OLT equipment uplink common single board hidden danger analysis method and system

Abstract

The invention discloses an OLT equipment uplink common single board hidden danger analysis method and system, belongs to the technical field of communication resources, and aims to solve the technical problem of how to quickly and accurately identify the OLT equipment uplink common single board hidden danger. Comprising the following steps: carrying out data preprocessing on collected running state data; extracting port attribute features based on the preprocessed operation state data, and for all ports with the same rate, constructing a feature subset by using the port attribute features corresponding to the ports belonging to the same board card; constructing a hidden danger recognition model based on a decision tree algorithm, and performing model training on the hidden danger recognition model; the method comprises the steps of collecting operation state data of a current network, performing data preprocessing on the operation state data, performing feature screening based on the preprocessed operation state data to obtain a feature subset, predicting and outputting a hidden danger type of an OLT uplink common single board through a trained hidden danger identification model based on the feature subset, and triggering an early warning mechanism based on the output hidden danger type.

Description

Technical Field

[0001] The invention relates to the technical field of communication resources, in particular to a method and system for analyzing hidden dangers of a common single board in an uplink of an OLT device. Background Art

[0002] With the widespread application of optical fiber communication technology, OLT (Optical Line Terminal) equipment, as the core equipment of the access network, has a direct impact on the stability and service quality of the entire network. However, since the uplink often relies on a limited number of key boards, once these boards fail, it may cause a large-scale network service interruption. Traditional monitoring methods rely on manual inspections and post-analysis, which makes it difficult to achieve timely discovery and early warning of potential hidden dangers. Therefore, it is particularly important to develop a method that can intelligently identify common single-board hidden dangers in the uplink of OLT equipment.

[0003] How to quickly and accurately identify the hidden dangers of the common board in the uplink of OLT equipment is a technical problem that needs to be solved. Summary of the invention

[0004] The technical task of the present invention is to address the above shortcomings and provide an OLT device uplink common board hidden danger analysis method and system to solve the technical problem of how to quickly and accurately identify OLT device uplink common board hidden dangers.

[0005] In a first aspect, the present invention provides a method for analyzing common board risks in an uplink of an OLT device, comprising the following steps:

[0006] Data collection: collect the operating status data of OLT equipment in real time, and perform data preprocessing on the collected operating status data to obtain the preprocessed operating status data. The operating status data includes key performance indicators related to the operating status and network elements whose life cycle status field is non-de-networked and whose network element type field is OLT. Among them, the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink;

[0007] Feature screening: Extract port attribute features based on pre-processed running status data. Port attribute features include port lifecycle status features, port board features, port rate features, and port networking types. For all ports with the same rate, construct a feature subset with the port attribute features corresponding to the ports belonging to the same board.

[0008] Model construction: constructing a hidden danger identification model based on a decision tree algorithm and training the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features;

[0009] Hidden danger identification: Collect the operation status data of the existing network and perform data preprocessing on the operation status data. Perform feature screening based on the preprocessed operation status data to obtain a feature subset. Based on the feature subset and the trained hidden danger identification model, predict the hidden danger type of the output OLT uplink board. Trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism.

[0010] Risk assessment and prediction: Conduct risk assessment on the identified hidden danger types and build response measures based on the assessment results;

[0011] Risk report decision: Build a hidden danger analysis report based on risk assessment results and response measures, and display the hidden danger analysis report.

[0012] Preferably, when data preprocessing is performed on the collected operating status data, the operating status data is cleaned, denoised and standardized through data preprocessing.

[0013] Preferably, when training the hidden danger identification model, the historical operating status data of the OLT equipment during failure and normal operation is obtained, and the hidden danger types corresponding to the historical operating status data are marked, the historical operating status data is preprocessed, and the preprocessed historical operating status data is subjected to feature screening, and the obtained feature subset is used as the sample feature, and the constructed hidden danger identification model is trained based on the sample features and the corresponding hidden danger types.

[0014] Preferably, the hidden danger identification model is used to perform the following hidden danger type identification:

[0015] Determine whether there is an OLT uplink port according to the port networking type field of all ports under the OLT device. If an OLT device has any port of the OLT uplink port type, the OLT device is determined to be an OLT device without an uplink port. The remaining devices with OLT uplink ports are OLT devices with uplink ports.

