Optical network reliability evaluation method and device, electronic equipment, storage medium and program product

Abstract

The invention provides an optical network reliability evaluation method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a transmission unit corresponding to a target service in a target optical network; determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data; determining a state transition relationship of the transmission unit, and constructing a dynamic Bayesian network based on the state transition relationship; determining current operation data of the transmission unit, and processing the current operation data based on the fault classification model to obtain current state information of the transmission unit; and processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network. According to the invention, the accuracy and interpretability of evaluation are improved, and continuously changing environments and conditions in the operation process of the optical network can be coped with.

Description

Technical Field

[0001] The present disclosure relates to the technical field of optical networks, and in particular, to a method, device, electronic device, storage medium, and program product for evaluating the reliability of an optical network. Background Art

[0002] This section aims to provide background or context for the embodiments of the present disclosure described in the claims. The description herein is not admitted to be prior art merely because it is included in this section.

[0003] With the rapid development of large-bandwidth services, as the core infrastructure to support these services, optical networks are facing higher transmission requirements, including high speed, large capacity, low latency, and high reliability.

[0004] However, the complexity and wide coverage of optical networks pose great challenges in practical applications, especially in ensuring reliable and stable transmission of services. Operators often ensure service quality through a Service Level Agreement (SLA), but the inventors of the present disclosure have found that most existing reliability evaluation methods focus on the design stage and lack continuous monitoring and accurate evaluation of the state of optical networks during actual operation. Summary of the Invention

[0005] In view of this, an object of the present disclosure is to provide a method, device, electronic device, storage medium, and program product for evaluating the reliability of an optical network, which can at least solve one of the technical problems in the related art to a certain extent.

[0006] Based on the above object, in the first aspect of an exemplary embodiment of the present disclosure, a method for evaluating the reliability of an optical network is provided, including:

[0007] Determine the transmission unit corresponding to the target service in the target optical network;

[0008] Determine the historical operation data of the transmission unit, and construct a fault classification model based on the historical operation data;

[0009] Determine the state transition relationship of the transmission unit, and construct a dynamic Bayesian network based on the state transition relationship;

[0010] Determine the current operation data of the transmission unit, and process the current operation data based on the fault classification model to obtain the current state information of the transmission unit;

[0011] Process the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network.

[0012] In some exemplary embodiments, determining the transmission unit corresponding to the target service in the target optical network includes:

[0013] Determine the transmitting unit, receiving unit, optical fiber amplifier, and optical fiber segment through which the target service passes when transmitted in the target optical network.

[0014] In some exemplary embodiments, determining the historical operation data of the transmission unit and constructing a fault classification model based on the historical operation data includes:

[0015] Determine the bit error rates of the transmitting unit and the receiving unit;

[0016] Determine the input power and output power of the optical fiber amplifier;

[0017] Construct a random forest model, and train the random forest model based on the bit error rates, the input power, and the output power to obtain the fault classification model.

[0018] In some exemplary embodiments, determining the state transition relationship of the transmission unit and constructing a dynamic Bayesian network based on the state transition relationship includes:

[0019] Determine the failure rate and repair rate of the transmission unit;

[0020] Based on the failure rate and the repair rate, determine the state transition relationship;

[0021] Construct the dynamic Bayesian network based on the state transition relationship.

[0022] In some exemplary embodiments, determining the current operation data of the transmission unit and processing the current operation data based on the fault classification model to obtain the current state information of the transmission unit includes:

[0023] Based on several decision trees in the fault classification model, respectively predict the current operation data;

[0024] Integrate the prediction results of all the decision trees to obtain the current state information of the transmission unit.

[0025] In some exemplary embodiments, processing the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network includes:

[0026] Use the current state information as an observation value, and perform reliability inference through the dynamic Bayesian network based on the observation value to obtain the reliability evaluation result.

[0027] Based on the same inventive concept, a second aspect of the exemplary embodiments of the present disclosure provides an optical network reliability evaluation device, including:

[0028] A transmission unit determination module configured to determine a transmission unit corresponding to a target service in a target optical network;

[0029] A fault classification model construction module configured to determine historical operation data of the transmission unit and construct a fault classification model based on the historical operation data;

[0030] A dynamic Bayesian network construction module configured to determine a state transition relationship of the transmission unit and construct a dynamic Bayesian network based on the state transition relationship;

[0031] A state information determination module configured to determine current operation data of the transmission unit, process the current operation data based on the fault classification model, and obtain current state information of the transmission unit;

[0032] A reliability evaluation module configured to process the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network.

