203中，结合目标函数和约束函数，建立以总中继数量最少、恢复的业务总数量最多为目标的数学模型。
在步骤204中，求解数学模型，得到最优的光网络故障恢复路径。
其中，所述以网络拓扑中每一根链路分别断开为基础，建立多个故障网络具体包括：以网络拓扑中的一根链路单独断开为假设，找到在链路断开时需要恢复的业务，以链路断开后的拓扑网络和在链路断开后需要恢复的业务建立一个故障网络，每一根链路对应一个故障网络。
本发明实施例通过对单根链路断开的情况，建立故障网络，根据网络拓扑中所有故障网络建立数学模型，使不同故障网络中的中继资源均被纳入计算过程中，使在不同故障网络中的资源能够被复用，对数学模型求解从而得到最优 的光网络故障恢复路径，从而在链路断开后进行业务恢复时，保证添加的中继数最少，进而减少网络资源消耗。
在上述实施例中，由于要求总中继数量最少，恢复的业务总数量最多，通常所能够联想到的实现方式为：将恢复的业务总数量与总中继数量的差值作为目标函数，当目标函数的值最大或最小时，即为最优的光网络方案。但在实际情况中，由于在业务恢复过程中，添加的中继数量最少与业务恢复数量最多存在一定的互斥关系，为了维持两个目标之间的均衡，存在以下优选的实施例：
所述目标函数如图2所示，具体为：
在步骤301中，以节点位于不同故障网络中时，节点上所添加的最大的中继数量作为节点中继数量，将每个故障网络中的每个节点的节点中继数量相加得到所述总中继数量。
在步骤302中，将每个故障网络中成功恢复的业务数量相加得到所述恢复的业务总数量。
在步骤303中，所述目标函数为所述恢复的业务总数量与所述总中继数量的加权求差。
其中，所述总中继数量的权重与恢复的业务总数量的权重是由本领域技术人员根据网络规模分析得出的。
所述目标函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000001
需要说明的是，在本发明实施例的描述中，凡未经特殊说明，所有公式中参数右上角和右下角的标识不应理解为参数的幂，而应理解为对参数的限定描述，且标识所处的位置不同代表进行的限定不同，(凡是在相同的位置的标识，所限定的意义相同)。
为更好的描述上述目标函数，在此提供两个决策变量的基本形式，并对两个决策变量进行说明，其中，第一个决策变量具体为：
Figure PCTCN2023070894-appb-000002
其中，变量k和e均用于对t的限定或指示。
变量e代指故障网络。
变量k代指所在故障网络中需要恢复的业务，k∈T e，T e为故障网络中需要恢复的业务的集合。
决策变量
Figure PCTCN2023070894-appb-000003
代表故障网络e下的需要恢复的业务k的恢复状态，若业务k被成功恢复，则
Figure PCTCN2023070894-appb-000004
的值为1，否则，
Figure PCTCN2023070894-appb-000005
的值为0。
第二个决策变量具体为：
Figure PCTCN2023070894-appb-000006
其中，变量k、h、a、b、i和j均用于对x的限定或指示。
变量i、j分别代指链路的源节点和宿节点,即指定一条链路(i，j)，其中，i∈V，j∈V，V为相应故障网络e下的网络拓扑中所有节点的集合，链路(i，j)∈E，E为故障网络中所有单向的链路的集合。
变量k代指业务，用于指示链路(i,j)是否被业务k所使用，k∈T e，T e为故障网络中需要恢复的业务的集合。
变量h代指子路径，用于指示链路(i,j)是否是业务k下的第h个子路径下的链路，h大于等于0且小于等于H，H为一条业务中所允许的最多的子路径的数量。
变量a代指第一条链路，用于指示链路(i,j)是否为业务k下的第h个子路径下的第一条链路，a的值为0或1。
变量b代指最后一条链路，用于指示链路(i,j)是否为业务k下的第h个子路径下的最后一条链路，b的值为0或1。
在目标函数或约束函数的实际使用过程中，决策变量中所限定的值不同，决策变量所指示的含义不同，当所指示含义成立时，
Figure PCTCN2023070894-appb-000007
的值为1，否则，
Figure PCTCN2023070894-appb-000008
的值为0，在各函数中，将进一步进行详细的解释说明。
本发明实施例还提供了相应的网络拓扑示意图，用于说明业务、源节点、宿节点、子路径、中继节点等网络元素之间的关系。
以如图3所示的包含11个节点的网络拓扑为例，假设在网络拓扑中不存在任何中断时，已建立了从节点1到节点6的业务k且业务k的路径为：业务依次经过源节点1、节点7、节点4、节点5在最终到达节点6。
