Network detection method, system, device and equipment and storage medium

Abstract

The invention provides a network detection method, system and device, equipment and a storage medium, and relates to the technical field of communication. In some embodiments of the present disclosure, an optical power parameter of an optical module in a target communication network is collected; preprocessing the optical power parameters to obtain preprocessed optical power parameters; inputting the preprocessed optical power parameters into a fault recognition model to obtain a target fault delimitation result of the target communication network; inputting the preprocessed optical power parameters into an optical path and route detection model to obtain a target optical path and route detection result; according to the invention, the optical power parameters of the optical module are collected, and based on the optical power parameters, the neural network model is adopted to carry out fault delimiting and optical path same-route detection, so that the detection efficiency is improved, and the hardware cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a network detection method, system, device, equipment and storage medium. Background Art

[0002] Fault delimitation refers to determining the device and cause of a fault when a system fault occurs. For example, when a near-end device has a no-light reception fault, determine whether it is caused by a failure in the remote device or an intermediate link fault. Optical path co-routing detection refers to whether two or more optical paths interconnecting multiple devices pass through the same route. It is generally used in communication systems where protection networking requires separate routing. Fault delimitation and optical path co-routing detection both come from the perception and analysis of device interconnection links.

[0003] There is an existing detection method, which is to obtain the topological structure information and alarm information of multiple devices, and at the same time, it is also necessary to combine the input information of the public resource system to complete the fault demarcation and optical path co-routing detection. However, when a fault occurs, the internal network management channel is often also faulty, and it is impossible to obtain the alarm information of all devices completely. At the same time, it is necessary to rely on the accuracy of the input information of the resource system, and it is impossible to perceive the actual link situation. Another detection method is to use optical fiber transmission technology to perceive and detect the link, which requires the deployment of multiple optical fiber sensor modules and at least one excitation terminal and co-routing detection unit, which is costly and difficult to transform.

[0004] Currently, the fault demarcation and optical path co-routing detection methods have low detection efficiency and high hardware cost. Summary of the invention

[0005] The present disclosure provides a network detection method, device, equipment and storage medium to at least solve the problems of low detection efficiency and high hardware cost in the prior art.

[0006] The technical solution of the present disclosure is as follows:

[0007] The present disclosure provides a network detection method, including:

[0008] Collect optical power parameters of optical modules in the target communication network;

[0009] Preprocessing the optical power parameter to obtain a preprocessed optical power parameter;

[0010] Inputting the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network; and

[0011] The pre-processed optical power parameters are input into the optical path same-route detection model to obtain the target optical path same-route detection result.

[0012] Optionally, the fault identification model includes: a first expert rule model, a first convolutional neural network and a first extreme gradient boosting model; the inputting the preprocessed optical power parameter into the fault identification model to obtain a target fault delimitation result of the target communication network includes:

[0013] Inputting the preprocessed optical power parameter into the first expert rule model to perform fault delimitation prediction to obtain a first prediction result; and

[0014] Inputting the preprocessed optical power parameter into the first convolutional neural network for fault delimitation prediction to obtain a second prediction result; and

[0015] Inputting the preprocessed optical power parameter into the first extreme gradient boosting model to perform fault delimitation prediction, and obtaining a third prediction result;

[0016] A target fault delimitation result is selected from a plurality of candidate fault delimitation results according to the first prediction result, the second prediction result and the third prediction result.

[0017] Optionally, the optical path same-route detection model includes: a second expert rule model, a second convolutional neural network and a second extreme gradient boosting model; the preprocessed optical power parameter is input into the optical path same-route detection model to obtain the target optical path same-route detection result, including:

[0018] Inputting the preprocessed optical power parameter into the second expert rule model to perform optical path same-path detection to obtain a fourth prediction result; and

[0019] Inputting the preprocessed optical power parameter into the second convolutional neural network to perform optical path same-path detection to obtain a fifth prediction result; and

[0020] Inputting the preprocessed optical power parameter into the second extreme gradient boosting model to perform optical path same-path detection to obtain a sixth prediction result;

[0021] The target optical path same-route detection result is determined according to the fourth prediction result, the fifth prediction result and the sixth prediction result.

[0022] Optionally, the target fault demarcation result is any one of power outage at the other end, optical fiber disconnection and connector disconnection; and the optical power parameter includes: an optical power value and an optical power fluctuation value.

[0023] Optionally, the optical power parameter is millisecond level data.

