Data processing method and related equipment based on optical network

Abstract

The present disclosure provides a data processing method based on an optical network and related equipment. The method comprises: obtaining optical network data of the optical network and power network data associated with the optical network; determining the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level; and determining the instantaneous reliability and / or cumulative reliability of the optical network based on the current state.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a data processing method based on an optical network and related equipment. Background Art

[0002] Current reliability assessments ignore the time dependency and functional dependency of the system. The time dependency of the system is reflected in changes in hardware and software configurations, performance changes caused by aging of optical network components, changes in maintenance policies, and the impact of programmable systems. Over time, link configurations may deviate from the optimal state, the so-called "configure and forget". Different failure modes and dynamic changes in optical networks limit the accuracy of optical network reliability assessments. All of these lead to low accuracy in optical network reliability assessments, which is not conducive to maintaining the stability of optical networks. Summary of the invention

[0003] The present disclosure proposes a data processing method based on an optical network and related equipment to solve the technical problem of low accuracy of reliability evaluation of the optical network to a certain extent.

[0004] In a first aspect, the present disclosure provides a data processing method based on an optical network, comprising:

[0005] Acquire optical network data of the optical network and power network data associated with the optical network;

[0006] Determine the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level;

[0007] The instantaneous reliability and / or cumulative reliability of the optical network is determined based on the current state; wherein the instantaneous reliability is used to characterize the probability of connectivity of all node pairs in the optical network, and the cumulative reliability is used to characterize the proportion of the sum of the reliable operating time of all components in the optical network to the total operating time.

[0008] In a second aspect of the present disclosure, there is provided a data processing device based on an optical network, comprising:

[0009] An acquisition module, used for acquiring optical network data of the optical network and power network data associated with the optical network;

[0010] A state determination module, configured to determine the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level;

[0011] A reliability determination module is used to determine the instantaneous reliability and cumulative reliability of the optical network based on the current state; wherein the instantaneous reliability is used to characterize the probability of connectivity of all node pairs in the optical network, and the cumulative reliability is used to characterize the proportion of the sum of the reliable operating time of all components in the optical network to the total operating time.

[0012] In a third aspect of the present disclosure, an electronic device is provided, comprising one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the programs include instructions for executing the method described in the first aspect.

[0013] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium containing a computer program is provided. When the computer program is executed by one or more processors, the processors execute the method described in the first aspect.

[0014] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0015] From the above, it can be seen that the data processing method and related equipment based on the optical network provided by the present disclosure model the optical network and the power network into a complex system composed of multi-level intelligent agent models, aiming to dynamically represent the working status of the optical network and the power network components while considering the dependency between the two networks. Each agent model simulates different failure modes of the optical network in the form of probability distribution. Finally, the instantaneous reliability and cumulative reliability of the optical network are calculated by time simulation. It can guide the network reliability design in the network design stage, especially the design of the reliable coupling relationship between the optical network and the power network. In the network operation stage, this method helps to timely identify the components with low reliability in the optical network, and by taking measures such as protection and maintenance, it effectively reduces the possibility of risks in the optical network and reduces the unavailable time, thereby ensuring the reliable and smooth operation of the optical network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of an optical network-based data processing architecture according to an embodiment of the present disclosure.

[0018] Figure 2 The figure is a schematic diagram of the hardware structure of an exemplary electronic device according to an embodiment of the present disclosure.

[0019] Figure 3 The present invention is a flowchart of a data processing method based on an optical network according to an embodiment of the present invention.

[0020] Figure 4a-4c It is a schematic diagram of the structure of the optical network and the power network of the embodiment of the present disclosure.

[0021] Figure 5 This is a state transition diagram of the optical network and power network coupling network agent model of the disclosed embodiment of the present disclosure.

[0022] Figure 6 It is a schematic diagram of a data processing device based on an optical network according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words 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. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure 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.

[0026] For example, in response 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 to be performed 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, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0027] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0028] Figure 1 A schematic diagram of an optical network-based data processing architecture according to an embodiment of the present disclosure is shown. Figure 1 , the optical network-based data processing architecture 100 may include a server 110, a terminal 120, and a network 130 providing a communication link. The server 110 and the terminal 120 may be connected via a wired or wireless network 130. The server 110 may be an independent physical server, or a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and CDN.