当业务k的路径上的任意一条链路断开时，业务k同样中断，为了使业务k能够恢复，则需重新对业务k规划路径，本发明实施例所提供的方法能够在网络拓扑中尚未发生链路中断时，以任意链路的单链路故障为假设，预先分析并得出实际出现链路故障时故障业务的恢复路径，从而在实际发生链路故障时，保障业务的迅速恢复。
如当业务k中的链路(7，4)断开时，业务k存在以下3种可能的故障恢复路径：
业务从源节点1依次经过节点2、节点3、节点4、节点5到达宿节点6。
业务从源节点1依次经过节点7和节点8到达宿节点6。
业务从源节点1依次经过节点2、节点9、节点10、节点11到达宿节点6。
以业务从源节点1依次经过节点2、节点3、节点4、节点5到达宿节点6该路径为例，设其中每个节点中存在3个本地组，在该路径下，节点3与节点4为中继节点，则该路径下包括3条子路径，分别是源节点1到节点3之间的子路径1、节点3到节点4之间的子路径2和节点4到宿节点6之间的子路径3。即以源节点、中继节点和宿节点为业务下的子路径的分界点，当业务所在路径中不存在中继节点时，则业务仅存在从源节点到宿节点这一条子路径。
以图3和图4为例对决策变量
Figure PCTCN2023070894-appb-000009
进行说明，如限定h为1、a为1，得到的决策变量
Figure PCTCN2023070894-appb-000010
则代表链路(i，j)是否为业务k下的第1条子路径的第一条链路，当链路(i，j)是业务k下的第1条子路径的第一条链路时，
Figure PCTCN2023070894-appb-000011
的值为1，如图4中的链路(1，2)满足该条件，即
Figure PCTCN2023070894-appb-000012
值为1；否则，如图4中的链路(2，3)虽属于业务k下的第1条子路径，但不是第一条链路，故
Figure PCTCN2023070894-appb-000013
的值为0。而对于链路(3，4)，由于节点3和节点4相邻且均为中继节点，故形成的子路径2仅包含链路(3，4)这一条链路，故链路(3，4)既是子路径2 的第一条链路，也是子路径2的最后一条链路，故
Figure PCTCN2023070894-appb-000014
与
Figure PCTCN2023070894-appb-000015
的值均为1。
在目标函数中，总中继数量为：
Figure PCTCN2023070894-appb-000016
其中，决策变量
Figure PCTCN2023070894-appb-000017
中的变量k、b、i和j均进行了累加，且在累加时每一个变量的取值范围均是其默认范围，而将变量a的值指定为1，在此情况下，
Figure PCTCN2023070894-appb-000018
代指链路(i，j)是否是业务k的第h个子路径下的第一条链路，当链路(i，j)是业务k的第h个子路径下的第一条链路时，
Figure PCTCN2023070894-appb-000019
的值为1，否则，
Figure PCTCN2023070894-appb-000020
的值为0。
由于变量h从2开始累加，故当
Figure PCTCN2023070894-appb-000021
的值为1时，链路(n，j)必然是业务k下的第2条或第2条以后的子路径下的第一条链路，故总中继数量的括号内的含义为：判断链路(n，j)是否是业务k中除第一条子路径以外的子路径上的第一条链路，由于节点n为链路(n，j)的源节点，故实际判断的是节点n是否是业务k中的中继节点，又由于对k进行了累加遍历，且当节点n是业务k中的中继节点时，
Figure PCTCN2023070894-appb-000022
的值为1，故实际查找的是故障网络e中，在以节点n为中继节点的业务的数量，即节点n上所添加的中继的数量。
Max e()表示在网络拓扑中所有故障网络e所对应的节点n的中继的数量中取最大值，即得到节点中继数量；由于在实际情况中，网络中多条链路同时发生故障的概率较小，即通常发生的是单链路故障，即网络中同一时间通常只存在一个故障网络。当多个故障网络均包含同一个节点n时，如针对3条链路分别生成了3个故障网络e 1、e 2和e 3，而在e 1、e 2和e 3中均包含节点n，其中，在e 1中进行业务恢复时，如需恢复5条业务，每条业务均需在节点n上设置中继，则在故障网络e 1中，节点n上设置有5个中继，而在故障网络e 2中，节点n上设置有4个中继，在故障网络e 3中，节点n上设置有6个中继，由于网络中同一时间通常只存在一个故障网络，故节点n上设置的中继能够被复用，即节点n上最多存在6个中继，此即为节点中继数量。再通过对节点n进行遍历累 加，从而得到所有故障网络中的所有节点的中继数量的和，即得到总中继数量。
在目标函数中，所述恢复的业务数量为：
