Time synchronization fault delimitation method and device based on machine learning, and storage medium

Abstract

The embodiments of the present application belong to the technical field of communications. Provided are a time synchronization fault delimitation method and device based on machine learning, and a storage medium. The time synchronization fault delimitation method based on machine learning comprise: determining a monitoring time synchronization link; and performing fault analysis on the monitoring time synchronization link by means of a preset machine learning algorithm, and determining a corresponding fault network element and / or fault module, so as to realize fault delimitation, wherein the fault delimitation comprises network-element-level delimitation, module-level delimitation, and network element-module delimitation.

Description

Time synchronization fault demarcation method, device and storage medium based on machine learning

[0001] Cross-references

[0002] The present invention claims priority to a Chinese patent application filed with the Patent Office of China on June 30, 2023, with application number 202310809803.0 and invention name “Time synchronization fault demarcation method, device and storage medium based on machine learning”. The entire contents of the application are incorporated into the present invention by reference. Technical Field

[0003] The present application relates to the field of communication technology, and in particular to a time synchronization fault demarcation method based on machine learning, a time synchronization fault demarcation device based on machine learning, and a storage medium. Background Art

[0004] With the rapid development of mobile communication network technology, networks are becoming increasingly larger and more complex. In a time synchronization network, master and slave clock nodes send synchronization data frames between them, recording and adding timestamps to the frames. Nodes calculate the deviation and transmission delay between the master and slave clocks based on the acquired data frames and timestamp information, thereby achieving time synchronization. If a node in a synchronization link experiences a time error, such as a frequency layer failure or a device-induced time error during time transfer, the synchronization time errors of other nodes following the failed node on the synchronization link will increase, ultimately impacting the normal operation of services in the system.

[0005] Time synchronization plays a crucial role in communication systems. Clock synchronization failures can lead to communication failures between communication systems, impacting system operations and services. Therefore, high-precision time synchronization technology is required to meet the needs of communication networks.

[0006] In related technologies, fault detection is generally performed manually. This requires printing out the synchronization timestamps and related configuration information of the network element equipment, and then observing and analyzing the fluctuations and changes of hundreds of timestamps to determine the fault location. Usually, fault detection of synchronous links is performed manually point by point. During this process, not only is it required to manually find the network element where the fault occurred from the network topology, but also each business expert is required to locate the module to which the fault belongs from the faulty network element and complete the fault repair. However, the number of nodes involved in synchronization in the network topology is huge, and manual positioning faces the problems of complex network topology and huge amounts of data to analyze. Manual fault location is prone to deviations. Therefore, how to improve the reliability of fault demarcation in communication systems has become a technical problem that needs to be solved urgently.

[0007] Summary of the Invention

[0008] Embodiments of the present application provide a machine learning-based time synchronization fault demarcation method, a machine learning-based time synchronization fault demarcation device, and a storage medium, aiming to improve the reliability of fault demarcation in a communication system.

[0009] In a first aspect, an embodiment of the present application provides a time synchronization fault demarcation method based on machine learning, including: determining a monitored time synchronization link; performing fault analysis on the monitored time synchronization link through a preset machine learning algorithm, and determining the corresponding faulty network element and / or faulty module to achieve fault demarcation; wherein, the fault demarcation includes network element-level demarcation, module-level demarcation and network element-module demarcation.

[0010] In a second aspect, an embodiment of the present application further provides a time synchronization fault demarcation device based on machine learning, wherein the time synchronization fault demarcation device based on machine learning includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, any one of the time synchronization fault demarcation methods based on machine learning provided in the specification of this application is realized.