[0029] The terminal 120 may be implemented in hardware or software. For example, when the terminal 120 is implemented in hardware, it may be various electronic devices having a display screen and supporting page display, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. When the terminal 120 device is implemented in software, it may be installed in the electronic devices listed above; it may be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0030] It should be noted that the optical network-based data processing method provided in the embodiment of the present application can be executed by the terminal 120 or by the server 110. It should be understood that Figure 1 The number of terminals, networks and servers in the embodiment is only for illustration and is not intended to limit the number of terminals, networks and servers. Any number of terminals, networks and servers may be provided as required.

[0031] Figure 2 FIG. 2 shows a schematic diagram of the hardware structure of an exemplary electronic device 200 provided in an embodiment of the present disclosure. Figure 2 As shown, the electronic device 200 may include: a processor 202, a memory 204, a network module 206, a peripheral interface 208 and a bus 210. The processor 202, the memory 204, the network module 206 and the peripheral interface 208 are connected to each other in communication within the electronic device 200 through the bus 210.

[0032] Processor 202 may be a central processing unit (CPU), an optical network-based data processor, a neural network processor (NPU), a microcontroller (MCU), a programmable logic device, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or one or more integrated circuits. Processor 202 may be used to perform functions related to the technology described in this disclosure. In some embodiments, processor 202 may also include multiple processors integrated into a single logical component. For example, Figure 2 As shown, processor 202 may include a plurality of processors 202a, 202b, and 202c.

[0033] The memory 204 may be configured to store data (eg, instructions, computer code, etc.). Figure 2 As shown, the data stored in the memory 204 may include program instructions (for example, program instructions for implementing the data processing method based on the optical network of the embodiment of the present disclosure) and data to be processed (for example, the memory may store configuration files of other modules, etc.). The processor 202 may also access the program instructions and data stored in the memory 204, and execute the program instructions to operate on the data to be processed. The memory 204 may include a volatile storage device or a non-volatile storage device. In some embodiments, the memory 204 may include a random access memory (RAM), a read-only memory (ROM), an optical disk, a magnetic disk, a hard disk, a solid-state drive (SSD), a flash memory, a memory stick, etc.

[0034] The network module 206 can be configured to provide communication with other external devices to the electronic device 200 via a network. The network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, near field communication (NFC) etc.), a cellular network, the Internet or a combination thereof. It is understood that the type of network is not limited to the above specific examples. In some embodiments, the network module 306 can include any number of network interface controllers (NICs), radio frequency modules, transceivers, modems, routers, gateways, adapters, cellular network chips, etc., in any combination.

[0035] The peripheral interface 208 can be configured to connect the electronic device 200 to one or more peripheral devices to achieve information input and output. For example, the peripheral devices can include input devices such as a keyboard, a mouse, a touch pad, a touch screen, a microphone, and various sensors, and output devices such as a display, a speaker, a vibrator, and an indicator light.

[0036] The bus 210 can be configured to transmit information between various components of the electronic device 200 (e.g., the processor 202, the memory 204, the network module 206, and the peripheral interface 208), such as an internal bus (e.g., a processor-memory bus), an external bus (USB port, PCI-E bus), etc.

[0037] It should be noted that, although the architecture of the electronic device 200 only shows the processor 202, the memory 204, the network module 206, the peripheral interface 208 and the bus 210, in the specific implementation process, the architecture of the electronic device 200 may also include other components necessary for normal execution. In addition, it can be understood by those skilled in the art that the architecture of the electronic device 200 may also only include the components necessary for implementing the embodiments of the present disclosure, and does not necessarily include all the components shown in the figure.

[0038] In recent years, thanks to the multi-faceted development of optical transmission technology, such as multiplexing technology, fiber technology, optical amplifier technology, etc., the channel capacity of optical networks has achieved exponential growth. Today, optical networks carry 99% of the global Internet capacity. However, when a fault occurs, thousands of connections may be interrupted and a large amount of data (up to thousands of gigabits) may be lost. For the fault of optical network components, locating it usually takes a lot of time, and repairing and replacing the optical network is time-consuming and expensive. Excessive protection, fault recovery and static network control greatly limit the further development of optical network capacity. When the reliability requirement is lower than the acceptable level, network maintenance personnel need to take appropriate measures to protect or maintain the optical network in time to reduce the time the network is unavailable. At present, some methods have been adopted for the reliable operation of optical networks, such as ensuring the transmission quality and reliability of the optical path by reserving a higher margin. However, the reserved protection resources will reduce the utilization rate of the optical network bandwidth. Therefore, accurate reliability assessment of optical networks is crucial. At present, common optical network reliability assessment models are mainly calculated based on statistical probability models, such as fault trees, Bayesian networks, event trees, Markov processes, reliability block diagrams, and Petri nets. However, these methods have some limitations. Reliability block diagrams and fault trees are limited by binary variables, and it is difficult to accurately describe the reliability degradation of optical network components due to aging or environmental changes. At the same time, it is challenging to use these models to represent the dependencies between different optical network components or factors. However, Markov processes and Petri nets have the problem of state space explosion, which limits their application in large-scale optical networks. Given the dynamics and polymorphism of optical networks, there is an urgent need for an optical network reliability assessment method that takes actual scenarios into account.

