Optical module fault detection method, electronic equipment, storage medium and program product
Patent Information
- Application Number
- CN202411709621.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art cannot predict optical module failure, resulting in problems such as communication link interruption and service performance degradation.
By obtaining the parameter time series of input optical power and output optical power of the optical module, input it to a pre-trained long and short-term memory model for fault detection and prediction.
It realizes the prediction of optical module failures, eliminates fault risks in advance, and avoids communication link interruptions and service performance degradation.
Smart Images
Figure CN119483738A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer technology, and in particular to an optical module fault detection method, electronic equipment, storage medium and program product. Background Art
[0002] In order to achieve long-distance data transmission and improve anti-interference ability, optical fiber can be used to transmit signals. When using optical fiber to transmit signals, optical modules are needed to complete the conversion between optical signals and electrical signals. A large number of optical modules are needed in network equipment in data centers, communication networks, and high-speed data transmission systems. However, if an optical module fails, it will cause serious impacts such as communication link interruption, data loss, network performance degradation, and service interruption.
[0003] Currently, the management of optical module failures mainly focuses on post-failure reminders after the optical module fails, but it is impossible to predict the failure of the optical module, making it difficult to avoid problems such as interruption of the communication link and degradation of service performance caused by the optical module failure. Summary of the invention
[0004] The embodiments of the present application provide an optical module fault detection method, device, storage medium and program product, which are used to predict the failure of the optical module to avoid the problem of communication link interruption and service performance degradation caused by the optical module failure.
[0005] In a first aspect, an embodiment of the present application provides an optical module fault detection method, comprising: obtaining a parameter time series of a target parameter of a target optical module, wherein the target parameter includes an input optical power and an output optical power; inputting the parameter time series into a pre-trained long short-term memory model, and processing the parameter time series by the long short-term memory model to obtain a fault detection result of the target optical module.
[0006] In a second aspect, an embodiment of the present application provides an optical module fault detection method device, comprising: an acquisition unit, used to acquire a parameter time series of a target parameter of a target optical module, wherein the target parameter includes an input optical power and an output optical power; a detection unit, used to input the parameter time series into a pre-trained long short-term memory model, and the long short-term memory model processes the parameter time series to obtain a fault detection result of the target optical module.
[0007] In a third aspect, an embodiment of the present application provides an optical module fault detection device, including: a memory, a processor;
[0008] The memory stores computer-executable instructions;
[0009] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.
[0010] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.
[0011] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0013] Figure 1 Schematic diagram of the optical module fault detection method provided in this application Figure 1 ;
[0014] Figure 2 Schematic diagram of the optical module fault detection method provided in this application Figure 2 ;
[0015] Figure 3 A schematic diagram of the principle of the optical module detection method provided in this application;
[0016] Figure 4 A schematic diagram of the structure of the optical module fault detection device provided in this application;
[0017] Figure 5 This is a schematic diagram of the structure of the optical module detection device provided in this application.
[0018] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION
[0019] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0020] First, the terms involved in this application are explained:
[0021] Optical module: Optical module is an important component of optical fiber communication. It is an optoelectronic device that realizes the photoelectric conversion and electro-optical conversion functions in the process of optical signal transmission. The optical module works at the physical layer of the Open Systems Interconnection Model (OSI model) and is one of the core components in the optical fiber communication system. It is mainly composed of optoelectronic devices (optical transmitters, optical receivers), functional circuits and optical interfaces. Its main function is to realize the photoelectric conversion and electro-optical conversion functions in optical fiber communication. The working principle of the optical module is shown in the working principle diagram of the optical module. The sending interface inputs an electrical signal with a certain bit rate. After being processed by the internal driver chip, the semiconductor laser or light emitting diode is driven to emit a modulated optical signal of the corresponding rate. After being transmitted through the optical fiber, the receiving interface converts the optical signal into an electrical signal by the optical detection diode, and outputs an electrical signal of the corresponding bit rate after passing through the preamplifier.