[0011] In a third aspect, an embodiment of the present application further provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any of the machine learning-based time synchronization fault delimitation methods provided in the specification of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] FIG1 is a flow chart of a method for time synchronization fault demarcation based on machine learning provided in the first embodiment of the present application;

[0014] FIG2 is a flow chart of a method for time synchronization fault demarcation based on machine learning provided in a second embodiment of the present application;

[0015] FIG3 is a schematic diagram of a faulty network element defined in a method for demarcating a time synchronization fault based on machine learning provided in an embodiment of the present application;

[0016] FIG4 is a flow chart of a method for time synchronization fault demarcation based on machine learning provided in a third embodiment of the present application;

[0017] FIG5 is a schematic block diagram of the structure of a time synchronization fault demarcation device based on machine learning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0020] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] Embodiments of the present application provide a machine learning-based time synchronization fault demarcation method, a machine learning-based time synchronization fault demarcation device, and a storage medium. The machine learning-based time synchronization fault demarcation method can be applied to mobile terminals, such as mobile phones, tablet computers, laptop computers, desktop computers, personal digital assistants, and wearable devices.

[0022] The following embodiments of the present application are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0023] The following will describe in detail the machine learning-based time synchronization fault demarcation method provided by the embodiment of the present application in conjunction with the scenario in Figure 1. It should be noted that the scenario in Figure 1 is only used to explain the machine learning-based time synchronization fault demarcation method provided by the embodiment of the present application, but does not constitute a limitation on the application scenarios of the machine learning-based time synchronization fault demarcation method provided by the embodiment of the present application.

[0024] Please refer to Figure 1, which is a flowchart of a time synchronization fault demarcation method based on machine learning provided in the first embodiment of the present application.

[0025] As shown in FIG1 , the time synchronization fault demarcation method based on machine learning includes steps S10 to S30 .

[0026] Step S10: Determine and monitor a time synchronization link.

[0027] In one embodiment, different devices correspond to different communication protocols. The present device takes the SPN (Slicing Packet Network) network as an example to briefly explain the clock synchronization principle of the 1588 protocol used by the SPN.

[0028] Step 1: The master clock sends a Sync message to the slave clock, carrying its own clock t1.

[0029] Step 2: Receive the Sync message from the clock and mark it with its own clock t2 at that moment.

[0030] Step 3: The slave clock sends a Delay_Req message to the master clock, carrying its own clock t3 at that moment.

[0031] Step 4: The master clock receives the Delay_Req message and marks its own clock time t4 at that time.

[0032] Step 5: The master clock sends a Delay_Resp message to the slave clock, carrying the clock t4.

[0033] Assuming the difference between the slave clock and the master clock is offset, the transmission delay from the master clock to the slave clock is t_ms, and the transmission delay from the slave clock to the master clock is t_sm, then the following two equations exist: t2-t1=t_ms+offset; t4-t3=t_sm-offset;

[0034] Assuming the two-way delay is the same, that is, delay = t_ms = t_sm, the following results can be calculated:

[0035] offset = [(t2-t1)-(t4-t3)] / 2, and finally correct the time according to the offset.

[0036] In another embodiment, the clock synchronization principle of the 802.1AS protocol is briefly described by taking the 802.1AS protocol used in the TSN (Time-Sensitive Network) network as an example.

[0037] Step 1: The master clock sends a Sync message to the slave clock through the intermediate node TC, marking the timestamp as t1.

[0038] Step 2: After receiving the message from the master clock, the TC node sends a Pdelay_Req request message to the master clock. Similar to 1588, it calculates the transmission delay ΔTA based on the returned response message.

[0039] Step 3: The TC node adds its own forwarding processing time ΔTms and transmission delay ΔTA to the synchronization data message and sends it to the slave clock node.

[0040] Step 4: The slave clock sends a Pdelay_Req request message to the TC node to calculate the transmission delay ΔTB.

[0041] Step 5: The slave clock receives the message forwarded from the TC node to the master clock and marks it with the timestamp t2.

[0042] According to the transmission delay and t1 and t2, it can be concluded that: offset = t2-t1-ΔTA-ΔTms-ΔTB.