[0039] In view of this, the embodiments of the present disclosure provide a data processing method based on an optical network and related equipment. The optical network and the power network are modeled as a complex system composed of multi-level intelligent agent models, aiming to dynamically represent the working status of the optical network and the power network components while considering the dependency between the two networks. Each agent model simulates different failure modes of the optical network in the form of probability distribution. Finally, the instantaneous reliability and cumulative reliability of the optical network are calculated through time simulation. It can guide the network reliability design in the network design stage, especially the design of the reliable coupling relationship between the optical network and the power network. In the network operation stage, this method helps to timely identify the components with low reliability in the optical network, and by taking measures such as protection and maintenance, it effectively reduces the possibility of risks in the optical network and reduces the unavailable time, thereby ensuring the reliable and smooth operation of the optical network.

[0040] Because the existing reliability assessment ignores the time dependency and functional dependency of the system. The time dependency of the system is reflected in the changes in hardware and software configuration, performance changes caused by aging of optical network components, changes in maintenance policies, and the impact of programmable systems. Over time, the link configuration may deviate from the optimal state, which is the so-called "configure and forget". The different failure modes and dynamic changes of optical networks limit the accuracy of optical network reliability assessment. In addition, the functional dependency of the system is reflected in the mutual dependence between the optical network and the power grid. However, the current methods for optical network reliability assessment do not consider the dependency between the optical network and the power grid. There are many active optical devices in the optical network, and the load of the power grid is required to provide power support for the active devices of the optical network, while the power grid relies on the optical network to transmit control signals to manage various loads and generators. Therefore, optical network failures may cause power network failures, and vice versa. Considering the mutual influence between the optical network and the power network is crucial for a comprehensive assessment of the reliability of the optical network.

[0041] See also Figure 3 , Figure 3 The schematic flow chart of the data processing method based on the optical network according to the embodiment of the present disclosure is shown. The data processing method based on the optical network according to the embodiment of the present disclosure can be deployed on the server side. Figure 3 In the data processing method 300 based on the optical network, the data processing method 300 may further include the following steps.

[0042] In step S310, optical network data of the optical network and power network data associated with the optical network are acquired.

[0043] The optical network data may include: the number of optical cross-connects (OXCs), EDFAs (Erbium-Doped Fiber Amplifiers) and optical fibers, and the connection relationship between each component. The power network data may include: the number of generators, loads, and power lines, and the connection relationship between each component. In addition, there is also the connection relationship between the optical network and the power grid. Specifically, G A (V A , E A ) represents an optical network, where V A represents the set of light nodes, {v|v∈V A}; E A represents the optical link set, {(v x , v y )|(v x , v y )∈E A , v x ≠v y , v x ∈V A , v y ∈V A},|E A | is the number of links; G P (V P , E P ) represents the power network, where V P represents the set of power network nodes, {v|v∈V P}; E P represents the set of power network links, {(v n , v m )|(v n , v m )∈E P , v n ≠v m , v n ∈V P , v m ∈V P},|E P | is the number of links; the topology between the optical network and the power network is represented by G C (V C , E C ), V C =V A ∪V P , use E C represents the set of power transmission lines and communication lines between the power grid and the optical network. The power transmission line between the optical network and the power grid can be expressed as {(v x , v n )|(v x , vn )∈E C , v n ∈V P , v x ∈V A}, the power transmission line between the optical network and the power grid can be expressed as {(v n , v x )|(v n , v x )∈E C , v n ∈V P , v x ∈V A}; The coupling network of the power network and the optical network can be expressed as G = G A ∪G P ∪G C .

[0044] In step S320, the current state of each level in the multi-level model based on the coupling of the optical network and the power network is determined based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level.

[0045] Specifically, the multi-level model based on optical network-power network coupling is shown in Table 1.