[0033] Based on the same inventive concept, a third aspect of the exemplary embodiments of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the first aspect is implemented.

[0034] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method described in the first aspect.

[0035] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product including computer program instructions that, when run on a computer, cause the computer to execute the method described in the first aspect.

[0036] As can be seen from the above, the optical network reliability evaluation method, apparatus, electronic device, storage medium, and program product provided by the embodiments of the present disclosure include: determining a transmission unit corresponding to a target service in a target optical network; determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data; determining a state transition relationship of the transmission unit, and constructing a dynamic Bayesian network based on the state transition relationship; determining current operation data of the transmission unit, and processing the current operation data based on the fault classification model to obtain current state information of the transmission unit; and processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network. The present disclosure improves the accuracy and interpretability of the evaluation, and can cope with the changing environment and conditions during the operation of the optical network. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only the embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0038] Figure 1 FIG. 1 is a schematic diagram of an application scenario of the optical network reliability evaluation method provided by an exemplary embodiment of the present disclosure;

[0039] Figure 2 FIG. 2 is a schematic flowchart of the optical network reliability evaluation method provided by an exemplary embodiment of the present disclosure;

[0040] Figure 3 FIG. 3 is a schematic diagram of a reliability evaluation model provided by an exemplary embodiment of the present disclosure;

[0041] Figure 4 FIG. 4 is a schematic diagram of the structure of a service transmission optical path provided by an exemplary embodiment of the present disclosure;

[0042] Figure 5 FIG. 5 is a schematic diagram of a dynamic Bayesian network model provided by an exemplary embodiment of the present disclosure;

[0043] Figure 6 FIG. 6 is a schematic diagram of the structure of an optical network reliability evaluation apparatus provided by an exemplary embodiment of the present disclosure;

[0044] Figure 7 FIG. 7 is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] It is understandable that before using the technical solutions disclosed in the embodiments of the present application, the types, usage scopes, usage scenarios, etc. of the personal information involved in the present application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0046] For example, when responding to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0047] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0048] It is understandable that the above process of notifying and obtaining the user's authorization is only illustrative and does not limit the implementation manner of the present application. Other manners that meet relevant laws and regulations can also be applied to the implementation manner of the present application.

[0049] It is understandable that the data involved in the present technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of corresponding laws, regulations and related provisions.

[0050] To make the purpose, technical solution and advantages of the present disclosure clearer and more understandable, the principles and spirit of the present disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and then implement the present disclosure, and do not limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to be able to fully convey the scope of the present disclosure to those skilled in the art.

[0051] In this article, it should be understood that the quantity of any element in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0052] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure belongs. The terms "first", "second" and similar terms used in the embodiments of the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly. The article "a" or "an" before an element does not exclude the existence of a plurality of such elements.

[0053] Next, with reference to several representative embodiments of the present disclosure, the principles and spirit of the present disclosure will be elaborated in detail.

[0054] As described in the background art, with the rapid development of large-bandwidth services, optical networks, as the core infrastructure to support these services, are facing higher transmission requirements, including high speed, large capacity, low latency and high reliability. However, the complexity and wide coverage of optical networks pose great challenges in practical applications, especially in ensuring reliable and stable transmission of services. Operators often use Service Level Agreement (SLA) to ensure service quality, but the inventors of the present disclosure have found that most of the existing reliability assessment methods focus on the design stage and lack continuous monitoring and accurate assessment of the state of optical networks during actual operation.

[0055] In related technologies, the reliability assessment of optical networks is generally divided into two methods: model-driven and data-driven. The model-driven method relies on theoretical models and prior knowledge and is suitable for assessments in data-scarce or design stages; while the data-driven method focuses on using actual operation data to evaluate the network state. The inventors of the present disclosure have found that with the gradual completion of network deployment, especially the huge scale of fiber optic networks, operators are in urgent need of efficient online reliability assessment methods to timely maintain network equipment, optimize transmission paths, and reduce losses caused by failures.

[0056] To solve the above problems, the present disclosure provides a reliability evaluation scheme for an optical network, specifically including: determining a transmission unit corresponding to a target service in the target optical network; determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data; determining a state transition relationship of the transmission unit, and constructing a dynamic Bayesian network based on the state transition relationship; determining current operation data of the transmission unit, processing the current operation data based on the fault classification model to obtain current state information of the transmission unit; and processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network.