[0016] For each OLT device with an OLT uplink port, the following three situations are analyzed:

[0017] Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device;

[0018] Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports;

[0019] Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device;

[0020] For OLT devices of the normal uplink port OLT device type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports with the same port rate of the port object, and verify whether the boards to which all ports with the same rate belong are the same. If they are the same, the board belongs to the uplink single-board OLT device.

[0021] As a preference, when risk assessment and prediction are made, response measures include enhanced monitoring, advance maintenance, and replacement of faulty boards; when risk report decisions are made, the risk assessment results and response measures are presented in the form of charts and images in the hidden danger analysis report.

[0022] In a second aspect, the present invention provides an OLT device uplink common board hidden danger analysis system, including a data acquisition module, a feature screening module, a model building module, a hidden danger identification module, a risk assessment prediction module and a risk report decision module;

[0023] The data collection module is used to perform the following: collect the operating status data of the OLT device in real time, and perform data preprocessing on the collected operating status data to obtain the preprocessed operating status data, wherein the operating status data includes key performance indicators related to the operating status and network elements whose life cycle status field is a non-de-networked state and whose network element type field is an OLT, wherein the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink;

[0024] The feature screening module is used to perform the following: extracting port attribute features based on the preprocessed running status data, the port attribute features include port life cycle status features, port board features, port rate features and port networking types, and for all ports with the same rate, constructing a feature subset of the port attribute features corresponding to the ports belonging to the same board;

[0025] The model building module is used to perform the following: construct a hidden danger identification model based on a decision tree algorithm and perform model training on the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features;

[0026] The hidden danger identification module is used to perform the following: collect the operation status data of the existing network and perform data preprocessing on the operation status data, perform feature screening based on the preprocessed operation status data to obtain a feature subset, predict the hidden danger type of the output OLT uplink board through the trained hidden danger identification model based on the feature subset, and trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism;

[0027] The risk assessment and prediction module is used to perform the following: conduct risk assessment on the identified hidden danger types and construct response measures based on the assessment results;

[0028] The risk report decision module is used to perform the following: construct a hidden danger analysis report based on risk assessment results and response measures, and display the hidden danger analysis report.

[0029] Preferably, when performing data preprocessing on the collected operating status data, the data collection module is used to clean, denoise and standardize the operating status data through data preprocessing.

[0030] Preferably, when training the hidden danger identification model, the model construction module is used to call the data acquisition module to obtain the historical operating status data of the OLT equipment during failure and normal operation, and to mark the hidden danger types corresponding to the historical operating status data, perform data preprocessing on the historical operating status data, and call the feature screening module to perform feature screening on the preprocessed historical operating status data, so as to obtain the feature subset as the sample feature, which is used to train the constructed hidden danger identification model based on the sample features and the corresponding hidden danger types.

[0031] Preferably, the hidden danger identification model is used to perform the following hidden danger type identification:

[0032] Determine whether there is an OLT uplink port according to the port networking type field of all ports under the OLT device. If an OLT device has any port of the OLT uplink port type, the OLT device is determined to be an OLT device without an uplink port. The remaining devices with OLT uplink ports are OLT devices with uplink ports.

[0033] For each OLT device with an OLT uplink port, the following three situations are analyzed:

[0034] Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device;

[0035] Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports;

[0036] Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device;

[0037] For OLT devices of the normal uplink port OLT device type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports with the same port rate of the port object, and verify whether the boards to which all ports with the same rate belong are the same. If they are the same, the board belongs to the uplink single-board OLT device.

[0038] As a preference, when risk assessment and prediction are made, response measures include enhanced monitoring, advance maintenance, and replacement of faulty boards; when risk report decisions are made, the risk assessment results and response measures are presented in the form of charts and images in the hidden danger analysis report.

[0039] The OLT device uplink common board hidden danger analysis method and system of the present invention have the following advantages:

[0040] 1. Improved the accuracy and timeliness of OLT equipment uplink common board hidden danger identification, effectively reducing the risk of network interruption caused by single board failure;

[0041] 2. It realizes intelligent monitoring and analysis of network operation status, and improves the automation level and efficiency of network operation and maintenance;

[0042] 3. Provides detailed risk assessment and response measures, providing strong decision-making support for network operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] The present invention is further described below in conjunction with the accompanying drawings.