Figure PCTCN2023070894-appb-000023
当业务k算通时，t k的值为1，否则，t k的值为0。通过将所有故障网络e下成功恢复的业务的数量相加得到所述恢复的业务数量。
其中，M与N分别是恢复的业务总数量的权重与总中继数量的权重，M和N均为由本领域技术人员根据网络拓扑和用户需求分析得出的定值。
本实施例通过对中继数量最少、恢复的业务总数量进行加权求差，使两个目标所占的比重得到控制，从而根据拓扑网络或用户的需求维持目标的相对均衡。
在实际情况中，网络故障分析通常是以恢复的业务总数量最多为第一目标，总中继数量最少为第二目标，对此，结合上述优选的实施例，还存在以下优选的实现方式，即对权重M与N的取值进行限定，具体包括：
M的取值需大于N与最大的总中继数量的乘积。
本优选实现方式通过对M与N的比例大小进行限制，使恢复的业务总数量的权重远大于总中继数量的权重，从而保证恢复的业务总数量最多为第一目标，总中继数量最少为第二目标。
由于在实际情况中，在求最小值时通常需要得到的是最小正值，针对此情况，存在以下优选的实现方式，所述求解数学模型，得到最优的光网络故障恢复路径，如图5所示，具体包括：
在步骤401中，若求解得到目标函数的值为负值，则将所述恢复的业务总数量所对应的权重在预设范围内依次递减并计算对应的目标函数的值，直至求解得到目标函数的值为正值。
在步骤402中，在目标函数为正值时所对应的光网络故障恢复路径即为最优的光网络故障恢复路径。
本优选实现方式通过调整恢复的业务数量的权重，使计算得到的目标函数 为正值，以便于计算。
所述一个或者多个约束函数，具体包括：
源宿节点约束函数、路径连通性约束函数、损耗约束函数、链路波长约束函数和节点上的中继数量约束函数中的一种或多种。
其中，所述源宿节点约束函数用于约束在成功恢复的业务的源节点和宿节点之间，业务的传输方向是唯一的。
所述路径连通性约束函数用于约束在成功恢复的业务传输的路径上的每一条链路均能够被业务使用，从而使业务能够沿所对应的路径从源节点到达宿节点。
所述损耗约束函数用于约束每一条子路径的损耗不超出相应的损耗允许范围。
所述链路波长约束函数用于约束在链路上所占用的波道数量不超出链路所允许的波道数量。
所述节点上的中继数量约束函数用于约束一个节点上所添加的中继数量不超出相应的允许添加的中继数量的范围。
上述所使用的约束函数的类型、数量及形式并非是完全固定的，而是可根据拓扑网络的网络故障恢复需求，选择其中的一种或多种进行相应的约束。其中，通过源宿节点约束函数和路径连通性约束函数能够约束确定业务的路径唯一性和完整性，通过损耗约束函数、链路波长约束函数和节点上的中继数量约束函数能够约束链路上所使用的波道数量和到达中继节点时的损耗，从而确保业务正常到达。在约束函数的基础上，再使用目标函数得到最优的光网络故障恢复路径。
所述源宿节点约束函数具体为：
针对故障网络e中的每一条需要恢复的业务k，在被业务k所使用的所有链路中，找到从业务k的源节点s出发的链路(s，j)和到达业务k的宿节点d的链路(i，d)。
当业务k被成功恢复时，所述业务k所对应的链路(s，j)有且仅有一条， 所对应的链路(i，d)有且仅有一条。
当业务k未被恢复时，所述业务k不存在对应的链路(s，j)和链路(i，d)。
所述源宿节点约束函数以数学公式的形式表现为以下公式一和公式二，其中，公式一为：
Figure PCTCN2023070894-appb-000024
其中
Figure PCTCN2023070894-appb-000025
公式二为：
Figure PCTCN2023070894-appb-000026
其中
Figure PCTCN2023070894-appb-000027
s为业务k的源节点，d为业务k的宿节点，当业务k算通时，t k的值为1，否则，t k的值为0，
Figure PCTCN2023070894-appb-000028
代表对故障网络e中的任意一条需要恢复的业务，均应满足相应的公式。
在公式一中，决策变量
Figure PCTCN2023070894-appb-000029
中的h的值被限定为1，a的值被限定为1，i的值被限定为s，在此情况下，
Figure PCTCN2023070894-appb-000030
代指链路(s，j)是否是业务k的第1条子路径下的第一条链路，当链路(s，j)是业务k的第1条子路径下的第一条链路时，
Figure PCTCN2023070894-appb-000031
的值为1，否则，
Figure PCTCN2023070894-appb-000032
的值为0；其中，将i的值被限定为s，或将h和a的值均限定为1这两种方式均能够代指相应链路是否是业务k的第1条子路径下的第一条链路，可择一使用，在此通过将两种方式合并使用以减少计算量。