[0057] Specifically, the present disclosure proposes an online reliability evaluation method for optical network services that combines machine learning and dynamic Bayesian network (DBN). By making full use of limited monitoring data and prior knowledge, it dynamically infers the reliability of service transmission. This method can not only improve the accuracy and interpretability of the evaluation, but also cope with the ever-changing environment and conditions during the operation of the optical network. Compared with the evaluation methods in related technologies, this method can obtain real-time reliability evaluation results of service transmission, provide more accurate decision-making support for operators, and help improve the stability of the optical network.

[0058] After introducing the basic principle of the present disclosure, the following specifically introduces various non-limiting implementation manners of the present disclosure.

[0059] Reference Figure 1 , which is a schematic diagram of an application scenario of the optical network reliability evaluation method provided by an exemplary embodiment of the present disclosure.

[0060] In this application scenario, there are a terminal device 101, a server 102, and a data storage system 103. Among them, the terminal device 101, the server 102, and the data storage system 103 can all be connected through a wired or wireless communication network to achieve data interaction.

[0061] The terminal device 101 can be an electronic device near the user side with data transmission, multimedia input / output functions, including but not limited to desktop computers, mobile phones, mobile computers, tablet computers, media players, smart wearable devices, personal digital assistants (PDAs), or other electronic devices capable of implementing the above functions. The electronic device can include a processor and a display screen with touch input function. The display screen is used to present a graphical user interface, and the graphical user interface can display an application interface. The processor is used to process application data, generate a graphical user interface, and control the display of the graphical user interface on the display screen.

[0062] Both the server 102 and the data storage system 103 can be independent physical servers, or server clusters or distributed systems composed of multiple physical servers. They can also be cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0063] In some exemplary embodiments, the optical network reliability evaluation method can run on the terminal device 101 or the server 102.

[0064] When the optical network reliability evaluation method runs on the server 102, the server 102 is used to provide the optical network reliability evaluation service to the users of the terminal device 101. A client for communicating with the server 102 is installed in the terminal device 101. The user can specify the object to be evaluated through this client. The server 102 determines the transmission unit corresponding to the target service in the target optical network; determines the historical operation data of the transmission unit, and constructs a fault classification model based on the historical operation data; determines the state transition relationship of the transmission unit, and constructs a dynamic Bayesian network based on the state transition relationship; determines the current operation data of the transmission unit, processes the current operation data based on the fault classification model to obtain the current state information of the transmission unit; processes the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network. The server 102 can send the reliability evaluation result to the client, and the client displays the reliability evaluation result to the user to provide more accurate decision support for the user.

[0065] When the optical network reliability evaluation method runs on the server 102, the method can be implemented and executed based on the cloud interaction system.

[0066] A large amount of data, such as historical operation data, is stored in the data storage system 103.

[0067] The following combines Figure 1 the application scenarios to describe the optical network reliability evaluation method according to the exemplary embodiments of the present disclosure. It should be noted that the above application scenarios are only shown for the convenience of understanding the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this regard. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0068] Refer to Figure 2 , which is a schematic flowchart of a method for evaluating the reliability of an optical network provided by an exemplary embodiment of the present disclosure.

[0069] Optical network reliability evaluation method, comprising the following steps:

[0070] Step S210, determining a transmission unit corresponding to a target service in a target optical network.

[0071] In some exemplary embodiments, the determining a transmission unit corresponding to a target service in a target optical network comprises:

[0072] Determining a transmitting unit, a receiving unit, an optical fiber amplifier, and an optical fiber segment through which the target service passes when transmitted in the target optical network.

[0073] As an example, obtaining the hardware transmission units through which an optical network service S passes. Denote it as S = {a1, a2, …, a n , b1, b2, …, b m , c1, c2, …, c w}, where a1, a2, …, a n correspond to the transmitting / receiving units through which the optical network service passes, such as optical cross-connect (OXC), and n represents the number of transmitting / receiving units passed through; b1, b2, …, b m correspond to the erbium-doped fiber amplifiers (EDFAs) through which the optical network service passes, and m represents the number of EDFAs passed through; c1, c2, …, c w correspond to the optical fiber segments through which the optical network service passes, and w represents the number of optical fiber segments passed through.

[0074] Step S220, determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data.

[0075] In some exemplary embodiments, the determining historical operation data of the transmission unit and constructing a fault classification model based on the historical operation data comprises:

[0076] Determining the bit error rates of the transmitting unit and the receiving unit;

[0077] Determining the input power and output power of the optical fiber amplifier;

[0078] Constructing a random forest model, and training the random forest model based on the bit error rates, the input power, and the output power to obtain the fault classification model.