[0045] Figure 1 This is a flowchart of a method for analyzing common board hazards in an uplink of an OLT device according to Example 1. DETAILED DESCRIPTION

[0046] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments may be combined with each other.

[0047] The embodiment of the present invention provides an OLT device uplink common board hidden danger analysis method and system, which are used to solve the technical problem of how to quickly and accurately identify the OLT device uplink common board hidden danger.

[0048] Embodiment 1:

[0049] The present invention discloses an OLT device uplink common board hidden danger analysis method, which comprises six steps: data collection, feature screening, model construction, hidden danger identification, risk assessment prediction and risk report decision-making.

[0050] Step S100 data collection: collect the operating status data of the OLT device in real time, and perform data preprocessing on the collected operating status data to obtain the preprocessed operating status data, wherein the operating status data includes key performance indicators related to the operating status and network elements whose life cycle status field is a non-de-networked state and whose network element type field is OLT, wherein the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink.

[0051] When preprocessing the collected operating status data, the operating status data is cleaned, denoised, and standardized through data preprocessing to ensure that the data quality meets the requirements of the machine learning model.

[0052] Step S200 feature screening: extract port attribute features based on the preprocessed running status data. The port attribute features include port life cycle status features, port board features, port rate features, and port networking types. For all ports with the same rate, construct a feature subset of the port attribute features corresponding to the ports belonging to the same board.

[0053] In this step of the present embodiment, features related to the uplink common board performance are extracted from the preprocessed operating status data, such as port life cycle status features, port board features, port rate features, etc., and a feature selection algorithm is used to filter out "whether the boards to which all ports with the same rate belong are the same" as the most critical feature subset for hidden danger identification, so as to improve the accuracy and efficiency of the model.

[0054] Step S300: Model construction: construct a hidden danger identification model based on a decision tree algorithm and perform model training on the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features.

[0055] In this step of the present embodiment, when the hidden danger identification model is trained, the historical operating status data of the OLT equipment during failure and normal operation is obtained, and the hidden danger types corresponding to the historical operating status data are marked, the historical operating status data is preprocessed, and the preprocessed historical operating status data is subjected to feature screening, and the obtained feature subset is used as the sample feature, and the constructed hidden danger identification model is trained based on the sample feature and the corresponding hidden danger type.

[0056] The hazard identification model is used to perform the following hazard type identification:

[0057] (1) judging whether there is an OLT uplink port according to the port networking type field of all ports aggregated by the home OLT device; if an OLT device has any port of the OLT uplink port type, the OLT device is judged as an OLT device without an uplink port, and the rest of the devices with OLT uplink ports are OLT devices with uplink ports;

[0058] (2) For each OLT device with an OLT uplink port, perform the following three situation analyses:

[0059] Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device;

[0060] Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports;

[0061] Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device;

[0062] (3) For an OLT device of the normal uplink port type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports of the port object with the same port rate, and verify whether the boards of all ports with the same rate belong to the same block. If they are the same block, the board belongs to the uplink single-board OLT device.

[0063] The specific algorithm is as follows:

[0064] (1) DEVICE.lifecycle_status! = 'offline' && DEVICE.ne_type = 'OLT', confirm the scope [DEVICE]

[0065] (2) PORT.related_ne = [DEVICE], count(PORT.bearing_num), determine the importance level of the hidden danger of [DEVICE]

[0066] (3) PORT.related_ne = [DEVICE] && all PORT.port_net_type! = 3 (OLT uplink port), determine the range [DEVICE1] —> OLT device without uplink port, and [DEVICE-DEVICE1] —> OLT device with uplink port

[0067] (4) PORT.related_ne = [DEVICE-DEVICE1] && PORT.port_net_type = 3 && (PORT.related_card is null || PORT.related_card! = BOARD.res_identifier) ​​—> none of the uplink ports belong to the board OLT device, PORT.related_ne = [DEVICE-DEVICE1] && PORT.port_net_type = 3 && PORT.port_rate is null —> none of the uplink ports have the rate OLT device, according to the port belonging device to determine the problem range [DEVICE2], the rest [DEVICE-DEVICE1-DEVICE2] —> the positive uplink port OLT device;

[0068] (5)PORT.related_ne=[DEVICE-DEVICE1-DEVICE2]&&count(PORT.related_cardgroup by PORT.port_rate)=1—>The uplink has a single-board OLT device.