[0024] The present disclosure also provides a network detection system, including:

[0025] An optical module, used for collecting optical power parameters of the optical module;

[0026] A device board, used for preprocessing the optical power parameter by edge AI computing power to obtain the preprocessed optical power parameter;

[0027] The remote server is used to use the fault identification model to perform fault delimitation prediction to obtain a target fault delimitation result; and use the optical path same-route detection model to perform optical path same-route detection to obtain a target optical path same-route detection result.

[0028] The present disclosure also provides a network detection device, including:

[0029] A collection module, used for collecting optical power parameters of optical modules in a target communication network;

[0030] A preprocessing module, used for preprocessing the optical power parameter to obtain the preprocessed optical power parameter;

[0031] A first identification module, used for inputting the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network;

[0032] The second identification module is used to input the pre-processed optical power parameters into the optical path same-route detection model to obtain the target optical path same-route detection result.

[0033] The present disclosure also provides an electronic device, including:

[0034] processor;

[0035] a memory for storing instructions executable by the processor;

[0036] The processor is configured to execute the instructions to implement each step in the above method.

[0037] The embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step in the above method is implemented.

[0038] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements each step of the above method when executed by a processor.

[0039] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0040] In some embodiments of the present disclosure, optical power parameters of optical modules in a target communication network are collected; the optical power parameters are preprocessed to obtain preprocessed optical power parameters; the preprocessed optical power parameters are input into a fault identification model to obtain a target fault delimitation result of the target communication network; and the preprocessed optical power parameters are input into an optical path co-routing detection model to obtain a target optical path co-routing detection result; the present disclosure collects optical power parameters of optical modules and uses a neural network model to perform fault delimitation and optical path co-routing detection based on the optical power parameters, thereby improving detection efficiency and reducing hardware costs.

[0041] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0043] Figure 1 A flowchart of a network detection method provided for an exemplary embodiment of the present disclosure;

[0044] Figure 2 A framework diagram of a network detection system provided for an exemplary embodiment of the present disclosure;

[0045] Figure 3 A schematic diagram of a time interval in the case of a pigtail interruption provided for an exemplary embodiment of the present disclosure;

[0046] Figure 4 A schematic diagram of a time interval in the case of an optical cable interruption provided for an exemplary embodiment of the present disclosure;

[0047] Figure 5 A schematic diagram of a time interval in the case of a power outage provided for an exemplary embodiment of the present disclosure;

[0048] Figure 6 A schematic diagram of the structure of a network detection device provided by an exemplary embodiment of the present disclosure;

[0049] Figure 7 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0051] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0052] It should be noted that the user information involved in this disclosure includes but is not limited to: user device information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in this disclosure comply with the relevant laws and regulations and do not violate public order and good morals.

[0053] In response to the above technical problems, in some embodiments of the present disclosure, optical power parameters of optical modules in a target communication network are collected; the optical power parameters are preprocessed to obtain preprocessed optical power parameters; the preprocessed optical power parameters are input into a fault identification model to obtain a target fault delimitation result of the target communication network; and the preprocessed optical power parameters are input into an optical path co-routing detection model to obtain a target optical path co-routing detection result; the present disclosure collects the optical power parameters of optical modules, and based on the optical power parameters, uses a neural network model to perform fault delimitation and optical path co-routing detection, thereby improving detection efficiency and reducing hardware costs.

[0054] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0055] Figure 1 A flow chart of a network detection method provided by an exemplary embodiment of the present disclosure. Figure 1 As shown, the method includes:

[0056] S101: Collecting optical power parameters of optical modules in a target communication network;

[0057] S102: preprocessing the optical power parameter to obtain the preprocessed optical power parameter;

[0058] S103: inputting the preprocessed optical power parameters into a fault identification model to obtain a target fault demarcation result of the target communication network; and

[0059] S104: Input the pre-processed optical power parameters into the optical path same-route detection model to obtain the target optical path same-route detection result.

[0060] In this embodiment, the execution subject of the above method may be a terminal device or a server.

[0061] Among them, the terminal device includes but is not limited to a mobile station (MS), a mobile terminal, a mobile phone, a handset and a portable equipment, etc. The terminal device can communicate with one or more core networks via a radio access network (RAN). For example, the terminal device can be a mobile phone (or a "cellular" phone), a computer with a wireless communication function, etc. The terminal device can also be a computer with a wireless transceiver function, a virtual reality (VR) terminal device, an AR terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc., and the operating system installed on the terminal device includes but is not limited to: IOS, Android, windows, linux, Mac OS and other operating systems. In different networks, the terminal may be called different names, such as user equipment, mobile station, subscriber unit, station, cellular phone, personal digital assistant, wireless modem, wireless communication device, handheld device, laptop computer, cordless phone, wireless local loop station, television, etc. For the convenience of description, it is referred to as terminal equipment in this embodiment.