[0046]

[0047] Table 1

[0048] In this hierarchical structure, each level of agent model is only responsible for controlling the next level of agent model and collecting the reliability data of the next level of agent model. The task management agent model specifies the tasks of the second-level agent. The optical network agent model is responsible for collecting the reliability information of two types of three-level agent models, namely optical node (OXC) and optical line system (OLS). The optical line system (OLS) agent model is responsible for collecting the reliability information of two types of four-level agent models, namely optical fiber segment and EDFA. The power network agent model is responsible for collecting the reliability information of two types of three-level agent models, namely power supply line and power node. The power node agent model is responsible for collecting the reliability information of two types of four-level agents, namely generator and load. As a second-level agent model, the SDN (Software Defined Network) control agent model can directly control the optical network node and send configuration or control information to it. The connection line between the optical network and the power network completes the function of power supply or signaling transmission. Since the network range is large, each component will be configured with an environmental agent model to describe the environmental status of the component.

[0049] The failure mode, repair mode, control mode and environment of each agent model can be modeled using the probability distribution method. Components are an important part of optical networks and power networks. They include optical network component agent models (OXC, fiber segments, EDFA), power network component agent models (generators, loads, transmission lines), and control lines and power supply lines between optical networks and power networks. These component agent models are abstractions of actual physical devices and are used to simulate various working states of components. For example, normal operation and various fault states, repair states. For multi-state optoelectronic interconnection networks, their reliability is related to the component states. Assuming that the optical network consists of M components, then for any component c i The state space S of (1≤i≤M) i It can be expressed as formula (1). The state space can be divided into four types of states: initial state (IS i ), degenerate state Failure state and repair status (RS i ). The state transition relationship of a component during its service cycle is: The optical network can work normally in both the initial state and the degraded state. Then for the state space S of all components of the optical network at time t net (t) can be expressed as formula (2). Assuming that the power network consists of W components, then for any component state space S i ' can be expressed as formula (3). Where n' represents the number of degraded states of power network elements, and m' represents the number of failed states of power network elements. The state space of all elements in the power network at time t can be expressed as formula (4). Specifically, it includes:

[0050]

[0051]

[0052]

[0053]

[0054] Specifically, the current state of the previous level can be determined based on the current state of the next level in the multi-level model. This includes:

[0055] 1) Component Agent Service Cycle Model

[0056] use Indicates element c i The service life of a component refers to the time from normal operation to aging failure when no other failures occur. TTF stands for the mean time between failures (MTBF) of a component. Indicates element c i By Status Transfer to state The time taken. x represents the normal working state, and y represents the fault state. It is determined by using the continuous random variable sampling method, through which a random sample that conforms to the original distribution can be sampled. Obey a specific distribution function, such as formula (3), then according to the continuous variable random sampling i th It can be expressed as formula (4).

[0057] M=F(g) (5)

[0058]

[0059] Component service life If it obeys exponential distribution, then its probability density function can be expressed as (7). Its corresponding cumulative distribution function (CFD) is as shown in formula (8). Finally, the sampling formula of the component obeying exponential distribution can be expressed as formula (9). Where v is a random number in the range [0,1], Indicates element c i The probability of transitioning from an available state to a faulty state.

[0060] f(g)=λe -λg (7)

[0061] F(g)=1-e -λg (8)

[0062]

[0063] 2) SDN Agent Model

[0064] The SDN proxy model is used for operations such as optical network management, control, and data collection. Parameter configuration errors are generally caused by human configuration errors. The occurrence time of human configuration errors is generally established as a normal logarithmic distribution. represents the time when the fault occurs, and based on the above sampling method of continuous random variables, formulas (10)-(12) are proposed. These formulas correspond to the probability density function, CDF and sampling formula of the logarithmic normal distribution function. In these formulas, μ represents the average error time, σ represents the root mean square of the error time of a person, erf is the error function, and y is a random number in the range of [0,1].

[0065]

[0066]

[0067]

[0068] 3) Random failure model

[0069] The occurrence of random events, such as fiber cutting and component failure, will directly lead to the unavailability of optical network components. In this study, the occurrence of random events is modeled as obeying an exponential distribution. According to formulas (5) to (8), the component c i Random failure time The method of sampling continuous random variables can be expressed as formula (13). Here, v is a random number in the range [0,1]. Indicates element c i The probability of transitioning from an available state to a random failure state.