即在
Figure PCTCN2023070894-appb-000033
的值为1时，链路(s，j)必然是业务k的第1条子路径下的第一条链路，故公式一所代表的含义为：对故障网络e中的任意一条需要恢复的业务k，均应满足条件：当业务k成功恢复时，业务k所使用的从业务k的源节点s出发的链路有且仅有一条，即业务k从源节点s出发时的方向唯一；当k未恢复时，业务k从源节点s出发后不使用任何链路。
在公式二中，决策变量
Figure PCTCN2023070894-appb-000034
中b的值被限定为1，j的值被限定为d，在此 情况下，
Figure PCTCN2023070894-appb-000035
代指链路(i，d)是否是业务k的第h条子路径下的最后一条链路，当链路(i，d)是业务k的第h条子路径下的最后一条链路时，
Figure PCTCN2023070894-appb-000036
的值为1，否则，
Figure PCTCN2023070894-appb-000037
的值为0。
即在
Figure PCTCN2023070894-appb-000038
的值为1时，链路(i，d)必然是业务k的第h条子路径下的最后一条链路，且到达业务的宿节点d，故公式一所代表的含义为：对网络拓扑中的任意一条业务k，均应满足以下条件：
当业务k算通时，业务k所使用的到达业务k的宿节点d出发的链路有且仅有一条，即业务k进入宿节点d的方向唯一；当k未算通时，业务k不使用任何进入源节点d的链路。
所述源宿节点约束函数通过公式一和公式二共同约束故障网络e中需要恢复的业务，使业务出发时的方向和到达时的方向唯一，从而保证业务的传输方向唯一。
所述路径连通性约束函数具体为：
针对故障网络e中每一条需要恢复的业务k下的每一个非中继节点n，业务k下进入节点n的链路(i，n)与业务k下从节点n出发的链路(n，j)的数量相同。
针对每一条业务k下的每一个中继节点m，业务k下进入节点m的链路(i，m)与从业务k下节点m出发的链路(m，j)的数量相同。
所述路径连通性约束函数以数学公式的形式表现为公式三和公式四，其中公式三为：
Figure PCTCN2023070894-appb-000039
其中
Figure PCTCN2023070894-appb-000040
h；n∈V且n≠s且n≠d；
公式四为：
Figure PCTCN2023070894-appb-000041
其中
Figure PCTCN2023070894-appb-000042
h；n∈V且n≠s且n≠d
Figure PCTCN2023070894-appb-000043
h；n∈V且n≠s且n≠d代表在故障网络e中的任意业务下，除业务的源节点s和宿节点d以外的任意节点均应满足相应的公式。
在公式三中的等号左侧，决策变量
Figure PCTCN2023070894-appb-000044
中b的值被限定为0，在此情况下，
Figure PCTCN2023070894-appb-000045
代指链路(i，n)是否为业务k的第h个路径下的最后一条链路，当链路(i，n)是业务k的第h个子路径下的最后一条链路时，
Figure PCTCN2023070894-appb-000046
的值为0，否则，当链路(i，n)是业务k的第h个子路径下的链路，且链路(i，n)不是业务k的第h个子路径下的最后一条链路时，
Figure PCTCN2023070894-appb-000047
的值为1。
在公式三中的等号右侧，决策变量
Figure PCTCN2023070894-appb-000048
中a的值被限定为0，在此情况下，
Figure PCTCN2023070894-appb-000049
代指链路(n，j)是否为业务k的第h个子路径下的第一条链路，当链路(n，j)是业务k的第h个子路径下的第一条链路时，
Figure PCTCN2023070894-appb-000050
的值为0，否则，当链路(n，j)是业务k的第h个子路径下的链路，且链路(n，j)不是业务k的第h个子路径下的第一条链路时，
Figure PCTCN2023070894-appb-000051
的值为1；
其中，链路(i，n)与链路(n，j)分别是以节点n为宿节点和以节点n为源节点的链路。
当节点n为业务k的中继节点时，链路(i，n)必然是业务k下的一条子路径的最后一条链路，即
Figure PCTCN2023070894-appb-000052
等于0，且链路(n，j)必然是业务k下的一条子路径的第一条链路，即
Figure PCTCN2023070894-appb-000053
等于0，故对于业务k中的中继节点，公式三始终成立。