[0079] As an example, determining the monitoring data of each component. For example, the OXC needs to monitor its bit error rate or signal-to-noise ratio. For a1, the monitored bit error rate is denoted as OSNR a1 , the EDFA needs to monitor its input and output powers. For b1, its input power is denoted as Input b1 , and its output power is denoted as Output b1。

[0080] As an example, a fault classification model is constructed, specifically including:

[0081] Fault identification is a multi-classification problem. Common multi-classification problem models include linear models (such as logistic regression), neighborhood-based models (such as KNN), probability-based models (such as naive Bayes), support vector machines (SVM), and ensemble models (such as random forest and XGBoost, etc.). In the present invention, the construction process of the multi-classification problem is described by taking the random forest as an example.

[0082] The random forest is an ensemble learning method based on multiple decision trees. Specifically, the random forest consists of W independent decision trees, and each decision tree can be represented as D w . Each decision tree samples a training subset from the original data D through Bagging, and the training set of the w-th tree can be represented as D w (w = 1, 2, 3, …, W). Each training subset is obtained by random sampling with replacement and can be represented as (1). Among them, H is the total number of samples, X represents the set of input vectors, and Y represents the set of label vectors. x represents the input vector, and y represents the corresponding label vector.

[0083] D w ={(x i , y i ) | x i ∈X, y i ∈Y, i = 1, 2,..., H} (1)

[0084] During the growth process of the decision tree, the splitting of each node needs to randomly select f features from F features and find the optimal splitting point among the selected feature values to make the Gini index reach the optimum. The calculation formula of the Gini index can be represented as (2). Among them, T represents the current node, and p k represents the proportion of class k in the current node T.

[0085]

[0086] For each decision tree, for the newly input x, each decision tree will give a classification prediction f m (x), which can be represented as (3).

[0087]

[0088] The random forest integrates the prediction results of W decision trees and assigns the input x to the class with the most votes, which can be represented as (4). Among them, 1(·) is the indicator function, which takes the value of 1 when f m (x) = k, and 0 otherwise.

[0089]

[0090] Step S230: Determine the state transition relationship of the transmission unit, and construct a dynamic Bayesian network based on the state transition relationship.

[0091] In some exemplary embodiments, the determining the state transition relationship of the transmission unit and constructing a dynamic Bayesian network based on the state transition relationship includes:

[0092] Determine the failure rate and repair rate of the transmission unit;

[0093] Based on the failure rate and the repair rate, determine the state transition relationship;

[0094] Construct the dynamic Bayesian network based on the state transition relationship.

[0095] As an example, obtain the failure rate and repair rate of the components through which the optical network service is transmitted. The average failure rate of the optical fiber is 0.38×10 -6 / km, and the average repair rate is 0.083 / km. The average failure rate of the EDFA is 2.85×10 -6 , and the average repair rate is 0.1667. The average failure rate of the OXC is 10 -4 , and the average repair rate is 0.1667.

[0096] As an example, determining the state transition relationship specifically includes:

[0097] Determine the failure mode and repair mode of the component and construct the state transition relationship between each state. Optical network components can generally be divided into three states: normal state (S0), degraded state (S1), and faulty state (S2). Generally, the aging or soft failure of the component will cause the component to enter the degraded state. In this patent, it is assumed that the transition of the component from the normal state to the degraded state and the faulty state both follow the exponential distribution. Similarly, the repair process of the component also conforms to the exponential distribution. This is a general assumption in the reliability assessment of optical networks. Then the time T1 for component a1 to have a soft failure follows the exponential distribution and can be expressed as (5). Where λ1 represents the average number of soft failures of component a1 per unit time (i.e., the failure rate). Similarly, the probabilities of component a1 having a hard failure and transitioning from the degraded state to a hard failure can be expressed as (6) and (7) respectively. Where λ2 represents the failure rate of component a1 having a hard failure, and λ3 represents the probability of the component transitioning from the degraded state to the hard failure state. The probability of component a1 completing the repair and transitioning from the hard failure state to the normal state can be expressed as (8), where μ represents the repair rate of the component.

[0098]

[0099] P(T4 ≤ t) = 1 - e -μt (8)

[0100] Assume the current time is t. For a DBN with Q nodes, the state transition probability between two time slices of related nodes can be expressed as formula (9). Here, Pa represents the set of parent nodes of node i.