[0043] Specifically, on the network topology selection interface, a time synchronization link that needs to be analyzed is manually selected to trigger fault demarcation analysis. Based on the selected link, the relevant time synchronization configuration instance is indexed, and an analysis request for the configuration instance is sent to the corresponding network element, triggering the network element to perform fault network element-level reasoning.

[0044] Step S20: Perform fault analysis on the monitoring time synchronization link using a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault demarcation.

[0045] The fault demarcation includes network element level demarcation, module level demarcation and network element-module demarcation.

[0046] In a specific embodiment, different fault demarcation instructions correspond to different levels of demarcation. For example, network element-level demarcation corresponds to network element-level demarcation instructions, module-level demarcation corresponds to module-level demarcation instructions, and network element-module demarcation corresponds to network element-module demarcation instructions. These fault demarcation instructions can be manually issued by a user or intelligently issued by the target device. For example, upon self-detection of a time synchronization anomaly, a fault demarcation instruction can be automatically issued to identify the faulty network element and / or faulty module. The machine learning-based time synchronization fault demarcation method provided in this embodiment can be applied to various scenarios, such as demarcating faulty network elements, demarcating faulty modules, and performing secondary demarcation between network elements and modules.

[0047] The fault demarcation steps for network elements and modules are similar. This article uses locating a faulty network element as an example. The network element collects message timestamp information from the underlying latch of that instance and, after several data processing steps, generates key features. These extracted features are fed into a machine learning algorithm to infer whether the timestamp information is abnormal. The inference results are aggregated to obtain the inference results for each network element in the selected link. Among all network elements that are inferred to be faulty, the one furthest upstream in the synchronization link is determined to be the faulty network element.

[0048] For the NE-module level, after locating the faulty NE using the aforementioned method, a configuration instance analysis request is sent to the faulty NE, triggering module-level inference. The NE collects and integrates synchronization timestamp information, configuration information, and relevant module diagnostic information, and extracts key features through data processing. These extracted features are fed into a machine learning algorithm to infer the faulty module number, which is then pushed to the user end.

[0049] The embodiments of the present application provide a method, device and storage medium for time synchronization fault delimitation based on machine learning, which belongs to the field of communication technology. The method for time synchronization fault delimitation based on machine learning includes: determining a monitoring time synchronization link; performing fault analysis on the monitoring time synchronization link through a preset machine learning algorithm, and determining the corresponding faulty network element and / or faulty module to achieve fault delimitation; wherein, the fault delimitation includes network element-level delimitation, module-level delimitation and network element-module delimitation. The present application targets a time synchronization link that requires monitoring, performs fault analysis on the time synchronization link through a preset machine learning algorithm, and achieves network element-level delimitation, module-level delimitation and network element-module two-level delimitation, thereby improving the reliability of fault delimitation in the communication system.

[0050] Please refer to Figure 2, which is a flowchart of a time synchronization fault demarcation method based on machine learning provided in the second embodiment of the present application.

[0051] Based on the embodiment shown in FIG. 1 , this embodiment is shown in FIG. 2 , where step S20 includes steps S201 to S202 .

[0052] Step S201: upon receiving a network element level delimitation instruction corresponding to the network element level delimitation and / or a module level delimitation instruction corresponding to the module level delimitation, obtaining timestamp information of the monitoring time synchronization link.

[0053] Specifically, based on the selected synchronization link, the 1588 configuration instances of the network element nodes are indexed, including but not limited to the master and slave configuration instances. An analysis request for the configuration instance is generated and sent to the corresponding network element, triggering network element-level reasoning. Indexing methods include but are not limited to searching for 1588 configuration instances on the network element by link port.