[0022] Optical module output power: Optical module output power refers to the optical power output by the light source at the transmitter end of the optical module under normal working conditions, which can be understood as the intensity of light. The transmitted optical power is related to the proportion of "1" in the transmitted data signal. The more "1" there are, the greater the optical power. When the transmitter sends a pseudo-random sequence signal, "1" and "0" account for roughly half each. At this time, the power obtained by the test is the average transmitted optical power, in W or mW or dBm. W or mW is a linear unit, and dBm is a logarithmic unit. In communication, we usually use dBm to represent optical power. When an optical module fails, the output light rate is too low, which will cause the device at the opposite end to fail to communicate normally. The output power is too high and exceeds the threshold of the input light of the optical module at the opposite end, which is easy to cause damage to the optical module at the opposite end.
[0023] Long Short-Term Memory Network: Long Short-Term Memory (LSTM) is specifically used to process the prediction and modeling of time series data. LSTM introduces the mechanism of forget gate, input gate and output gate. These gating mechanisms allow the network to dynamically adjust the flow of neuron states and hidden states according to the current input and previous state, thereby adapting to various complex time patterns and data trends. LSTM is widely used due to its excellent performance in speech recognition, natural language processing, stock market prediction and other fields. It can effectively process time series data of indefinite length and provide powerful tool support for prediction and analysis tasks.
[0024] Optical fiber is used as the transmission medium between network devices and between network devices and servers in the data center. When the optical module fails, the port of the network device cannot be used, which will cause network interruption.
[0025] At present, the processing of optical module failure is mainly that when the optical module of the network device fails, the network device will generate a log as a reminder. That is, the current processing of optical module failure is mainly focused on the processing of data failure during the occurrence, and it is impossible to prevent it in advance. Therefore, it is impossible to predict the risk points in advance and eliminate the hidden dangers. It is impossible to avoid network interruption and service performance degradation caused by optical module failure.
[0026] The optical module fault detection solution provided in the present application obtains the parameter time series of the optical module, inputs the parameter time series into a pre-trained long short-term memory model, and the long short-term memory model outputs the fault prediction of the optical module, thereby realizing the prediction of the optical module failure based on the historical parameters of the optical module. The risk points can be predicted in advance and the hidden dangers can be eliminated, thereby avoiding problems such as interruption of the communication link and degradation of service performance due to the optical module failure.
[0027] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.
[0028] Figure 1 Schematic diagram of the optical module fault detection method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:
[0029] S101. Acquire a parameter time series of target parameters of a target optical module, wherein the target parameters include input optical power and output optical power.
[0030] The execution subject of the optical module fault detection method in this embodiment can be an electronic device such as a server. The target optical module can be any one of multiple optical modules. The network device can include at least one port. Each port can be provided with at least one optical module.
[0031] The target parameter may be a specified parameter used for fault analysis among multiple parameters of the optical module. Here, the target parameter includes input optical power and output optical power.
[0032] The parameter time series of the target optical module may include an input power time series and an output power time series. For example, the input optical power time series may be obtained by arranging the input optical powers collected in multiple collection cycles in the order of collection time. The output optical power time series may be obtained by arranging the output optical powers collected in multiple collection cycles in the order of collection time.
[0033] S102: Inputting the parameter time series into a pre-trained long short-term memory model, and processing the parameter time series by the long short-term memory model to obtain a fault detection result of the target optical module.
[0034] When an optical module has a potential fault, the input optical power and output optical power may continue to decrease, or the input optical power and output optical power may fluctuate frequently and significantly. By using the long short-term memory network to learn and predict the historical received optical power and emitted optical power of an optical module at a port, optical modules that may fail can be discovered in advance.
[0035] The long short-term memory model here may include model parameters of a forget gate, an input gate, and an output gate obtained according to the training phase. The long short-term memory model can be predicted according to the parameter time series of the target parameter input therein to obtain a fault prediction result of the target optical module.