[0070]

[0071] 4) Repair the activity model

[0072] Repair activities can transform a component from a faulty state to a normal state. i Time required for repair activities TTR i It follows a log-normal distribution. TTR i represents the mean repair time (MTTR) of the component. According to formula (10) to formula (11), the continuous random variable sampling of the repair time of the component can obtain formula (14). Among them, μ represents the mean repair time, and σ represents the root mean square of the repair time.

[0073]

[0074] 5) Environmental Agent Model

[0075] The state of the environmental proxy model can be divided into three categories: normal environment, unsuitable environment and harsh environment. In a normal environment, all components can work normally. In an unsuitable environment (such as unsuitable temperature and fiber stress), the components will accelerate the aging process. In a harsh environment, such as extreme weather or natural disasters, components will fail. The Gumbel distribution can be used to describe its changes. The determination of the state of the environmental proxy model adopts the probability percentile method. Specifically, for an unsuitable environment, the Gumbel sampling value x can be set 1 At the corresponding probability percentile 99.9%. 2When it is greater than the probability percentile 99.99%, it can be regarded as an extreme event. The probability density function and cumulative distribution function of the Gumbel distribution are given by formulas (15) and (16), where μ is the location parameter, indicating the center of the Gumbel distribution, and β is the scale coefficient of the distribution curve. The sampling formula of the state of the environmental agent model is obtained based on the sampling method of continuous random variables, as shown in formula (17). Here, v is a random number with a value range of [0,1].

[0076]

[0077]

[0078] g=F -1 (M) = μ-β ln (-ln(v)),v∈[0,1] (17)

[0079] Can be used To represent the component c i The environmental proxy model state is {-1, 0, 1}. Specifically, 0 represents a normal state, -1 represents an unsuitable environment, and 1 represents an extreme event. This value selection method can be expressed by formula (18).

[0080]

[0081] 6) Fault propagation model

[0082] The impact of cascading failures in the optical network-power network coupling network on reliability can be considered. i =P 0 , …P j For optical network node A i The collection of power network nodes that provide power supply, |J i | represents the number of elements in the set. j A binary variable indicating the failure of a power network node, with a value of 1 for power supply and 0 for no power supply. i = {l 0 ,…l j} represents the set of power supply lines between the power grid nodes and the optical network nodes. j A value of 1 indicates that the line is normal, and a value of 0 indicates that the line is faulty. i Represents optical network node A i The number of connections to the power network nodes is proportional to the size of |J i | and |L i | are equal. For optical network node A i , only when all the power network loads it supplies fail or the power supply line fails, the optical network node Ai Cascading failure will occur. For any optical network node A i , the fault propagation model can be expressed as formula (19).

[0083]

[0084] For the power network, its control signal depends on the optical network. i =A 0 , …A j For the power network node P i The collection of optical network nodes that perform control, |C i | represents the number of elements in a set. j It is a binary variable indicating the failure of optical network nodes, with a value of 1 indicating monitoring and control, and 0 indicating no monitoring and control. i ={y 0 ,…y j} represents the set of signal lines between optical network nodes and power grid nodes. j A value of 1 indicates that the line is normal, and a value of 0 indicates that the line is faulty. i Represents the power network node P i The connection sequence number with the optical network node is the same as |C i | and |Y i | are equal. For the load node P of the power network i For any power network node P, only when all the interconnected optical network nodes or control lines fail, the power network load will experience a cascading failure. i ,The impact of the optical network fault propagating to the power network can be expressed as formula (20).

[0085]

[0086] 6) Proxy model operation mechanism

[0087] The mechanism of using the simulation clock, fault clock and repair clock in parallel can be run, that is, the fault clock and repair clock will not interfere with the simulation clock. In addition, a dynamic consumption mechanism is used for the fault mode and repair mode of the component. The basic principle of the dynamic consumption mechanism is shown in formula (21). Among them, represents the consumption function of failure time, t represents the failure time or repair time, They represent the consumption rate of the faulty clock, for Any one of and are inversely proportional to each other. This means that when When the fault clock consumption is completed, a fault occurs. In addition, in cases involving human operating errors or inappropriate environments that accelerate such events, a degradation factor c is defined. k ∈[0, 1]. The failure consumption rate changes accordingly as shown in formula (22). The failure time consumption rate of a component is determined by the number of degradation events n and The base value fluctuates. The repair mechanism of the component can be expressed as formula (23) based on dynamic consumption. i (t) represents the consumption function of the repair time, v i Indicates the rate at which the repair time is consumed, which is equal to 1 / TTR i .