[0054] After receiving the request, the network element configures the underlying high-speed chip to latch the message timestamp (T1-T4) of the corresponding 1588 instance. The data acquisition module reads the latched timestamp and caches multiple sampling points to form timestamp information. It then processes the data, cleans the data, and performs feature engineering to extract important features that can characterize the changing characteristics of the timestamp information. Feature engineering steps include but are not limited to differential processing, time series statistical feature extraction, time domain / frequency domain feature extraction of time series, and feature normalization. The categories of extracted features include but are not limited to the following: 1. Time domain features, such as differential mean and differential variance; 2. Statistical features, such as mean, variance, and peak; 3. Frequency domain features, such as Fourier transform or wavelet transform of time series.

[0055] Step S202: perform data processing on the timestamp information to generate network element timestamp features and / or module timestamp features, and determine the faulty network element and / or the faulty module based on the preset machine learning algorithm, the network element timestamp features and / or the module timestamp features.

[0056] In a specific embodiment, the extracted time series features are sent to a lightweight machine learning algorithm for reasoning, and the reasoning result is whether the timestamp information (T1 to T4) is abnormal, and the result is pushed to the management background. The machine learning algorithm is a supervised learning algorithm, including but not limited to tree model algorithms (such as random forests, gradient boosting trees), support vector machines, artificial neural networks, naive Bayes and other algorithms; one or more algorithms can be used for prediction to obtain the prediction results of each algorithm, and then combined with the weight coefficients, the final prediction results are obtained by weighting. The reasoning results of each network element are combined with the time synchronization link topology to determine the network element to which the fault belongs.

[0057] As shown in Figure 3, Figure 3 is a schematic diagram of a faulty network element demarcation method based on machine learning provided by an embodiment of the present application. If the selected link contains a total of 5 network elements A, B, C, D, and E, each network element will push the inference result to the management background, and the time synchronization link direction is from A to E. When B fails and causes a timestamp exception, the timestamp of network element C, which uses B as the master clock for synchronization, will also produce an exception. Similarly, network elements D and E will also produce timestamp exceptions. Among the network elements that produce timestamp exceptions, if B is at the upstream of the link, B will be determined as a faulty network element. The network element that has a T1 or T4 timestamp exception and is located at the upstream of the link is a faulty network element, and this is a Master instance exception; or the network element that has a T2 or T3 timestamp exception and is located at the upstream of the link is a faulty network element, and this is a Slave instance exception.

[0058] The embodiment of the present application provides a method, device and storage medium for time synchronization fault delimitation based on machine learning, which belongs to the field of communication technology. The method for time synchronization fault delimitation based on machine learning includes: determining a monitoring time synchronization link; obtaining the timestamp information of the monitoring time synchronization link when receiving the network element level delimitation instruction corresponding to the network element level delimitation and / or the module level delimitation instruction corresponding to the module level delimitation; performing data processing on the timestamp information to generate network element timestamp features and / or module timestamp features, and determining the faulty network element and / or the faulty module based on the preset machine learning algorithm, the network element timestamp features and / or the module timestamp features; wherein the fault delimitation includes network element level delimitation, module level delimitation and network element-module delimitation. The present application targets a time synchronization link that requires monitoring, performs fault analysis on the time synchronization link through a preset machine learning algorithm, realizes network element level delimitation, module level delimitation and network element-module two-level delimitation, and improves the reliability of fault delimitation in the communication system.

[0059] Based on the embodiment shown in FIG2 , in this embodiment, step S201 includes:

[0060] A preset number of message timestamps are determined based on the monitoring time synchronization link; and the preset number of message timestamps and corresponding sampling point information are read to generate the timestamp information.

[0061] In a specific embodiment, the fault module is determined using an SPN product as an example. After receiving the request, the network element configures the underlying high-speed chip to latch the message timestamp (T1-T4) of the corresponding 1588 instance. The data acquisition module reads the latched timestamp and caches multiple sampling points to form timestamp information, and then performs data processing. The data processing module cleans and performs feature engineering on the data to extract important features that can characterize the changing characteristics of the timestamp information. The feature engineering steps include but are not limited to differential processing, time series statistical feature extraction, time domain / frequency domain feature extraction of time series, and feature normalization.