当n是业务k的非中继节点时，上述公式三的等号左侧的含义为：在业务k的链路中，被业务k占用且宿节点为非中继节点n的链路的数量；上述公式三的等号右侧的含义为：在业务k的链路中，被业务k占用且源节点为非中继节点n的链路的数量。即公式三所代表的含义为：对故障网络e中的任意需要恢复的业务k，在业务k下的所有进出节点n的链路中，进入节点n的链路(i，n)与从节点n出发的链路(n，j)的数量相同。
在公式四的等号左侧，决策变量
Figure PCTCN2023070894-appb-000054
中b的值被限定为1，在此情况下，
Figure PCTCN2023070894-appb-000055
代指链路(i，n)是否为业务k的第h个子路径下的最后一条链路，当链路(i，n)为业务k的第h个路径下的最后一条链路时，
Figure PCTCN2023070894-appb-000056
的值为1，否则，
Figure PCTCN2023070894-appb-000057
的值为0。
在公式四的等号右侧，决策变量
Figure PCTCN2023070894-appb-000058
中b的值被限定为1，在此情况下，
Figure PCTCN2023070894-appb-000059
代指链路(n，j)是否为业务k的第h+1个子路径下的最后一条链路，当链路(n，j)为业务k的第h+1个路径下的最后一条链路时，
Figure PCTCN2023070894-appb-000060
的值为1，否则，
Figure PCTCN2023070894-appb-000061
的值为0。
其中，当节点n为业务k的非中继节点时，链路(i，n)必然不是业务k的第h个子路径下的最后一条链路，即
Figure PCTCN2023070894-appb-000062
等于0，链路(n，j)必然不是业务k的第h+1个子路径下的第一条链路，即
Figure PCTCN2023070894-appb-000063
等于0，故对于业务k中的非中继节点，公式四始终成立。
当节点n为业务k的中继节点时，公式四的等号左侧代表业务k所使用的以节点n为宿节点的链路的数量，等号右侧代表业务k所使用的以节点n为源节点的链路的数量；即公式四所代表的含义为：对故障网络e下的任意需要恢复的业务k的中继节点n，均应满足条件：业务k中进入中继节点n的链路的数量和业务k中从中继节点n出发的链路的数量相同。
所述损耗约束函数具体为：
针对故障网络e中每一条需要恢复的业务k下的每一条子路径h，将子路径h下每一条链路(i，j)的损耗相加得到子路径h的总损耗。
每一条子路径h的总损耗小于等于相应的预设损耗阈值。
所述损耗约束函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000064
其中
Figure PCTCN2023070894-appb-000065
h
lf ij代表链路(i，j)的损耗，Th代表预设损耗阈值，所述预设损耗阈值和是由本领域技术人员根据用户或网络拓扑的OSNR要求设定的。可针对所有链路设定相同的预设损耗阈值，也可针对每一条链路预设相应的预设损耗阈值。
Figure PCTCN2023070894-appb-000066
h代表对故障网络e中的任意一条需要恢复的业务下的任意一条子路径，均应满足相应的公式。
上述公式所代表的含义为：对故障网络e中的任意一条需要恢复的业务下的任意一条子路径h，将子路径h下每一条链路的损耗相加得到子路径的总损耗，得到的子路径的损耗应小于等于相应的预设损耗阈值。
所述链路波长约束函数具体为：
针对故障网络e下的每一条链路(i，j)，在链路(i，j)上正向占用波道的业务k1的数量和反向占用波道的业务k2的数量的和小于等于链路(i，j)所对应的预设波道数量阈值。
所述链路波长约束函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000067
其中
Figure PCTCN2023070894-appb-000068
|W ij|代表在链路上所能够支持的最多的波道数量，即预设波道数量阈值，其中，W ij代表链路上所能够支持的波道的集合，|W ij|为集合中元素的数量。所述预设波道数量阈值是由本领域技术人员根据拓扑网络的需求分析设定的。
Figure PCTCN2023070894-appb-000069
w∈W代表针对故障网络e中的任意链路，均应满足相应的公式。
在上述公式的左侧，决策变量
Figure PCTCN2023070894-appb-000070
代表业务k是否正向使用链路(i，j)，由于业务在使用链路时必然占用一条波道，故决策变量