[0036] The above fault detection results include fault categories, etc. The above fault categories include but are not limited to one or more of the following:
[0037] Transmitter failure, receiver failure, over temperature, poor interface contact, signal attenuation and no failure.
[0038] Among them, transmitter failures such as laser failure or driver circuit failure will lead to communication interruption; receiver failures such as light detector failure or amplifier circuit failure will lead to data reception failure; excessive temperature may cause damage to internal components of the optical module due to poor heat dissipation or high ambient temperature; poor interface contact includes loose connection or interface contamination, resulting in unstable signal transmission.
[0039] In this embodiment, by obtaining the parameter time series of the optical module, the parameter time series is input into a pre-trained long short-term memory model, and the long short-term memory model outputs the fault detection result of the optical module, thereby realizing the prediction of the fault of the optical module according to the historical parameters of the optical module. The fault risk points can be predicted in advance and eliminated in advance, thereby avoiding network interruption, service performance degradation and other problems caused by optical module failure.
[0040] Figure 2 Schematic diagram of the optical module fault detection method provided in this application Figure 2 ,like Figure 2 As shown, the optical module fault detection method provided in this embodiment includes:
[0041] S201: Periodically collect parameters of multiple ports of a network device, determine parameters of multiple optical modules from the parameters of the multiple ports; and store the parameters of each of the multiple optical modules in a preset database.
[0042] For example, parameters of each of the multiple ports of one or more network devices are collected every 24 hours or every week.
[0043] The execution subject of the optical module fault detection method of this embodiment can periodically collect parameters of each port through a data acquisition module. The data acquisition module (which can be composed of a data acquisition server) collects parameters of multiple ports of a network device through a secure shell protocol (Secure Shell, SSH) protocol and a simple network management protocol (Simple Network Management Protocol, SNMP) protocol. Specifically, the above-mentioned data acquisition module can collect the device name corresponding to each port of the network device, the port status (whether the port is enabled), the optical module type (the type is single-mode, dual-mode, and the bandwidth is 10G, 40G, 100G), the optical module information (serial number, brand), the input optical power of the optical module, and the output optical power. The input optical power here includes the input optical power of the optical module. The output optical power may include the output optical power of the optical module.
[0044] The parameters of each optical module include one or more of the following: the network device where the optical module is located, the port, the port status, the type of the optical module, the input optical power, the output optical power, the input optical power threshold, the output optical power threshold, etc.
[0045] The data acquisition module sends the acquired parameters of the optical module to the data storage and analysis module. The storage function of the data storage and analysis module is implemented by a message processing platform and a preset database. The message processing platform is generally a publish / subscribe message system that adapts to high throughput, which is used to receive data from multiple data acquisition modules and distribute it to various databases for storage.
[0046] The preset database is generally a high-performance key-value database. The above data storage and analysis module performs cleaning and filtering (filtering out unused ports and ports using network cables) and formatting (saving various information of the optical module and port status information in the form of key-value pairs for subsequent storage). Specifically, the preset data analysis program can be used to clean and classify the data, and then format it, and the formatted parameters can be saved in the preset database.
[0047] In some embodiments, the above step S201 further includes:
[0048] First, for each port parameter collected each time, it is determined whether the port parameter is a parameter of an optical module according to the working state of the port and the connection line information of the port.
[0049] Secondly, if the determination result is yes, the parameters of the optical module corresponding to the port are stored in a preset database.
[0050] The above ports can be connected to optical fibers through optical modules or network cables. The connection line information of each port can be recorded in advance, and an association relationship between the port identifier and the port connection line can be established.
[0051] The working state of the above-mentioned port includes an online working state and an offline state. Among them, the offline working state includes a set offline state and a physical offline state. The set offline state is an offline state of the port not working set by the user. The physical offline state refers to a non-working state caused by a fault. The offline state can be identified as a set offline state and a physical offline state according to the identifier of the offline state.
[0052] The connection line information of a port may be determined according to an identifier of a port and an association relationship between a preset port identifier and a port connection line.