[0088]

[0089]

[0090] E i (t) = 1-v i t=1-t / TTR i (twenty three)

[0091] 6) Agent model interaction model

[0092] The optical network and power grid proxy models are responsible for collecting the reliability data of the optical network and power grid in real time and performing reliability analysis. The information sent by the optical network element proxy model to the optical network proxy model can be described as formula (24), where t represents the current time, Indicates element c i The current state.

[0093]

[0094] For optical networks, the EDFA agent model and fiber segment agent model in the fourth-level agent model determine the status of the corresponding third-level optical line system agent model. i The EDFA proxy model set and the fiber segment proxy model set involved in the process are denoted as E i and O i Therefore, at time t, OLS i The state can be expressed by formula (25).

[0095]

[0096] The OXC agent model and the optical line system agent model pass the current state to the optical network agent model through formula (24). In the secondary and primary agent models, the reliability information of the components is stored in the form of a state matrix. According to formula (1)-formula (6), the state storage matrix S of the optical network agent model and the power network agent model at time t isONA and S PGA As shown in formulas (26)-(27). The state storage matrix is ​​a matrix in which the optical network agent model and the power network agent model collect and store the state information of the next-level agent model. Based on the current state of the components, the optical network agent model and the power network agent model calculate the instantaneous reliability and the cumulative reliability respectively. Finally, the optical network agent model and the power network agent model send the current state of all components to the TM agent model to calculate the reliability of the entire network.

[0097]

[0098]

[0099] Among them, PGN can refer to the state of the node agent in the power network, and PGL is the state of the line agent in the power network. Similar to the optical network, its state is determined by the service cycle of the component, the time of fault occurrence and the time of repair. Its state space is also composed of the initial state, the degraded state, the failed state and the repair state. Specifically, the occurrence time of each fault type can be determined according to the method of time series and continuous random variable sampling. When the time arrives, the fault or repair event is triggered and the state transition is performed.

[0100] In step S330, the instantaneous reliability and the cumulative reliability of the optical network are determined based on the current state.

[0101] Among them, availability is divided into instantaneous reliability and cumulative reliability. Instantaneous reliability represents the reliability of the network at the current moment T. Cumulative reliability represents the reliability of the network in the time [0, T]. The calculation indicator of instantaneous reliability is full terminal reliability, which can refer to the probability of connection on all node pairs in the network. The terminal reliability between two nodes is defined as 1 here if there is a path between them. If there is no connection path, it is 0. If the network is fully connected, the value of full terminal reliability is 1. Otherwise, it is necessary to add the number of node pairs in each connected component and divide it by the total number of node pairs in the network. Instantaneous reliability of the network at time T The calculation method of is as shown in formula (28). Where V A represents the set of optical network nodes, |V A | represents the number of optical network nodes. When T time v x , v y When there is a connecting line between x v y =1. Represents the number of connections between any two points in the network.

[0102]

[0103] The calculation index of cumulative reliability is the ratio of the sum of the reliability operation time of all components to the total time. The cumulative reliability of the network at [0, T] is The calculation method is as shown in formula (29). Where N represents the number of network components, represents the available time of component j, and NT represents the sum of the total running time of all components.

[0104]

[0105] Specifically, see Figure 4a-4c , Figure 4a-4c The structure diagram of the optical network and the power network according to the embodiment of the present disclosure is shown. Figure 4a and Figure 4c Optical network, power network topology, and Figure 4b The coupled structure between the two networks shown is taken as an example for reliability evaluation. Figure 5 The state transition diagram of the optical network and power network coupling network agent model of the embodiment of the present disclosure is shown. The agent model in the figure corresponds to the agent model in Table 1.

[0106] Each proxy model transforms between states according to the timing and distribution function. There are four typical states for components, namely normal working state, degraded state, failure state and repair state. There are three typical states for environmental proxy models, namely normal state, degraded state and extreme state. First, the behavior of the five-level proxy model proposed in the present invention is modeled according to formulas (1) to (18), specifically including failure time, repair time, aging time, coupled fault events, human operation errors, degraded environment and extreme environment events. The failure time, repair time and probability of occurrence of extreme events of each component are sampled by random sampling of continuous variables. The dependency between the optical network and the power network at each moment is determined according to formulas (19) and (20). Finally, the various activities of the intelligent proxy model are simulated according to formulas (21) to (27). The parameters required for modeling are shown in Tables 2 and 3.