[0101]

[0102] The transition probability of DBN nodes at different times is transferred according to the probability density functions of faults and repairs. Among them, represents the probability of remaining in the X = 0 state when the component is in the normal state X = 0 within the time interval Δt. It should be noted that when a soft fault occurs in the component, it does not fail immediately but enters the aging state, resulting in an earlier occurrence of the fault. After a soft fault occurs, the component is not repaired to the normal state, so repair is only carried out when a hard fault occurs in the component. After repair, the component returns to the normal state, that is Similarly, when the component enters the fault state, it can only remain in that fault state and wait for repair, and it can return to the normal working state only after repair. According to the actual situation, the component does not enter the degradation state when it is in the fault state, so The state transition probability is represented by a matrix as formula (10), where P(0|0) is a simplified representation of.

[0103]

[0104] In particular, for state transitions, it is necessary to ensure that the sum of the probabilities of transitioning to other states (including remaining in the current state) is 1 for any state. For an e-dimensional state transition matrix P, it needs to satisfy formula (11).

[0105]

[0106] As an example, construct a dynamic Bayesian network, specifically including:

[0107] A Bayesian network (BN) is a directed acyclic graph that describes the conditional probability relationships between data variables based on the theory of probabilistic inference. According to whether it includes time factors, BN can be divided into static Bayesian network (SBN) and dynamic Bayesian network (DBN). A BN consists of a set of random variables (nodes), conditional dependence relationships (directed arcs), and conditional probability tables (CPT). For a DBN with Q nodes, its joint probability distribution can be expressed as formula (12), where Pa represents the set of parent nodes of node i.

[0108]

[0109] The SBN is applicable to evaluating the reliability of time - independent objects. However, for large and complex networks, the SBN has low computational efficiency and cannot reflect the dynamic behavior of variables. Therefore, most studies use DBN to evaluate the reliability of complex networks. The probability distribution of DBN spanning multiple time slices can be expressed as formula (13). Where Q represents the number of nodes in the DBN, and X represents the i - th node in the t - th time slice.

[0110]

[0111] Collect data through the monitoring nodes of optical network services as the monitoring data input at time t. Use these data to train a fault classification model to identify the fault state and determine the state of the state nodes at time t. State t represents the set of component states at time t. represents the states of the corresponding components at time t, including normal operation, soft faults, and hard faults, etc., and then serves as the observed value input to the DBN. Use the state nodes as the prior knowledge of the dynamic Bayesian model at time t.

[0112] The DBN is an extension of the SBN in the time domain and consists of an initial network U0 and a transition network U. → where U0 defines the prior distribution of the initial state of the network. The transition network between adjacent time slices is represented by U. → This invention uses. to represent the state value of the i - th component node / subsystem node and system node at time t. Assume that there are a total of K nodes. Use. to represent the observed value of the j - th state node at time t, and there are a total of J state nodes. Therefore, according to formulas (9), (12), and (13), the joint distribution probability of the DBN composed of state nodes with observed values can be expressed as (14). Where. represents the prior distribution probability of the initial network. represents the initial moment. of the conditional probability, which depends on its parent node. represents the prior probability of the transition network. represents the conditional probability of the state node at the initial moment. If the initial state is normal, then. the probability of being normal is 1. and. respectively represent. and. the state probabilities at time t.

[0113]

[0114] As an example, Figure 3 a DBN including two time slices is shown, where the network at time t is called the initial network, and the network between time t and t+Δt is called the transition network. In the initial network, the nodes of the DBN are divided into four categories: state nodes, component nodes, subsystem nodes, and system nodes. Among them, the state nodes are represented by B, and the component nodes, subsystem nodes, and system nodes are represented by A. The state nodes process the collected monitoring data through the fault classification model to reflect the state of the component nodes; the component nodes represent the components in the actual network, such as transmitters, receivers, EDFAs, and optical fiber segments. The state of the subsystem nodes is determined by the connection relationship between the component nodes. The system node represents the network state that needs to be evaluated finally, and is used to estimate the service transmission reliability under the condition of partial component state observation values and known prior knowledge.

[0115] As an example, refer to Figure 4 , draw the DBN state transition diagram according to the logical structure of the hardware through which the service transmission passes. As Figure 4 shown, assume that a service starts from node 1 (OXC1) and reaches node 2 (OXC2) through two 80-km optical fibers and two EDFAs. The Figure 4 path of the service transmission shown can be converted into the DBN state transition model and can be represented by Figure 5 . In Figure 5 a two-time-slice dynamic Bayesian model is shown, and the time interval between the two time slices is Δt.

[0116] Step S240: Determine the current operating data of the transmission unit, and process the current operating data based on the fault classification model to obtain the current state information of the transmission unit.

[0117] In some exemplary embodiments, the determining the current operating data of the transmission unit, processing the current operating data based on the fault classification model to obtain the current state information of the transmission unit includes:

[0118] Based on several decision trees in the fault classification model, respectively predict the current operating data;

[0119] Integrate the prediction results of all the decision trees to obtain the current state information of the transmission unit.