[0053] The above association relationship includes multiple port identifiers and the connection line information corresponding to each port identifier. The above connection line includes an optical fiber connection line and a network cable connection line. For any port, the port can be connected to one of the optical fiber and the network cable. The port connected to the network cable connection line will not use an optical module.
[0054] In the above preprocessing process, if the port is connected to the network cable, the parameters of the port are not the parameters of the optical module.
[0055] If the port is set to offline state, the parameters of the port are not the parameters of the optical module.
[0056] If the port is connected to an optical fiber and is not set to an offline state, the parameters of the port can be used as the parameters of the optical module, and the parameters of the optical module can be stored in a preset database. The parameters of the optical module can be stored in the preset database in the form of key-value pairs. By determining the parameters of the optical module from the parameters of multiple ports according to the working status of the port and the connection line of the port, interference parameters can be avoided to improve the accuracy of the fault detection result.
[0057] In some embodiments, the periodic collection of parameters of multiple ports of the network device in step S201 above includes:
[0058] Use the parameter collection command to collect the parameters of multiple ports online.
[0059] The above-mentioned execution subject can use the executable parameter collection instruction to collect the parameters of each port online.
[0060] Specifically, an online optical power meter may be provided for each optical module to detect the output optical power and input optical power of the optical module online, and the output optical power and input optical power are written into the port parameters.
[0061] The parameter collection instruction can be used to collect multiple port parameters online, so as to realize online fault detection of optical modules.
[0062] S202: Acquire parameters collected in multiple collection cycles from a preset database, and form a parameter time series from target parameters collected in multiple collection cycles; wherein the target parameters include input optical power and output optical power.
[0063] S203: Inputting the parameter time series into a pre-trained long short-term memory model, and processing the parameter time series by the long short-term memory model to obtain a fault detection result of the target optical module.
[0064] In this example, the periodic collection of parameters of multiple optical modules and the storage of the collected parameters of the multiple optical modules in a preset database are described, thereby providing data for the long short-term memory model to perform online fault detection on the optical modules through the parameter time series of the optical modules, which is beneficial to improving the accuracy of the optical module fault detection results.
[0065] exist Figure 1 and Figure 2 In some embodiments of the provided optical module fault detection method, the method further includes:
[0066] First, historical fault information of multiple optical modules and a historical parameter time series of target parameters of each optical module are obtained; wherein the historical fault information includes the fault category of the historical fault;
[0067] Secondly, construct a training data set; wherein the training data set includes a plurality of input-output pairs, the input of the input-output pair includes the historical parameter time series, and the output includes the corresponding fault category;
[0068] Finally, the input of the input-output pair is used as the input of the long short-term memory model, the corresponding fault category is used as the output of the long short-term memory model, and the long short-term memory model is trained to obtain a trained long short-term memory model.
[0069] The above fault categories include but are not limited to: transmitter fault, receiver fault, overtemperature, poor interface contact, signal attenuation and no fault.
[0070] The long short-term memory model is supervisedly trained using the above training data set. During the above supervised training process, a preset loss function can be used to calculate the difference between the fault prediction category output by the long short-term memory model and the fault category information in the input-output pair, and the parameters of the long short-term memory model are adjusted according to the above difference.
[0071] The long short-term memory model obtained through the above training can more accurately predict the failure of the optical module based on the input parameter time series.
[0072] exist Figure 1 and Figure 2 In some embodiments of the provided optical module fault detection method, the method further includes:
[0073] If the fault detection result of the target optical module indicates that the target optical module has a fault, displaying the fault information of the target optical module; and / or,
[0074] Displays historical target parameter information of multiple optical modules.
[0075] In an example, if the fault detection result output by the long short-term memory model indicates that a target optical module is faulty, the fault information is displayed, and the fault information includes the fault category, the port where the fault occurs, the optical module type, the serial number, the identifier of the network device where the optical module is located, and other information.
[0076] Specifically, the fault information may be displayed in a preset display page, which may be a web page.