[0062] Based on the embodiment shown in FIG2 , in this embodiment, before step S202, the following steps are included:

[0063] Based on the preset machine learning algorithm, determine whether the network element timestamp feature and / or the module timestamp feature is abnormal; if it is determined that the network element timestamp feature and / or the module timestamp feature is abnormal, determine that the corresponding network element or module is an abnormal network element or abnormal module.

[0064] Specifically, the extracted time series features are fed into a lightweight machine learning algorithm for inference. The inference result is whether the timestamp information (T1-T4) is abnormal, and the result is pushed to the management backend. Machine learning algorithms include but are not limited to gradient boosting trees, support vector machines, and naive Bayes. Forecasting can be performed using one or more algorithms, and the prediction results of each algorithm are then combined with weight coefficients to obtain the final prediction result.

[0065] Based on the embodiment shown in FIG2 , in this embodiment, step S202 includes the following steps:

[0066] When it is detected that the detection results of each network element or each module of the time synchronization link are abnormal, at least one abnormal network element or at least one abnormal module is determined; and the abnormal network element at the most upstream of the time synchronization link is determined as the faulty network element.

[0067] Please refer to Figure 4, which is a flowchart of a time synchronization fault demarcation method based on machine learning provided in the third embodiment of the present application.

[0068] Based on the embodiment shown in FIG. 1 , in this embodiment, as shown in FIG. 4 , step S20 further includes steps S203 to S204 .

[0069] Step S203: When a network element-module delimitation instruction corresponding to the network element-module delimitation is received, a configuration instance corresponding to the monitoring time synchronization link is indexed.

[0070] Specifically, the management backend indexes the 1588 configuration instances of the network element nodes, including but not limited to master and slave configuration instances, based on the selected synchronization link. It then generates an analysis request for the configuration instance and sends it to the corresponding network element, triggering network element-level reasoning. TSN networks use the IEEE 802.1AS protocol for time synchronization.

[0071] Step S204: Determine the faulty network element based on the configuration instance and the preset machine learning algorithm, and determine the faulty module in the faulty network element.

[0072] In a specific embodiment, after receiving the request, the network element configures the underlying high-speed chip to latch the message timestamp (T1 to T4) of the corresponding 1588 instance; collects timestamp information, 1588 configuration instance information, and diagnostic information of related modules, and performs data processing. The above static configuration data and dynamic time series data are integrated, and also analyzed and cleaned, and feature engineering-related processing is performed to extract important features that can reflect business characteristics. The categories of extracted features include but are not limited to the following categories: 1. Time domain features, such as differential mean and differential variance. 2. Statistical features, such as mean, variance, peak, etc. 3. Frequency domain features, such as Fourier transform or wavelet transform of time series.

[0073] The extracted business features are fed into a lightweight machine learning algorithm for inference. The inference result is the module number to which the fault belongs, and the result is pushed to the management backend. Faulty modules include, but are not limited to, crystal oscillators, phase-locked loop chips, FPGAs, and switch chips. Machine learning algorithms are supervised learning algorithms, including but not limited to tree model algorithms (such as random forests and gradient boosted trees), support vector machines, artificial neural networks, and naive Bayes. One or more algorithms can be used for prediction, and the prediction results of each algorithm are then combined with weight coefficients to obtain the final prediction result.

[0074] The management backend receives the inference results of the faulty network element, indexes the relevant causes and repair suggestions by the fault block number, and pushes them to the user.

[0075] The embodiment of the present application provides a method, device and storage medium for time synchronization fault delimitation based on machine learning, which belongs to the field of communication technology. The method for time synchronization fault delimitation based on machine learning includes: determining a monitoring time synchronization link; when receiving a network element-module delimitation instruction corresponding to the network element-module delimitation, indexing a configuration instance corresponding to the monitoring time synchronization link; based on the configuration instance and the preset machine learning algorithm, determining the faulty network element, and determining the faulty module in the faulty network element; wherein the fault delimitation includes network element-level delimitation, module-level delimitation and network element-module delimitation. The present application targets a time synchronization link that requires monitoring, performs fault analysis on the time synchronization link through a preset machine learning algorithm, realizes network element-level delimitation, module-level delimitation and network element-module two-level delimitation, and improves the reliability of fault delimitation in the communication system.