Figure PCTCN2023070894-appb-000071
可代表业务k是否在链路(i，j)上正向占用波道，当业务k在链路(i，j)上正向占用波道时，
Figure PCTCN2023070894-appb-000072
的值为1，否则，
Figure PCTCN2023070894-appb-000073
的值为0。
决策变量
Figure PCTCN2023070894-appb-000074
代表业务k是否反向使用链路(i，j)，由于业务在使用链路时必然占用一条波道，故决策变量
Figure PCTCN2023070894-appb-000075
可代表业务k是否在链路(i，j)上反向占用波道，当业务k在链路(i，j)上反向向占用波道时，
Figure PCTCN2023070894-appb-000076
的值为1，否则，
Figure PCTCN2023070894-appb-000077
的值为0。
即上述公式左侧所代表的含义为：所有在链路(i，j)上正向占用波道的业务的数量加上所有在链路(i，j)上反向占用波道的业务的数量，上述公式整体的含义为：在链路(i，j)上被正向占用的波道和被反向占用的波道的数量不超出预设波道数量阈值。
所述节点上的中继数量约束函数具体为：
针对故障网络e中的每一个节点n，找到故障网络e中以节点n为中继节点的业务k，节点n所对应的业务k的数量小于等于节点n所对应的预设最大中继数量。
所述节点上的中继数量约束函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000078
其中
Figure PCTCN2023070894-appb-000079
|R n|代表节点n上所能够设置的最大的中继个数，即所述预设最大中继个数，其中，R n为节点n上所能够设置的中继的集合，|R n|为集合中元素的数量，所述预设最大中继个数是由本领域技术人员根据拓扑网络中的路径规划需求分析设定的。
Figure PCTCN2023070894-appb-000080
代表对故障网络e中的任意节点n，均应满足相应的公式。
在上述公式左侧，决策变量
Figure PCTCN2023070894-appb-000081
中a被限定为1，h的累加范围从2开始，则
Figure PCTCN2023070894-appb-000082
用于指代链路(n，j)是否是业务k的除第一个子路径以外的任意子路径下的第一条链路，当链路(n，j)是业务k的除第一个子路径以外的任意子路径下的第一条链路时，
Figure PCTCN2023070894-appb-000083
的值为1，否则，
Figure PCTCN2023070894-appb-000084
的值为0。
上述公式左侧所代表的含义与所述目标函数中总中继数量类似，上述公式所代表的含义为：在节点n上所设置的中继的数量。上述公式整体的含义为：针对每个故障网络e，在节点n上所设置的中继的数量小于等于预设最大中继个数|R n|。
在实际计算过程中，由于目标函数中存在取最大值这一非线性操作，即对应公式中的Max e()，故其目标函数本身是非线性的，所建立的数学模型同样是非线性的，在求解过程中，通常非线性数学模型比线性数学模型的计算过程 更复杂，其计算量也较线性数学模型大，为了减少数学模型求解的计算量，缩减光网络故障分析的时间，存在以下优选的实施例，具体包括：
将目标函数转化为一个线性的目标函数和相应的约束函数，具体为：引入新的决策变量x n，用于代表每个节点n的中继数量，并以此决策变量替换目标函数中的相应位置，引入节点上的中继数量第二约束函数，用于约束节点n上的中继数量大于等于节点n位于任何故障网络中时所添加的中继的数量。
所得到的新的目标函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000085
其中，决策变量x n代表节点n的中继数量。
所新引入的节点上的中继数量第二约束函数以数学公式的形式表现为：
Figure PCTCN2023070894-appb-000086
其中
Figure PCTCN2023070894-appb-000087
上述中继数量第二约束函数的含义为：对任意节点n，均应满足：在节点n上所添加的中继的数量大于等于在任意故障网络中节点n上所添加的中继的数量。
在上述目标函数和约束函数的基础上，所述求解数学模型的一种具体的实现方法包括：
针对每个故障网络，在一个或多个约束函数中，选择一个或多个约束函数作为基础，得到满足所选的约束函数的光网络故障恢复路径规划情况，再使用未选中的约束函数，对所有可能的光网络故障恢复路径规划情况进行限缩，以缩小可选的光网络路径范围，最后代入目标函数，计算限缩后的各种光网络路径规划情况所对应的目标函数的值，若得到目标函数的值为正值，则选择目标函数所对应的光网络故障恢复路径作为最优的光网络故障恢复路径。