[0062] In this embodiment, the implementation form of the server is not limited. For example, the server can be a conventional server, a cloud server, a cloud host, a virtual center, or other server devices. The server mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general computer architecture type.

[0063] In this embodiment, optical power parameters of optical modules in the target communication network are collected; the optical power parameters are preprocessed to obtain preprocessed optical power parameters; the preprocessed optical power parameters are input into a fault identification model to obtain a target fault delimitation result of the target communication network; and the preprocessed optical power parameters are input into an optical path same-route detection model to obtain a target optical path same-route detection result; the present disclosure collects optical power parameters of optical modules, and based on the optical power parameters, uses a neural network model to perform fault delimitation and optical path same-route detection, thereby improving detection efficiency and reducing hardware costs.

[0064] It should be noted that the optical power parameters include but are not limited to: optical power value and optical power fluctuation value. The optical power parameters are millisecond-level data.

[0065] Figure 2 A framework diagram of a network detection system provided by an exemplary embodiment of the present disclosure. Figure 2 As shown in the figure, the network detection system includes: training and reasoning layer, preprocessing layer and collection layer. The collection layer is deployed on the optical module, and collects millisecond-level optical power parameters through the optical module; the preprocessing layer is deployed on the device board, and performs data cleaning and data preprocessing through edge AI computing power; the training and reasoning layer is deployed on the remote server, and outputs fault delimitation results and optical path same-route detection results through the training and reasoning of the fault identification model and the optical path same-route detection model.

[0066] In some embodiments of the present disclosure, optical power parameters of optical modules in the target communication network are collected, wherein the optical modules of the collection layer collect millisecond optical power parameters, form time vector data and report to the preprocessing layer.

[0067] In some embodiments of the present disclosure, the optical power parameters are preprocessed to obtain the preprocessed optical power parameters. Since the data reported by the device preprocessing layer to the server training and reasoning layer is transmitted in-band, it will occupy the link bandwidth. If all the collected data are reported to the preprocessing layer, the transmission of normal services will be affected. Therefore, the preprocessing layer will preprocess the millisecond-level data, and the initial default is that the timestamp and change value will be reported only when the optical power changes by more than 1dB within 10ms. The timestamp is obtained by 1588V2 and the time synchronization of all network devices is maintained. At the same time, the training and reasoning layer will also adjust the time interval and threshold value of the optical power change in real time according to the trained model / features to ensure that the preprocessing layer reports more accurate and effective data to the training and reasoning layer. With the help of time synchronization, the present disclosure reduces the occupancy of in-band bandwidth by minimizing the reported timestamps and optical power time series values.

[0068] The following examples illustrate the time intervals during which the preprocessing layer reports to the training and inference layer under three typical characteristics: pigtail break, optical cable break, and power outage. Figure 3A schematic diagram of a time interval in the case of a pigtail break provided for an exemplary embodiment of the present disclosure. Figure 4 A schematic diagram of a time interval in the case of an optical cable break provided for an exemplary embodiment of the present disclosure. Figure 5 A schematic diagram of a time interval in the event of a power outage provided for an exemplary embodiment of the present disclosure.

[0069] It should be noted that, based on the idea of ​​ensemble learning, the expert rule algorithm and the neural network algorithm are first used to effectively utilize typical features and the neural network can effectively extract data by itself, and then combined with the extreme gradient boosting algorithm to learn a richer nonlinear decision boundary. The algorithm used in the fault identification model disclosed in the present invention and the optical path co-routing detection model is the same. It should be noted that the fault identification model includes: a first expert rule model, a first convolutional neural network and a first extreme gradient boosting model. The optical path co-routing detection model includes: a second expert rule model, a second convolutional neural network and a second extreme gradient boosting model. The training inference algorithm used in the optical path co-routing detection model is the same.