[0077] In one example, for each optical module, the historical parameter values of the target parameters of the optical module may also be displayed, such as the historical input optical power and historical output optical power of each of the multiple ports of the optical module.
[0078] Please refer to Figure 3 , Figure 3The following is a schematic diagram of a publicly available optical module detection method. Figure 3 As shown, the network device 301 may include multiple ports, and the multiple ports may only include one or more ports connected to the optical module. The data acquisition module may periodically collect parameters of multiple ports in the network device 301. And determine the parameters of the optical module according to the parameters of the multiple ports. The parameters of the optical module are sent to the data storage and analysis module, and the data storage and analysis module stores the parameters of the optical module in the preset database 302 in the form of key-value pairs. The preset database 302 can store parameters collected in multiple collection cycles according to information such as network devices, ports, and optical modules. The above parameters include specific parameter values of target parameters.
[0079] The data storage and analysis module can obtain the parameter time series of the target parameter from the preset database (the parameter time series can include the latest target parameter value), input the parameter time series into the long short-term memory model 303, and the long short-term memory model predicts the fault information according to the input parameter time series. The fault information includes the port where the fault occurs, the type of optical module, the serial number, the identifier of the network device where the optical module is located, etc. The fault information is sent to the display module. The display module can display the fault information in the display device 304.
[0080] By displaying the fault information of the target optical module, the user can intuitively browse which optical module has failed and understand the fault information, so as to replace or repair the optical module that may fail in time to avoid communication link interruption, data loss or communication link performance degradation caused by optical module failure.
[0081] In addition, by displaying the historical target parameter information of multiple optical modules, the user can browse the parameter value change information of the target parameter of each optical module, so that the user can judge whether there is a hidden risk in the optical module.
[0082] In some embodiments, the method further comprises:
[0083] Receive user-input fault information of one or more optical modules, and fine-tune the parameters of the long short-term memory model according to the user-input fault information.
[0084] The user input fault information here can be information input by the user into the execution body of the above optical module detection method according to the actual fault information. The above execution body can fine-tune the parameters of the above long short-term memory model according to the user input fault information, so that the fine-tuned long short-term memory model can predict the fault indicated by the user input fault information. By continuously enriching the fine-tuning sample data and improving the robustness of the long short-term memory model, the accuracy of optical module fault prediction can be further improved.
[0085] Figure 4 The schematic diagram of the structure of the optical module fault detection device provided in this application is as follows: Figure 4 As shown, the optical module fault detection device 40 provided in this embodiment includes an acquisition unit 401 and a detection unit 402, wherein:
[0086] An acquisition unit, used to acquire a parameter time series of a target parameter of a target optical module, wherein the target parameter includes an input optical power and an output optical power;
[0087] The detection unit is used to input the parameter time series into a pre-trained long short-term memory model, and the long short-term memory model processes the parameter time series to obtain a fault detection result of the target optical module.
[0088] In a possible implementation, the device 40 also includes a data acquisition unit (not shown in the figure), which is used to: periodically collect parameters of multiple ports of the network device, determine parameters of multiple optical modules from the parameters of the multiple ports; and store the parameters of each of the multiple optical modules in a preset database; and the acquisition unit 401 is further used to: obtain the target parameters collected in multiple acquisition cycles from the preset database, and form the parameter time series by the target parameters collected in multiple acquisition cycles.
[0089] In a possible implementation, the data acquisition unit is further used to: for each port parameter acquired each time, determine whether the port parameter includes the optical module parameter according to the working state of the port and the connection line information of the port;
[0090] If the determination result is yes, the parameters of the optical module corresponding to the port are stored in a preset database.
[0091] In a possible implementation, the parameters of the multiple ports are collected online using a parameter collection instruction.