[0069] Step S400: hidden danger identification: collect the operation status data of the existing network and perform data preprocessing on the operation status data, perform feature screening based on the preprocessed operation status data to obtain a feature subset, and predict the hidden danger type of the output OLT uplink board through the trained hidden danger identification model based on the feature subset, and trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism.

[0070] As a specific implementation of hidden danger identification, based on the output results of the model, it is determined whether there are potential hidden dangers in the OLT uplink board, and the development trend of the hidden dangers is predicted. When the hidden danger of the OLT uplink board is identified, the early warning mechanism is triggered and the early warning information is sent to the operation and maintenance personnel. At the same time, suggestions for handling hidden dangers are provided to assist managers in quickly locating and resolving hidden dangers. Managers provide feedback and optimization to the model based on the actual processing results to improve the accuracy and stability of the machine learning model.

[0071] Step S500: Risk assessment prediction: Conduct risk assessment on the identified hidden danger types and build response measures based on the assessment results.

[0072] When risk assessment is predicted, response measures include enhanced monitoring, proactive maintenance, and replacement of faulty boards.

[0073] Step S600: Risk report decision: construct a hidden danger analysis report based on the risk assessment results and countermeasures, and display the hidden danger analysis report.

[0074] As a concrete implementation of the risk report decision, the hidden danger analysis report presents the risk assessment results and response measures in the form of charts and images.

[0075] This step provides intuitive decision support to network operation and maintenance personnel through risk analysis reports and response measures, helping them to quickly respond to and handle potential risks.

[0076] The method of this embodiment constructs an efficient machine learning model to conduct in-depth analysis of the real-time operation data of the OLT device, thereby realizing intelligent identification and prediction of potential hidden dangers of the common board.

[0077] Embodiment 2:

[0078] The present invention discloses an OLT equipment uplink common single board hidden danger analysis system, comprising a data acquisition module, a feature screening module, a model building module, a hidden danger identification module, a risk assessment prediction module and a risk report decision module.

[0079] The data acquisition module is used to perform the following: real-time collection of the operating status data of the OLT device, and data preprocessing of the collected operating status data to obtain the preprocessed operating status data, the operating status data including key performance indicators related to the operating status and network elements whose life cycle status field is non-de-networked and whose network element type field is OLT, wherein the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink.

[0080] As a specific implementation of the data acquisition module, when preprocessing the collected operating status data, the operating status data is cleaned, denoised and standardized through data preprocessing to ensure that the data quality meets the requirements of the machine learning model.

[0081] The feature screening module is used to perform the following: extract port attribute features based on the preprocessed running status data. The port attribute features include port life cycle status features, port board features, port rate features, and port networking types. For all ports with the same rate, the port attribute features corresponding to the ports belonging to the same board are used to construct a feature subset.

[0082] In this embodiment, the feature screening module is used to extract features related to the uplink common board performance from the preprocessed operating status data, such as port life cycle status features, port board features, port rate features, etc., and use a feature selection algorithm to screen out "whether the boards to which all ports with the same rate belong are the same" as the most critical feature subset for hidden danger identification, so as to improve the accuracy and efficiency of the model.

[0083] The model building module is used to perform the following: construct a hidden danger identification model based on a decision tree algorithm and perform model training on the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features.

[0084] As a specific implementation of the model construction module, when training the hidden danger identification model, the model construction module is used to call the data acquisition module to obtain the historical operating status data of the OLT equipment during failure and normal operation, and to mark the hidden danger types corresponding to the historical operating status data, perform data preprocessing on the historical operating status data, and call the feature screening module to perform feature screening on the preprocessed historical operating status data, so as to obtain the feature subset as the sample feature, which is used to train the constructed hidden danger identification model based on the sample features and the corresponding hidden danger types.

[0085] In this step of the present embodiment, when the hidden danger identification model is trained, the historical operating status data of the OLT equipment during failure and normal operation is obtained, and the hidden danger types corresponding to the historical operating status data are marked, the historical operating status data is preprocessed, and the preprocessed historical operating status data is subjected to feature screening, and the obtained feature subset is used as the sample feature, and the constructed hidden danger identification model is trained based on the sample feature and the corresponding hidden danger type.