[0070] Among them, the expert rule algorithm, although the millisecond optical power has obvious characteristics, the causes of failures in the existing network are very rich, and typical characteristics are difficult to list. The neural network algorithm adopts a neural network solution, which can further reduce the investment of experts at the feature level, allowing the neural network to effectively extract itself, convert expert experience into a neural network model architecture and maintain a high-quality sample data set, so that the model can adaptively maintain key features and nonlinear decision boundaries. The extreme gradient boosting algorithm adopts feature engineering combined with machine learning solutions to learn richer nonlinear decision boundaries. Compared with rule-based solutions, experts only need to focus on discovering potentially useful features and maintaining high-quality sample data sets, so that the model can adaptively maintain nonlinear decision strategies. It should be noted that the expert rule algorithm, neural network algorithm and extreme gradient boosting algorithm in the embodiments of the present disclosure all adopt existing algorithms, and the expert rule algorithm, neural network algorithm and extreme gradient boosting algorithm of the present disclosure are not described in detail.

[0071] In the above embodiment, the extreme gradient boosting algorithm is used to extract the stage characteristics of the optical power parameters before, during and after the drop; wherein the stage characteristics include but are not limited to: stage mean, average distance from the maximum value, first-order maximum value and stage extreme difference. The stage characteristics of each stage are merged into a 54-dimensional feature vector, and the final fault demarcation result is obtained based on cross-validation and random search hyperparameter optimization.

[0072] It should be noted that the candidate fault demarcation results include: power failure at the other end, fiber disconnection, and connector disconnection.

[0073] In some embodiments of the present disclosure, the preprocessed optical power parameters are input into the fault identification model to obtain the target fault delimitation result of the target communication network. One achievable method is to input the preprocessed optical power parameters into the first expert rule model for fault delimitation prediction to obtain a first prediction result; and input the preprocessed optical power parameters into the first convolutional neural network for fault delimitation prediction to obtain a second prediction result; and input the preprocessed optical power parameters into the first extreme gradient boosting model for fault delimitation prediction to obtain a third prediction result; and select the target fault delimitation result from multiple candidate fault delimitation results according to the first prediction result, the second prediction result and the third prediction result. The present disclosure adopts a comprehensive voting rule, and each model outputs a prediction result; based on the first prediction result, the second prediction result and the third prediction result, the number of each candidate fault delimitation result is counted, and the target fault delimitation result with the largest number is selected from multiple candidate fault delimitation results. If there are multiple candidate fault delimitation results with the same number, the third prediction result of the first extreme gradient boosting model is used as the target fault delimitation result.

[0074] It should be noted that the optical path same-route detection result includes: multiple optical paths pass through the same route and multiple optical paths do not pass through the same route.

[0075] In some embodiments of the present disclosure, the preprocessed optical power parameters are input into the second expert rule model to perform optical path co-routing detection to obtain a fourth prediction result; and the preprocessed optical power parameters are input into the second convolutional neural network to perform optical path co-routing detection to obtain a fifth prediction result; and the preprocessed optical power parameters are input into the second extreme gradient boosting model to perform optical path co-routing detection to obtain a sixth prediction result; and the target optical path co-routing detection result is determined based on the fourth prediction result, the fifth prediction result, and the sixth prediction result. The present disclosure adopts a comprehensive voting rule, and each model outputs a prediction result; based on the fourth prediction result, the fifth prediction result, and the sixth prediction result, the number of each candidate optical path co-routing detection result is counted, and the target optical path co-routing detection result with the largest number is selected from multiple candidate optical path co-routing detection results. If there are multiple optical path co-routing detection results with the same number, the sixth prediction result of the second extreme gradient boosting model is used as the target fault delimitation result.

[0076] In the above method embodiment of the present disclosure, optical power parameters of optical modules in the target communication network are collected; the optical power parameters are preprocessed to obtain preprocessed optical power parameters; the preprocessed optical power parameters are input into a fault identification model to obtain a target fault delimitation result of the target communication network; and the preprocessed optical power parameters are input into an optical path same-route detection model to obtain a target optical path same-route detection result; the present disclosure collects the optical power parameters of the optical modules, and based on the optical power parameters, uses a neural network model to perform fault delimitation and optical path same-route detection, thereby improving detection efficiency and reducing hardware costs.

[0077] Figure 6 FIG. 6 is a schematic diagram of a network detection device 60 provided by an exemplary embodiment of the present disclosure. Figure 6 As shown, the network detection device 60 includes: a collection module 61, a preprocessing module 62, a first identification module 63 and a second identification module 64.