[0076] Based on the embodiment shown in FIG4 , in this embodiment, step S204 includes:

[0077] Based on the configuration instance, determine the standard network element timestamp characteristics and the standard module timestamp characteristics; through the preset machine learning algorithm, compare the network element timestamp characteristics of each network element in the monitoring time synchronization link with the standard network element timestamp characteristics to determine the faulty network element; extract the module timestamp characteristics of the faulty network element, and compare the module timestamp characteristics with the standard module timestamp characteristics through the preset machine learning algorithm to determine the faulty module.

[0078] In a specific embodiment, lightweight supervised machine learning algorithms are deployed within the network element, including but not limited to tree model algorithms (such as random forests, gradient boosting trees), support vector machines, artificial neural networks, naive Bayes and other algorithms; multi-classification is performed on multivariate features, and the fault module number is output. The fault module includes but is not limited to crystal oscillators, phase-locked loop chips, FPGAs, switching chips, etc., and finally the results are pushed to the data channel.

[0079] Based on all the above embodiments, in this embodiment, after step S20, the following steps are further included:

[0080] The fault causes and repair suggestions matching the faulty network element and / or the faulty module are indexed and pushed to the user end.

[0081] Specifically, it receives the inference results pushed by the faulty network element, that is, the faulty module number, and indexes the relevant causes and repair suggestions, and pushes them to the user.

[0082] Please refer to Figure 5, which is a schematic block diagram of the structure of a time synchronization fault demarcation device based on machine learning provided in an embodiment of the present application.

[0083] As shown in FIG5 , a time synchronization fault demarcation device 300 based on machine learning includes a processor 301 and a memory 302 . The processor 301 and the memory 302 are connected via a bus 303 , such as an I2C (Inter-integrated Circuit) bus.

[0084] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire machine learning-based time synchronization fault demarcation device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0085] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0086] Those skilled in the art will understand that the structure shown in FIG5 is merely a block diagram of a partial structure related to the embodiment of the present application, and does not constitute a limitation on the machine learning-based time synchronization fault demarcation device to which the embodiment of the present application is applied. The specific machine learning-based time synchronization fault demarcation device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0087] The processor 301 is configured to run a computer program stored in the memory 302 and implement any one of the machine learning-based time synchronization fault demarcation methods provided in the embodiments of the present application when executing the computer program.

[0088] In one embodiment, the processor 301 is used to run a computer program stored in a memory, and implement the following steps when executing the computer program: determining a monitoring time synchronization link; performing fault analysis on the monitoring time synchronization link through a preset machine learning algorithm, and determining the corresponding faulty network element and / or faulty module to achieve fault demarcation; wherein the fault demarcation includes network element-level demarcation, module-level demarcation, and network element-module demarcation.

[0089] In one embodiment, the fault delimitation is the network element level delimitation and / or the module level delimitation, and the fault analysis of the monitoring time synchronization link is performed through a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault delimitation, which is used to achieve: upon receiving the network element level delimitation instruction corresponding to the network element level delimitation and / or the module level delimitation instruction corresponding to the module level delimitation, obtaining the timestamp information of the monitoring time synchronization link; performing data processing on the timestamp information to generate network element timestamp features and / or module timestamp features, and determining the faulty network element and / or the faulty module based on the preset machine learning algorithm, the network element timestamp features and / or the module timestamp features.