若得到目标函数的值为负值，则以依次递减的方式调整恢复的业务总数量所对应的权重，再计算目标函数的值，直至目标函数为正值，以最终得到正值的目标函数所对应的光网络故障恢复路径作为最优的光网络故障恢复路径。
其另一种具体的实现方法为对其进行整数规划求解，所能够采用的算法如分支定界法，割平面法等。在实际应用情况中，由于实际应用的拓扑网络的规模通常较大，故常使用整数规划求解器进行求解，如Cplex、Gurobi等。
根据求解所述数据模型得到的最优整数解所对应的规划的光网络下，能够在满足约束函数的前提下，总中继数量最少且恢复的业务总数量最多。
实施例2:
本发明基于实施例1所描述的方法基础上，结合具体的应用场景，并借由相关场景下的技术表述来阐述本发明特性场景下的实现过程。
如图6所示为一个光传输网络，在这个光传输网络中，存在A、B、C、D、E、F和G这7个节点，由这7个节点所组成的网络拓扑中共有9条链路，分别是(A，B)、(A，C)、(B，D)、(B，E)、(C，D)、(C，F)、(D，E)、(E，G)和(F，G)，每条链路上的1-80波长所对应的1-80波道均可被用，即每条链路上所对应的预设波道数量阈值均为80。在该网络拓扑中未存在链路中断时，所规划的每条业务以及相应所使用的路径和波道具体如下：
建立有20条自源节点A到达宿节点E的100G业务，该业务依次经过节点A、节点B和节点E，占用1-20波长，将该20条业务记为s 1-s 20；
建立有10条自源节点D到达宿节点G的100G业务，该业务依次经过节点D、节点E和节点G，占用1-10波长，将该10条业务记为s 21-s 30；
建立有10条自源节点B到达宿节点G的100G业务，该业务依次经过节点B、节点E和节点G，占用21-30波长，将该10条业务记为s 31-s 40；
建立有10条自源节点F到达宿节点B的100G业务，该业务依次经过节点F、节点C、节点D和节点B，占用1-10波长，将该10条业务记为s 41-s 50。
设定总中继数量所对应的权重N的值为1，恢复的业务总数量所对应的权重M的取值需大于N与最大的总中继数量的乘积，根据拓扑网络的规模，得到网络 中的节点数量，并计算节点上可能的中继数量，从而设定权重M的取值，这里将M的取值设定为11520。设定一条业务中所允许的最多的子路径的数量H为3。
分别以9条链路中的每一条链路分别中断为基础，建立相应的故障网络，如当链路(A，B)中断时，经过链路(A，B)的业务s 1-s 20被中断，即所对应的故障网络中需要修复的业务T AB的集合为s 1-s 20；同样的，当链路(B，E)断开时，所对应T BE的集合为s 1-s 20和s 31-s 40；由9条链路分别断开所组成的故障网络的集合为{e AB，e AC，e BD，e BE，e CD，e CF，e DE，e EG，e FG}。
针对拓扑网络中的所有故障网络，构建一个目标函数；针对每一个故障网络，构建一个或多个约束函数。
通过对目标函数和约束函数进行求解，得到最优的光网络故障恢复路径。
在求解过程中，若得到的目标函数的值为正值，则选择目标函数最小值所对应的光网络故障恢复路径作为最优的光网络故障恢复路径。
若得到目标函数的值为负值，则根据拓扑网络的规模，制定权重M的可调范围和调节梯度，在此实施例中，制定权重M的可调范围为[6000，11520]，调节梯度为500，将权重M从11520依次递减500，每递减一次重新计算一次目标函数的值，直至得到目标函数的值为正值时停止递减，以目标函数为正值时所对应的光网络故障恢复路径作为最优的光网络故障恢复路径。
实施例3:
如图7所示，是本发明实施例的光网络故障分析装置的架构示意图。本实施例的光网络故障分析装置包括一个或多个处理器21以及存储器22。其中，图7中以一个处理器21为例。
处理器21和存储器22可以通过总线或者其他方式连接，图7中以通过总线连接为例。
存储器22作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序和非易失性计算机可执行程序，如实施例1中的光网络故障分析方法。处理器21通过运行存储在存储器22中的非易失性软件程序和指令，从而执行光网络故障分析方法。
存储器22可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器22可选包括相对于处理器21远程设置的存储器，这些远程存储器可以通过网络连接至处理器21。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述程序指令/模块存储在所述存储器22中，当被所述一个或者多个处理器21执行时，执行上述实施例1和实施例2中的光网络故障分析方法，例如，执行以上描述的图1、图2和图5所示的各个步骤。