[0078] Wherein, the acquisition module 61 is used to acquire the optical power parameters of the optical modules in the target communication network;

[0079] A preprocessing module 62, used for preprocessing the optical power parameter to obtain the preprocessed optical power parameter;

[0080] A first identification module 63, used to input the preprocessed optical power parameters into a fault identification model to obtain a target fault demarcation result of a target communication network;

[0081] The second identification module 64 is used to input the pre-processed optical power parameters into the optical path same-route detection model to obtain the target optical path same-route detection result.

[0082] Optionally, the fault identification model includes: a first expert rule model, a first convolutional neural network and a first extreme gradient boosting model; when the first identification module 63 inputs the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network, it is used to:

[0083] Inputting the preprocessed optical power parameters into a first expert rule model to perform fault delimitation prediction to obtain a first prediction result; and

[0084] Inputting the preprocessed optical power parameter into the first convolutional neural network for fault delimitation prediction to obtain a second prediction result; and

[0085] Inputting the preprocessed optical power parameters into the first extreme gradient boosting model to perform fault delimitation prediction, and obtaining a third prediction result;

[0086] A target fault delimitation result is selected from a plurality of candidate fault delimitation results according to the first prediction result, the second prediction result and the third prediction result.

[0087] Optionally, the optical path same-route detection model includes: a second expert rule model, a second convolutional neural network and a second extreme gradient boosting model; when the second recognition module 64 inputs the preprocessed optical power parameter into the optical path same-route detection model to obtain the target optical path same-route detection result, it is used to:

[0088] Inputting the preprocessed optical power parameter into the second expert rule model to perform optical path same-path detection to obtain a fourth prediction result; and

[0089] Inputting the preprocessed optical power parameter into the second convolutional neural network to perform optical path same-path detection to obtain a fifth prediction result; and

[0090] Inputting the preprocessed optical power parameters into the second extreme gradient boosting model to perform optical path same-path detection, and obtaining a sixth prediction result;

[0091] The target optical path same-route detection result is determined according to the fourth prediction result, the fifth prediction result and the sixth prediction result.

[0092] Optionally, the target fault demarcation result is any one of a power outage at the other end, a fiber disconnection, and a connector disconnection; and the optical power parameter includes: an optical power value and an optical power fluctuation value.

[0093] Optionally, the optical power parameter is millisecond level data.

[0094] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0095] Figure 7 FIG. 1 is a schematic diagram of a structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 7 As shown, the electronic device includes: a memory 71 and a processor 72. In addition, the electronic device also includes a power supply component 73 and a communication component 74.

[0096] The memory 71 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device.

[0097] The memory 71 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.

[0098] The communication component 74 is used for data transmission with other devices.

[0099] The processor 72 can execute computer instructions stored in the memory 71 to: collect optical power parameters of optical modules in the target communication network; preprocess the optical power parameters to obtain preprocessed optical power parameters; input the preprocessed optical power parameters into a fault identification model to obtain a target fault delimitation result of the target communication network; and input the preprocessed optical power parameters into an optical path same-route detection model to obtain a target optical path same-route detection result.

[0100] Optionally, the fault identification model includes: a first expert rule model, a first convolutional neural network and a first extreme gradient boosting model; when the processor 72 inputs the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network, it is used to:

[0101] Inputting the preprocessed optical power parameters into a first expert rule model to perform fault delimitation prediction to obtain a first prediction result; and

[0102] Inputting the preprocessed optical power parameter into the first convolutional neural network for fault delimitation prediction to obtain a second prediction result; and

[0103] Inputting the preprocessed optical power parameters into the first extreme gradient boosting model to perform fault delimitation prediction, and obtaining a third prediction result;

[0104] A target fault delimitation result is selected from a plurality of candidate fault delimitation results according to the first prediction result, the second prediction result and the third prediction result.

[0105] Optionally, the optical path same-route detection model includes: a second expert rule model, a second convolutional neural network and a second extreme gradient boosting model; when the processor 72 inputs the preprocessed optical power parameter into the optical path same-route detection model to obtain the target optical path same-route detection result, it is used to:

[0106] Inputting the preprocessed optical power parameter into the second expert rule model to perform optical path same-path detection to obtain a fourth prediction result; and

[0107] Inputting the preprocessed optical power parameter into the second convolutional neural network to perform optical path same-path detection to obtain a fifth prediction result; and

[0108] Inputting the preprocessed optical power parameters into the second extreme gradient boosting model to perform optical path same-path detection, and obtaining a sixth prediction result;

[0109] The target optical path same-route detection result is determined according to the fourth prediction result, the fifth prediction result and the sixth prediction result.