[0092] In a possible implementation, the device 40 further includes a training unit (not shown in the figure), which is used to: obtain historical fault information of multiple optical modules and a historical parameter time series of target parameters of each of the optical modules; wherein the historical fault information includes a fault category of a historical fault;
[0093] Constructing a training data set; wherein the training data set includes a plurality of input-output pairs, the input of the input-output pair includes the historical parameter time series, and the output includes the corresponding fault category;
[0094] The input of the input-output pair is used as the input of the long short-term memory model, the corresponding fault category is used as the output of the long short-term memory model, and the long short-term memory model is trained to obtain a trained long short-term memory model.
[0095] In a possible implementation, the device 40 further includes a display unit (not shown in the figure), which is used to: if the fault detection result of the target optical module indicates that the target optical module has a fault, display the fault information of the target optical module; and / or,
[0096] Displays historical target parameter information of multiple optical modules.
[0097] In a possible implementation, the device 40 further includes a fine-tuning unit (not shown in the figure), which is configured to:
[0098] Receive user-input fault information of one or more optical modules, and fine-tune the parameters of the long short-term memory model according to the user-input fault information.
[0099] The database recovery device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.
[0100] Figure 5 This is a schematic diagram of the structure of the optical module fault detection device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.
[0101] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.
[0102] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.
[0103] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.
[0104] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.
[0105] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.
[0106] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.
[0107] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.
[0108] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.
[0109] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.
[0110] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.
[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0112] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0113] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.
[0114] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.
[0115] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
Claims
1. A method for detecting an optical module fault, comprising: Acquire a parameter time series of a target parameter of a target optical module, wherein the target parameter includes an input optical power and an output optical power; The parameter time series is input into a pre-trained long short-term memory model, and the long short-term memory model processes the parameter time series to obtain a fault detection result of the target optical module.
2. The method according to claim 1, characterized in that The method further comprises: Periodically collecting parameters of multiple ports of a network device, determining parameters of multiple optical modules from the parameters of the multiple ports; and storing the parameters of each of the multiple optical modules in a preset database; and The step of obtaining a parameter time series of a target parameter of a target optical module includes: The target parameters collected in multiple collection cycles are obtained from the preset database, and the target parameters collected in multiple collection cycles form the parameter time series.
3. The method according to claim 1, characterized in that The parameters of the multiple ports of the network device are periodically collected, and the parameters of the multiple optical modules are determined from the parameter information of the multiple ports according to the working status and connection line information of the multiple ports; And storing the parameters of each of the plurality of optical modules in a preset database, including: For each port parameter collected each time, determine whether the port parameter includes the optical module parameter according to the working state of the port and the connection line information of the port; If the determination result is yes, the parameters of the optical module corresponding to the port are stored in a preset database.
4. The method according to claim 2, characterized in that: The periodic collection of parameters of multiple ports of the network device includes: Use the parameter collection command to collect the parameters of multiple ports online.
5. The method according to claim 1, characterized in that The method further comprises: Acquire historical fault information of multiple optical modules and a historical parameter time series of target parameters of each of the optical modules; wherein the historical fault information includes the fault category of the historical fault; Constructing a training data set; wherein the training data set includes a plurality of input-output pairs, the input of the input-output pair includes the historical parameter time series, and the output includes the corresponding fault category; The input of the input-output pair is used as the input of the long short-term memory model, the corresponding fault category is used as the output of the long short-term memory model, and the long short-term memory model is trained to obtain a trained long short-term memory model.
6. The method according to claim 1, characterized in that The method further comprises: If the fault detection result of the target optical module indicates that the target optical module is faulty, displaying fault information of the target optical module; and / or, Displays historical target parameter information of multiple optical modules.
7. The method according to claim 6, characterized in that The fault detection result includes a fault category, and the fault category includes one or more of the following: Transmitter failure, receiver failure, over temperature, poor interface contact, signal attenuation and no failure.
8. An optical module fault detection device, comprising: An acquisition unit, used to acquire a parameter time series of a target parameter of a target optical module, wherein the target parameter includes an input optical power and an output optical power; The detection unit is used to input the parameter time series into a pre-trained long short-term memory model, and the long short-term memory model processes the parameter time series to obtain a fault detection result of the target optical module.
9. An optical module fault detection device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.