值得说明的是，上述装置和系统内的模块、单元之间的信息交互、执行过程等内容，由于与本发明的处理方法实施例基于同一构思，具体内容可参见本发明方法实施例中的叙述，此处不再赘述。
本领域普通技术人员可以理解实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：只读存储器(ROM，Read Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁盘或光盘等。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种光网络故障分析方法，其特征在于，包括：
    以网络拓扑中每一根链路分别断开为基础，建立多个故障网络；
    根据建立的故障网络和故障网络中需要恢复的业务，生成目标函数，并针对每一个故障网络，生成一个或多个约束函数；
    结合目标函数和约束函数，建立以总中继数量最少、恢复的业务总数量最多为目标的数学模型；
    求解数学模型，得到最优的光网络故障恢复路径。
  2. 根据权利要求1所述的光网络故障分析方法，其特征在于，所述目标函数具体为：
    以节点位于不同故障网络中时，节点上所添加的最大的中继数量作为节点中继数量，将每个故障网络中的每个节点的节点中继数量相加得到所述总中继数量；
    将每个故障网络中成功恢复的业务数量相加得到所述恢复的业务总数量；
    所述目标函数为所述恢复的业务总数量与所述总中继数量的加权求差。
  3. 根据权利要求2所述的光网络故障分析方法，其特征在于，所述求解数学模型，得到最优的光网络故障恢复路径，具体包括：
    若求解得到目标函数的值为负值，则将所述恢复的业务总数量所对应的权重在预设范围内依次递减并计算对应的目标函数的值，直至求解得到目标函数的值为正值，在目标函数为正值时所对应的光网络故障恢复路径即为最优的光网络故障恢复路径。
  4. 根据权利要求1所述的光网络故障分析方法，其特征在于，所述一个或者多个约束函数，具体包括：
    源宿节点约束函数、路径连通性约束函数、损耗约束函数、链路波长约束 函数和节点上的中继数量约束函数中的一种或多种。
  5. 根据权利要求4所述的光网络故障分析方法，其特征在于，所述源宿节点约束函数具体为：
    针对故障网络e中的每一条需要恢复的业务k，在被业务k所使用的所有链路中，找到从业务k的源节点s出发的链路(s，j)和到达业务k的宿节点d的链路(i，d)；
    当业务k被成功恢复时，所述业务k所对应的链路(s，j)有且仅有一条，所对应的链路(i，d)有且仅有一条；
    当业务k未被恢复时，所述业务k不存在对应的链路(s，j)和链路(i，d)。
  6. 根据权利要求4所述的光网络故障分析方法，其特征在于，所述路径连通性约束函数具体为：
    针对故障网络e中每一条需要恢复的业务k下的每一个非中继节点n，业务k下进入节点n的链路(i，n)与业务k下从节点n出发的链路(n，j)的数量相同；
    针对每一条业务k下的每一个中继节点m，业务k下进入节点m的链路(i，m)与从业务k下节点m出发的链路(m，j)的数量相同。
  7. 根据权利要求4所述的光网络故障分析方法，其特征在于，所述损耗约束函数具体为：
    针对故障网络e中每一条需要恢复的业务k下的每一条子路径h，将子路径h下每一条链路(i，j)的损耗相加得到子路径h的总损耗；
    每一条子路径h的总损耗小于等于相应的预设损耗阈值。
  8. 根据权利要求4所述的光网络故障分析方法，其特征在于，所述链路波 长约束函数具体为：
    针对故障网络e下的每一条链路(i，j)，在链路(i，j)上正向占用波道的业务k1的数量和反向占用波道的业务k2的数量的和小于等于链路(i，j)所对应的预设波道数量阈值。
  9. 根据权利要求4所述的光网络故障分析方法，其特征在于，所述节点上的中继数量约束函数具体为：
    针对故障网络e中的每一个节点n，找到故障网络e中以节点n为中继节点的业务k，节点n所对应的业务k的数量小于等于节点n所对应的预设最大中继数量。
  10. 一种光网络故障分析装置，其特征在于，所述装置包括：
    至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述处理器执行，用于执行权利要求1-9任一所述的光网络故障分析方法。
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