[0110] Optionally, the target fault demarcation result is any one of a power outage at the other end, a fiber disconnection, and a connector disconnection; and the optical power parameter includes: an optical power value and an optical power fluctuation value.

[0111] Optionally, the optical power parameter is millisecond level data.

[0112] Accordingly, the present disclosure also provides a computer-readable storage medium storing a computer program. When the computer-readable storage medium stores the computer program and the computer program is executed by one or more processors, the one or more processors execute Figure 1 Each step in the method embodiment.

[0113] Accordingly, the present disclosure also provides a computer program product, the computer program product includes a computer program / instruction, the computer program / instruction is executed by a processor Figure 1 Each step in the method embodiment.

[0114] Above Figure 7 The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0115] Above Figure 7 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0116] The electronic device also includes a display screen and an audio component.

[0117] The display screen includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0118] The audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0119] In the above-mentioned apparatus, device, storage medium and computer program product embodiments of the present disclosure, optical power parameters of optical modules in the target communication network are collected; the optical power parameters are preprocessed to obtain preprocessed optical power parameters; the preprocessed optical power parameters are input into a fault identification model to obtain a target fault delimitation result of the target communication network; and the preprocessed optical power parameters are input into an optical path same-route detection model to obtain a target optical path same-route detection result; the present disclosure collects the optical power parameters of the optical modules, and based on the optical power parameters, uses a neural network model to perform fault delimitation and optical path same-route detection, thereby improving detection efficiency and reducing hardware costs.

[0120] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0125] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0126] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, 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. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0127] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0128] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A network detection method, characterized in that: include: Collect optical power parameters of optical modules in the target communication network; Preprocessing the optical power parameter to obtain a preprocessed optical power parameter; Inputting the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network; as well as The pre-processed optical power parameters are input into the optical path same-route detection model to obtain the target optical path same-route detection result.

2. The method according to claim 1, characterized in that The fault identification model includes: a first expert rule model, a first convolutional neural network and a first extreme gradient boosting model; the preprocessed optical power parameter is input into the fault identification model to obtain a target fault delimitation result of the target communication network, including: Inputting the preprocessed optical power parameter into the first expert rule model to perform fault delimitation prediction to obtain a first prediction result; and Inputting the preprocessed optical power parameter into the first convolutional neural network for fault delimitation prediction to obtain a second prediction result; and Inputting the preprocessed optical power parameter into the first extreme gradient boosting model to perform fault delimitation prediction, to obtain a third prediction result; A target fault delimitation result is selected from a plurality of candidate fault delimitation results according to the first prediction result, the second prediction result and the third prediction result.

3. The method according to claim 1, characterized in that The optical path same-route detection model includes: a second expert rule model, a second convolutional neural network and a second extreme gradient boosting model; the pre-processed optical power parameter is input into the optical path same-route detection model to obtain the target optical path same-route detection result, including: Inputting the preprocessed optical power parameter into the second expert rule model to perform optical path same-path detection to obtain a fourth prediction result; and Inputting the preprocessed optical power parameter into the second convolutional neural network to perform optical path same-path detection to obtain a fifth prediction result; and Inputting the preprocessed optical power parameter into the second extreme gradient boosting model to perform optical path same-path detection to obtain a sixth prediction result; The target optical path same-route detection result is determined according to the fourth prediction result, the fifth prediction result and the sixth prediction result.

4. The method according to claim 1, characterized in that: The target fault demarcation result is any one of a power outage at the other end, a fiber disconnection, and a connector disconnection; and the optical power parameters include: an optical power value and an optical power fluctuation value.

5. The method according to claim 1, characterized in that The optical power parameter is millisecond level data.

6. A network detection system, characterized in that: include: An optical module, used for collecting optical power parameters of the optical module; A device board, used for preprocessing the optical power parameter by edge AI computing power to obtain the preprocessed optical power parameter; The remote server is used to perform fault demarcation prediction using a fault identification model to obtain a target fault demarcation result; And adopt the optical path same route detection model to perform optical path same route detection and obtain the target optical path same route detection result.

7. A network detection device, characterized in that: include: A collection module, used for collecting optical power parameters of optical modules in a target communication network; A preprocessing module, used for preprocessing the optical power parameter to obtain the preprocessed optical power parameter; A first identification module, used for inputting the preprocessed optical power parameter into the fault identification model to obtain a target fault demarcation result of the target communication network; The second identification module is used to input the pre-processed optical power parameters into the optical path same-route detection model to obtain the target optical path same-route detection result.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement each step in the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 5 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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