[0107]

[0108]

[0109] Table 2

[0110]

[0111] Table 3

[0112] According to the agent modeling and agent model behavior modeling mentioned above, through timing simulation, the instantaneous reliability and cumulative reliability of the optical network at each moment are calculated according to formulas (28) and (29). The evaluation results are shown in Table 4.

[0113]

[0114] Table 4

[0115] It can be seen that according to the method of the embodiment of the present disclosure, the optical network and the power network are modeled into a complex system composed of multi-level intelligent agent models, which aims to dynamically represent the working status of the optical network and the power network elements while considering the dependency between the two networks. Each agent model simulates different failure modes of the optical network in the form of probability distribution. Finally, the instantaneous reliability and cumulative reliability of the optical network are calculated through time simulation. It can guide the network reliability design in the network design stage, especially the design of the reliable coupling relationship between the optical network and the power network. In the network operation stage, this method helps to timely identify the components with low reliability in the optical network, and by taking measures such as protection and maintenance, it effectively reduces the possibility of risks in the optical network and reduces the unavailable time, thereby ensuring the reliable and smooth operation of the optical network.

[0116] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0117] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides a data processing device based on an optical network, see Figure 6 , the optical network-based data processing device comprises:

[0119] An acquisition module, used for acquiring optical network data of the optical network and power network data associated with the optical network;

[0120] A state determination module, configured to determine the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level;

[0121] A reliability determination module is used to determine the instantaneous reliability and cumulative reliability of the optical network based on the current state; wherein the instantaneous reliability is used to characterize the probability of all nodes in the optical network being connected, and the cumulative reliability is used to characterize the proportion of the sum of the reliable operation time of all components in the optical network to the total operation time. For the convenience of description, the above device is described separately according to the functions of various modules. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0122] The device of the above embodiment is used to implement the corresponding optical network-based data processing method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0123] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the optical network-based data processing method as described in any of the above embodiments.

[0124] 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. Information can be computer-readable instructions, data structures, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0125] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the optical network-based data processing method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0126] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0127] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0128] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0129] The embodiments of the present disclosure 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 disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A data processing method based on an optical network, comprising: Acquire optical network data of the optical network and power network data associated with the optical network; Determine the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level; Determine the instantaneous reliability and / or cumulative reliability of the optical network based on the current state; wherein the instantaneous reliability is used to characterize the probability of all nodes in the optical network being connected, and the cumulative reliability is used to characterize the proportion of the sum of the reliable operation time of all components in the optical network to the total operation time; The multi-level model includes a first level and a second level, the second level is a level below the first level; the first level includes a task management agent model; the second level includes at least one of a power network agent model, an optical network agent model, an SDN control agent model, a power grid power line agent model, and an optical network control line agent model; The multi-level model further includes a third level, which is a level below the second level; the third level includes at least one of a power transmission line proxy model, a power grid node proxy model, an optical node proxy model, and an optical line system proxy model; the power transmission line proxy model and the power grid node proxy model are a level below the power network proxy model, and the optical node proxy model and the optical line system proxy model are a level below the optical network proxy model; The multi-level model further includes a fourth level, which is a level below the third level; the fourth level includes a generator proxy model, a load proxy model, and at least one of an EDFA proxy model and an optical fiber segment proxy model; the generator proxy model and the load proxy model are a level below the power grid node proxy model, and the EDFA proxy model and the optical fiber segment proxy model are a level below the optical line system proxy model; The multi-level model further includes a fifth level, which is a level below the fourth level; the fifth level includes an environmental proxy model of the element.

2. The method according to claim 1, wherein: Determining a current state of each level in a multi-level model of the optical network and the power network based on the optical network data and the power network data, comprising: For the state space of all components in the optical network at time t Where M is the number of components in the optical network, IS i represents the initial state, Indicates a degraded state. Indicates failure status and RS i represents the repair state; n represents the number of degradation states of optical network elements, and m represents the number of failure states of optical network elements; for the state space of all elements in the power network at time t W is the number of components in the power network, n' represents the number of degradation states of power network elements, m' represents the number of failure states of power network elements; Component c i Service cycle Indicates element c i The probability of transitioning from the available state to the fault state, v is a random number in the range of [0,1]; The SDN control agent model is based on the time when the failure caused by misoperation occurs μ represents the average error time, σ represents the root mean square of the error time, erf is the error function, and y is a random number in the range of [0,1]; Component c i Random failure time Indicates element c i The probability of transitioning from the available state to the random fault state, v is a random number in the range [0,1]; Component c i Mean time to repair μ represents the mean repair time, σ represents the root mean square of the repair time, and v is a random number in the range of [0,1].