[0120] Step S250: Process the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network.

[0121] In some exemplary embodiments, processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network includes:

[0122] Using the current state information as an observation value, performing reliability inference based on the observation value through the dynamic Bayesian network to obtain the reliability evaluation result.

[0123] As an example, according to the component state values collected at each moment, use the fault classification model trained in step S220 to infer the state of the component. Use the inferred component state as an observation value and perform reliability inference according to formula (13).

[0124] As an example, referring to Table 1, the results of evaluating the reliability of optical network service transmission using the online monitoring data and the dynamic Bayesian by this method are shown. At t = 0, the reliability is 1 and it slowly decreases over time until the fault and repair activities reach a dynamic balance, that is, the steady-state reliability. At t = 4, the fault classification model detects a soft fault in EDFA1, and at this time the service transmission reliability drops from 0.988926 to 0.938995. At t = 9, the fault classification model monitors a soft fault in EDFA2, and at this time the service transmission reliability further drops, from 0.937214 to 0.887502. At t = 11, hard faults in EDFA1 and EDFA2 are diagnosed by the fault classification model, and at this time the service transmission path is unavailable and the reliability drops to 0. At t = 14, EDFA1 and EDFA2 are repaired, raising the reliability to 0.984212. As time goes on, at t = 200, the fault and repair activities of this service transmission path reach a dynamic balance, that is, the reliability reaches the steady state.

[0125] Table 1 Service reliability calculation results

[0126] Time 0 1 2 3 4 Reliability 1 0.99 0.989455 0.988926 0.938995 Time 5 6 7 8 9 Reliability 0.938525 0.938072 0.937635 0.937214 0.887502 Time 10 11 12 13 14 Reliability 0.887132 0 0 0 0.984212 Time 15 16 17 … 200 Reliability 0.983875 0.983553 0.983244 … 0.976869

[0127] As can be seen from the above, the optical network reliability evaluation method provided by the embodiments of the present disclosure includes: determining a transmission unit corresponding to a target service in a target optical network; determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data; determining a state transition relationship of the transmission unit, and constructing a dynamic Bayesian network based on the state transition relationship; determining current operation data of the transmission unit, and processing the current operation data based on the fault classification model to obtain current state information of the transmission unit; processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network. The present disclosure improves the accuracy and interpretability of the evaluation, and can cope with the changing environment and conditions during the operation of the optical network.

[0128] Among them, the optical network can be a core optical network or a backbone optical network. The present invention lies in dynamically inferring the reliability of service transmission with limited monitoring data and prior knowledge. Specifically, according to the online monitoring data and using a fault diagnosis model to infer the states of some easily monitored components, and this part of the state information is used as prior knowledge to be input into a dynamic Bayesian network to infer the reliability of service transmission.

[0129] Among them, using the states of some easily obtained components to quantitatively evaluate the hardware reliability of service transmission, combining online monitoring data and long-term statistical data, users such as operators and researchers can accurately quantitatively evaluate the reliability of optical network service transmission. Specifically, this method comprehensively considers key factors such as the network structure, failure rate and repair rate of optical components, and online monitoring data. Through the quantitative evaluation of the reliability of optical network service transmission, the present invention can provide reasonable reliability suggestions for network service route selection for network maintenance personnel, so as to select a route with higher reliability for data transmission and ensure the long-term stability of the system.

[0130] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0131] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an optical network reliability evaluation device.

[0133] Refer to Figure 6 , which is a schematic structural diagram of an optical network reliability evaluation device provided by an exemplary embodiment of the present disclosure.

[0134] The optical network reliability evaluation device includes the following modules:

[0135] A transmission unit determination module 910, configured to determine a transmission unit corresponding to a target service in a target optical network;

[0136] A fault classification model construction module 920, configured to determine historical operation data of the transmission unit and construct a fault classification model based on the historical operation data;

[0137] A dynamic Bayesian network construction module 930, configured to determine a state transition relationship of the transmission unit and construct a dynamic Bayesian network based on the state transition relationship;

[0138] A status information determination module 940, configured to determine current operation data of the transmission unit, process the current operation data based on the fault classification model, and obtain current status information of the transmission unit;

[0139] A reliability evaluation module 950, configured to process the current status information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network.

[0140] In some exemplary embodiments, the transmission unit determination module 910 is specifically configured to:

[0141] Determine a transmitting unit, a receiving unit, an optical fiber amplifier, and an optical fiber section through which the target service passes when transmitted in the target optical network.