[0090] In one embodiment, upon receiving a network element-level delimitation instruction corresponding to the network element-level delimitation and / or a module-level delimitation instruction corresponding to the module-level delimitation, the timestamp information of the monitoring time synchronization link is obtained to implement: determining a preset number of message timestamps based on the monitoring time synchronization link; reading a preset number of message timestamps and corresponding sampling point information to generate the timestamp information.

[0091] In one embodiment, the timestamp information is processed to generate a network element timestamp feature and / or a module timestamp feature, and before the faulty network element and / or the faulty module are determined based on the preset machine learning algorithm, the network element timestamp feature and / or the module timestamp feature, it is also used to implement: judging whether the network element timestamp feature and / or the module timestamp feature is abnormal based on the preset machine learning algorithm; and determining that the corresponding network element or module is an abnormal network element or abnormal module when it is determined that the network element timestamp feature and / or the module timestamp feature is abnormal.

[0092] In one embodiment, data processing is performed on the timestamp information to generate network element timestamp features and / or module timestamp features, and based on the preset machine learning algorithm, the network element timestamp features and / or the module timestamp features, the faulty network element and / or the faulty module are determined, so as to achieve: when it is detected that the detection results of each network element or each module of the time synchronization link are abnormal, at least one abnormal network element or at least one abnormal module is determined; and the abnormal network element at the most upstream end of the time synchronization link is determined as the faulty network element.

[0093] In one embodiment, the fault delimitation is the network element-module delimitation, and the fault analysis of the monitoring time synchronization link is performed through a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault delimitation. It is also used to achieve: when a network element-module delimitation instruction corresponding to the network element-module delimitation is received, indexing the configuration instance corresponding to the monitoring time synchronization link; based on the configuration instance and the preset machine learning algorithm, determining the faulty network element, and determining the faulty module in the faulty network element.

[0094] In one embodiment, based on the configuration instance and the preset machine learning algorithm, the faulty network element is determined, and the faulty module in the faulty network element is determined, which is used to achieve: based on the configuration instance, the standard network element timestamp feature and the standard module timestamp feature are determined; through the preset machine learning algorithm, the network element timestamp feature of each network element in the monitoring time synchronization link is compared with the standard network element timestamp feature to determine the faulty network element; the module timestamp feature of the faulty network element is extracted, and the module timestamp feature is compared with the standard module timestamp feature through the preset machine learning algorithm to determine the faulty module.

[0095] In one embodiment, after performing fault analysis on the monitoring time synchronization link through a preset machine learning algorithm and determining the corresponding faulty network element and / or faulty module to achieve fault demarcation, it is used to achieve: indexing the fault cause and repair suggestions that match the faulty network element and / or the faulty module and pushing them to the user end.

[0096] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process of the time synchronization fault demarcation device based on machine learning described above can refer to the corresponding process in the aforementioned embodiment of the time synchronization fault demarcation method based on machine learning, and will not be repeated here.

[0097] An embodiment of the present application also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any of the machine learning-based time synchronization fault delimitation methods provided in the description of the embodiment of the present application.

[0098] The storage medium may be an internal storage unit of the machine learning-based time synchronization fault demarcation device described in the aforementioned embodiment, such as a hard disk or memory of the machine learning-based time synchronization fault demarcation device. The storage medium may also be an external storage device of the machine learning-based time synchronization fault demarcation device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the machine learning-based time synchronization fault demarcation device.

[0099] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0100] It should be understood that the term "and / or" used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0101] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments. The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection of the claims.

Claims

1. A time synchronization fault demarcation method based on machine learning, wherein: include: Determine monitoring time synchronization link; Performing fault analysis on the monitoring time synchronization link by using a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault demarcation; The fault delimitation includes network element level delimitation, module level delimitation and network element-module delimitation.