3. The method according to claim 2, wherein: The current state of the environment agent includes: in, For component c i The environmental proxy state, 0 represents the normal state, -1 represents an unsuitable environment, 1 represents an extreme event, and x1 and x2 are preset values.

4. The method according to claim 3, wherein: Determining the current state of each level in the multi-level model of the optical network and the power network based on the optical network data and the power network data, further comprising: For optical network nodes, the impact of power network faults propagating to the optical network includes: Among them, J i =P0,…P j represents a first set of power network nodes that supply power to the optical network nodes, |J i | represents the number of elements in the first set, P j A binary variable indicating the failure of a power network node, with a value of 1 for power supply and 0 for no power supply; L i = {l0, ...l j } represents the second set of power supply lines between the grid node and the optical network node, |L i | represents the number of elements in the second set; l j A value of 1 indicates that the line is normal, and 0 indicates that the line is faulty; AC i Indicates the number of connections between optical network nodes and power network nodes; For power network nodes, the impact of optical network failure propagating to the power network includes: Among them, C i =A0,…A j represents the third set of optical network nodes that control the power network nodes, |C i | represents the number of elements in the third set; A j A binary variable indicating the failure of an optical network node, with a value of 1 for monitoring and control and 0 for no monitoring and control; Y i ={y0, ...y j } represents the fourth set of signal lines between the optical network node and the power grid node, |Y i | represents the number of elements in the fourth set; y j A value of 1 indicates that the line is normal, and 0 indicates that the line is faulty; PC i Represents the power network node P i The number of connections to optical network nodes.

5. The method according to claim 4, further comprising: Failure time consumption function represents the consumption rate of failure time, and The two are reciprocal; for Any one of; Repair time consumption function E i (t) = 1-v i t=1-t / TTR i , v i represents the rate of consumption of repair time, v i The size is equal to 1 / TTR i ; The power network agent model collects the storage form of the next-level agent state as follows: The optical network proxy model collects the storage form of the next-level proxy state as follows: OXC represents the current state of the optical node, and OLS represents the current state of the optical line; E i and O i Represents the EDFA proxy model set and the fiber segment proxy model set.

6. The method according to claim 5, wherein: Determining the instantaneous reliability and the cumulative reliability of the optical network based on the current state includes: Instantaneous reliability of optical networks Among them, V A represents the set of optical network nodes, |V A | represents the number of optical network nodes. When the optical network node v x , v y When there is a connecting line between x v y =1; Indicates the number of connections between any two optical network nodes in the optical network; Cumulative reliability of optical networks Where N represents the number of network components. represents the available time of component j, and NT represents the sum of the total operating time of all components.

7. A data processing device based on an optical network, comprising: An acquisition module, used for acquiring optical network data of the optical network and power network data associated with the optical network; A state determination module, configured to determine the current state of each level in a multi-level model based on the coupling of the optical network and the power network based on the optical network data and the power network data; wherein the current state of the previous level in the multi-level model is determined based on the current state of the next level; A reliability determination module, configured to determine the instantaneous reliability and cumulative reliability of the optical network based on the current state; wherein the instantaneous reliability is used to characterize the probability of all nodes in the optical network being connected, and the cumulative reliability is used to characterize the proportion of the sum of the reliable operation time of all components in the optical network to the total operation time; The multi-level model includes a first level and a second level, the second level is a level below the first level; the first level includes a task management agent model; the second level includes at least one of a power network agent model, an optical network agent model, an SDN control agent model, a power grid power line agent model, and an optical network control line agent model; The multi-level model further includes a third level, which is a level below the second level; the third level includes at least one of a power transmission line proxy model, a power grid node proxy model, an optical node proxy model, and an optical line system proxy model; the power transmission line proxy model and the power grid node proxy model are a level below the power network proxy model, and the optical node proxy model and the optical line system proxy model are a level below the optical network proxy model; The multi-level model further includes a fourth level, which is a level below the third level; the fourth level includes a generator proxy model, a load proxy model, and at least one of an EDFA proxy model and an optical fiber segment proxy model; the generator proxy model and the load proxy model are a level below the power grid node proxy model, and the EDFA proxy model and the optical fiber segment proxy model are a level below the optical line system proxy model; The multi-level model further includes a fifth level, which is a level below the fourth level; the fifth level includes an environmental proxy model of the element.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 6.

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