[0142] In some exemplary embodiments, the fault classification model construction module 920 is specifically configured to:

[0143] Determine the bit error rates of the transmitting unit and the receiving unit;

[0144] Determine the input power and output power of the optical fiber amplifier;

[0145] Construct a random forest model, train the random forest model based on the bit error rates, the input power, and the output power, and obtain the fault classification model.

[0146] In some exemplary embodiments, the dynamic Bayesian network construction module 930 is specifically configured to:

[0147] Determine the failure rate and repair rate of the transmission unit;

[0148] Based on the failure rate and the repair rate, determine the state transition relationship;

[0149] Construct the dynamic Bayesian network based on the state transition relationship.

[0150] In some exemplary embodiments, the status information determination module 940 is specifically configured to:

[0151] Based on several decision trees in the fault classification model, respectively predict the current operation data;

[0152] Integrate the prediction results of all the decision trees to obtain the current status information of the transmission unit.

[0153] In some exemplary embodiments, the reliability evaluation module 950 is specifically configured to:

[0154] Take the current status information as an observation value, and perform reliability inference based on the observation value through the dynamic Bayesian network to obtain the reliability evaluation result.

[0155] For the convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0156] The device in the above embodiment is used to implement the corresponding optical network reliability evaluation method in any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here.

[0157] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the optical network reliability evaluation method in any one of the above embodiments.

[0158] Figure 7 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0159] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0160] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0161] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0162] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to achieve communication and interaction between this device and other devices. Among them, the communication module can achieve communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0163] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0164] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, this device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solutions of the embodiments of this specification, and do not necessarily include all the components shown in the figure.

[0165] The electronic device in the above embodiment is used to implement the corresponding optical network reliability evaluation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0166] The memory 1020 stores machine-readable instructions executable by the processor 1010. When the electronic device runs, the processor 1010 communicates with the memory 1020 through the bus 1030, so that the processor 1010 executes the following instructions when running:

[0167] Determine the transmission unit corresponding to the target service in the target optical network;

[0168] Determine the historical operation data of the transmission unit, and construct a fault classification model based on the historical operation data;

[0169] Determine the state transition relationship of the transmission unit, and construct a dynamic Bayesian network based on the state transition relationship;

[0170] Determine the current operation data of the transmission unit, process the current operation data based on the fault classification model, and obtain the current state information of the transmission unit;

[0171] Process the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network.

[0172] In a possible implementation manner, in the instructions executed by the processor 1010, the determining the transmission unit corresponding to the target service in the target optical network includes:

[0173] Determine the transmitting unit, receiving unit, optical fiber amplifier, and optical fiber section through which the target service passes when transmitted in the target optical network.

[0174] In a possible implementation manner, in the instructions executed by the processor 1010, the determining the historical operation data of the transmission unit and constructing a fault classification model based on the historical operation data includes:

[0175] Determine the bit error rates of the transmitting unit and the receiving unit;

[0176] Determine the input power and output power of the optical fiber amplifier;

[0177] Construct a random forest model, and train the random forest model based on the bit error rate, the input power, and the output power to obtain the fault classification model.

[0178] In a possible implementation manner, in the instructions executed by the processor 1010, the determining the state transition relationship of the transmission unit and constructing a dynamic Bayesian network based on the state transition relationship includes:

[0179] Determine the failure rate and repair rate of the transmission unit;

[0180] Based on the failure rate and the repair rate, determine the state transition relationship;

[0181] Construct the dynamic Bayesian network based on the state transition relationship.

[0182] In a possible implementation manner, among the instructions executed by the processor 1010, determining the current operation data of the transmission unit, and processing the current operation data based on the fault classification model to obtain the current state information of the transmission unit includes:

[0183] Respectively predicting the current operation data based on a plurality of decision trees in the fault classification model;

[0184] Integrating the prediction results of all the decision trees to obtain the current state information of the transmission unit.

[0185] In a possible implementation manner, among the instructions executed by the processor 1010, processing the current state information based on the dynamic Bayesian network to obtain the reliability evaluation result of the target optical network includes:

[0186] Taking the current state information as an observation value, and performing reliability inference based on the observation value through the dynamic Bayesian network to obtain the reliability evaluation result.

[0187] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the optical network reliability evaluation method described in any of the above embodiments.

[0188] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0189] The above non-transitory computer-readable storage medium can be any available medium or data storage device accessible by a computer, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drives (SSD)), etc.