2. The time synchronization fault demarcation method based on machine learning according to claim 1, wherein: The fault delimitation is the network element level delimitation and / or the module level delimitation, and the performing fault analysis on the monitoring time synchronization link by using a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault delimitation includes: Upon receiving a network element level delimitation instruction corresponding to the network element level delimitation and / or a module level delimitation instruction corresponding to the module level delimitation, acquiring timestamp information of the monitoring time synchronization link; The timestamp information is processed to generate a network element timestamp feature and / or a module timestamp feature, and the faulty network element and / or the faulty module is determined based on the preset machine learning algorithm, the network element timestamp feature and / or the module timestamp feature.

3. The time synchronization fault demarcation method based on machine learning according to claim 2, wherein: The acquiring the timestamp information of the monitoring time synchronization link when receiving the network element level delimitation instruction corresponding to the network element level delimitation and / or the module level delimitation instruction corresponding to the module level delimitation comprises: Determining a preset number of message timestamps based on the monitoring time synchronization link; A preset number of message timestamps and corresponding sampling point information are read to generate the timestamp information.

4. The method for time synchronization fault demarcation based on machine learning according to claim 2, wherein: Before the data processing of the timestamp information to generate a network element timestamp feature and / or a module timestamp feature, and determining the faulty network element and / or the faulty module based on the preset machine learning algorithm, the network element timestamp feature and / or the module timestamp feature, the method further includes: Based on the preset machine learning algorithm, determining whether the network element timestamp feature and / or the module timestamp feature is abnormal; When it is determined that the network element timestamp feature and / or the module timestamp feature is abnormal, the corresponding network element or module is determined to be an abnormal network element or abnormal module.

5. The method for time synchronization fault demarcation based on machine learning according to claim 4, wherein: The performing data processing on the timestamp information to generate a network element timestamp feature and / or a module timestamp feature, and determining the faulty network element and / or the faulty module based on the preset machine learning algorithm, the network element timestamp feature and / or the module timestamp feature, includes: In the case where the detection result of each network element or each module of the time synchronization link is detected to be abnormal, determining at least one abnormal network element or at least one abnormal module; The abnormal network element most upstream in the time synchronization link is determined as the faulty network element.

6. The method for time synchronization fault demarcation based on machine learning according to claim 1, wherein: The fault delimitation is the network element-module delimitation, and the fault analysis of the monitoring time synchronization link is performed by a preset machine learning algorithm to determine the corresponding faulty network element and / or faulty module to achieve fault delimitation, and further includes: In case of receiving a network element-module delimitation instruction corresponding to the network element-module delimitation, indexing a configuration instance corresponding to the monitoring time synchronization link; Based on the configuration instance and the preset machine learning algorithm, the faulty network element is determined, and the faulty module in the faulty network element is determined.

7. The method for time synchronization fault demarcation based on machine learning according to claim 6, wherein: The determining the faulty network element based on the configuration instance and the preset machine learning algorithm, and determining the faulty module in the faulty network element, comprises: Based on the configuration example, determining a standard network element timestamp feature and a standard module timestamp feature; By using the preset machine learning algorithm, the network element timestamp feature of each network element in the monitoring time synchronization link is compared with the standard network element timestamp feature to determine the faulty network element; Extract the module timestamp features of the faulty network element and compare them using the preset machine learning algorithm The module timestamp feature and the standard module timestamp feature are used to determine the faulty module.

8. The time synchronization fault delimitation method based on machine learning according to any one of claims 1 to 7, wherein: After performing fault analysis on the monitoring time synchronization link by using a preset machine learning algorithm and determining the corresponding faulty network element and / or faulty module to achieve fault demarcation, the method includes: The fault causes and repair suggestions matching the faulty network element and / or the faulty module are indexed and pushed to the user end.

9. A time synchronization fault demarcation device based on machine learning, wherein: The machine learning-based time synchronization fault delimitation device includes a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the steps of the machine learning-based time synchronization fault delimitation method as described in any one of claims 1 to 8.

10. A storage medium for computer-readable storage, wherein: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method for time synchronization fault delimitation based on machine learning as described in any one of claims 1 to 8.

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