[0190] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the optical network reliability evaluation method described in any one of the above exemplary method embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0191] Based on the same inventive concept, corresponding to the optical network reliability evaluation method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of the computer to cause the computer and / or the processor to execute the optical network reliability evaluation method. Corresponding to the execution subjects corresponding to the steps in the various embodiments of the optical network reliability evaluation method, the processor that executes the corresponding steps can belong to the corresponding execution subject.

[0192] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the optical network reliability evaluation method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0193] Those skilled in the art know that the embodiments of the present disclosure can be implemented as a system, method, or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as "circuit", "module", or "system". In addition, in some embodiments, the present disclosure can also be implemented in the form of a computer program product in one or more computer-readable media, which contain computer-readable program codes.

[0194] Any combination of one or more computer-readable media may be employed. The computer-readable media may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program which can be used by or in connection with an instruction execution system, apparatus, or device.

[0195] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium that is not a computer-readable storage medium and that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0196] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0197] The computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0198] It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, when executed by the computer or other programmable data processing apparatus, create means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0199] These computer program instructions can also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0200] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer or other programmable apparatus provide processes for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0201] In addition, although the operations of the methods of the present disclosure are depicted in the figures in a particular order, this is not required or implied to perform the operations in that particular order, or to perform all of the illustrated operations to achieve the desired result. Instead, the steps depicted in the flowchart may be performed in a different order. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step and performed, and / or one step may be decomposed into multiple steps and performed.

[0202] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion thereof that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the figures. For example, two blocks shown in succession may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams or flowcharts, and combinations of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0203] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0204] Those of ordinary skill in the art should understand that: The discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; Under the concept of the present application, the technical features between the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0205] In addition, for simplicity of description and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the device may be shown in block diagram form to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of such block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0206] Although the present application has been described in conjunction with specific embodiments of the present application, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) can be used with the embodiments discussed.

[0207] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

[0208] Although the spirit and principles of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the specific embodiments disclosed, and the division of various aspects does not mean that the features in these aspects cannot be combined for benefits. Such division is only for convenience of expression. The present disclosure aims to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the appended claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. A method for evaluating the reliability of an optical network, characterized in that, including: determining a transmission unit corresponding to a target service in a target optical network; determining historical operation data of the transmission unit, and constructing a fault classification model based on the historical operation data; determining a state transition relationship of the transmission unit, and constructing a dynamic Bayesian network based on the state transition relationship; determining current operation data of the transmission unit, and processing the current operation data based on the fault classification model to obtain current state information of the transmission unit; processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network.

2. The method according to claim 1, characterized in that, The determining a transmission unit corresponding to a target service in a target optical network includes: determining a transmitting unit, a receiving unit, an optical fiber amplifier, and an optical fiber section through which the target service passes when transmitted in the target optical network.

3. The method according to claim 2, characterized in that, The determining historical operation data of the transmission unit and constructing a fault classification model based on the historical operation data includes: determining bit error rates of the transmitting unit and the receiving unit; determining input power and output power of the optical fiber amplifier; constructing a random forest model, and training the random forest model based on the bit error rates, the input power, and the output power to obtain the fault classification model.

4. The method according to claim 1, characterized in that, The determining a state transition relationship of the transmission unit and constructing a dynamic Bayesian network based on the state transition relationship includes: determining a failure rate and a repair rate of the transmission unit; determining the state transition relationship based on the failure rate and the repair rate; constructing the dynamic Bayesian network based on the state transition relationship.

5. The method according to claim 3, characterized in that, The determining current operation data of the transmission unit and processing the current operation data based on the fault classification model to obtain current state information of the transmission unit includes: respectively predicting the current operation data based on a plurality of decision trees in the fault classification model; integrating prediction results of all the decision trees to obtain current state information of the transmission unit.

6. The method according to claim 4, wherein The processing the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network includes: using the current state information as an observation value, and performing reliability inference based on the observation value through the dynamic Bayesian network to obtain the reliability evaluation result.

7. An optical network reliability evaluation device, characterized in that, including: a transmission unit determination module configured to determine a transmission unit corresponding to a target service in a target optical network; a fault classification model construction module configured to determine historical operation data of the transmission unit and construct a fault classification model based on the historical operation data; a dynamic Bayesian network construction module configured to determine a state transition relationship of the transmission unit and construct a dynamic Bayesian network based on the state transition relationship; a state information determination module configured to determine current operation data of the transmission unit and process the current operation data based on the fault classification model to obtain current state information of the transmission unit; a reliability evaluation module configured to process the current state information based on the dynamic Bayesian network to obtain a reliability evaluation result of the target optical network.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer program instructions which, when run on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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