[0086] The hazard identification model is used to perform the following hazard type identification:

[0087] (1) judging whether there is an OLT uplink port according to the port networking type field of all ports aggregated by the home OLT device; if an OLT device has any port of the OLT uplink port type, the OLT device is judged as an OLT device without an uplink port, and the rest of the devices with OLT uplink ports are OLT devices with uplink ports;

[0088] (2) For each OLT device with an OLT uplink port, perform the following three situation analyses:

[0089] Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device;

[0090] Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports;

[0091] Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device;

[0092] (3) For an OLT device of the normal uplink port type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports of the port object with the same port rate, and verify whether the boards of all ports with the same rate belong to the same block. If they are the same block, the board belongs to the uplink single-board OLT device.

[0093] The hidden danger identification module is used to perform the following: collect the operation status data of the existing network and perform data preprocessing on the operation status data, perform feature screening based on the preprocessed operation status data to obtain a feature subset, predict the hidden danger type of the output OLT uplink board based on the feature subset and through the trained hidden danger identification model, and trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism.

[0094] As a specific implementation of the hidden danger identification module, based on the output results of the model, it is determined whether there are potential hidden dangers in the OLT uplink board, and the development trend of the hidden dangers is predicted. When the hidden danger of the OLT uplink board is identified, the early warning mechanism is triggered and the early warning information is sent to the operation and maintenance personnel. At the same time, suggestions for handling hidden dangers are provided to assist managers in quickly locating and resolving hidden dangers. Managers provide feedback and optimization to the model based on the actual processing results to improve the accuracy and stability of the machine learning model.

[0095] The risk assessment and prediction module is used to perform the following: conduct risk assessment on the identified hidden danger types and build response measures based on the assessment results.

[0096] Among them, when risk assessment is predicted, the response measures include strengthening monitoring, advance maintenance and replacing faulty boards.

[0097] The risk report decision module is used to perform the following: construct a hidden danger analysis report based on risk assessment results and response measures, and display the hidden danger analysis report.

[0098] Among them, the hidden danger analysis report presents the risk assessment results and response measures in the form of charts and images.

[0099] The system of this embodiment can execute the method disclosed in Example 1 by building an efficient machine learning model to conduct in-depth analysis of the real-time operation data of the OLT equipment, thereby realizing intelligent identification and prediction of potential hidden dangers of the common board.

[0100] The above is a detailed introduction to the OLT equipment uplink common board hidden danger analysis method and system provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for analyzing common board risks in an OLT device uplink, characterized in that: The steps include: Data collection: collect the operating status data of OLT equipment in real time, and perform data preprocessing on the collected operating status data to obtain the preprocessed operating status data. The operating status data includes key performance indicators related to the operating status and network elements whose life cycle status field is non-de-networked and whose network element type field is OLT. Among them, the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink; Feature screening: Extract port attribute features based on pre-processed running status data. Port attribute features include port lifecycle status features, port board features, port rate features, and port networking types. For all ports with the same rate, construct a feature subset with the port attribute features corresponding to the ports belonging to the same board. Model construction: constructing a hidden danger identification model based on a decision tree algorithm and training the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features; Hidden danger identification: Collect the operation status data of the existing network and perform data preprocessing on the operation status data. Perform feature screening based on the preprocessed operation status data to obtain a feature subset. Based on the feature subset and the trained hidden danger identification model, predict the hidden danger type of the output OLT uplink board. Trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism. Risk assessment and prediction: Conduct risk assessment on the identified hidden danger types and build response measures based on the assessment results; Risk report decision: Build a hidden danger analysis report based on risk assessment results and response measures, and display the hidden danger analysis report.

2. The OLT device uplink common board hidden danger analysis method according to claim 1, characterized in that: When the collected operating status data is preprocessed, the operating status data is cleaned, denoised and standardized through data preprocessing.

3. The OLT device uplink common board hidden danger analysis method according to claim 1, characterized in that: When training the hidden danger identification model, the historical operating status data of the OLT equipment during failure and normal operation is obtained, and the hidden danger types corresponding to the historical operating status data are marked. The historical operating status data is preprocessed, and feature screening is performed on the preprocessed historical operating status data. The obtained feature subset is used as the sample feature, and the constructed hidden danger identification model is trained based on the sample features and the corresponding hidden danger types.

4. The OLT device uplink common board hidden danger analysis method according to claim 1, characterized in that: The hidden danger identification model is used to perform the following hidden danger type identification: Determine whether there is an OLT uplink port according to the port networking type field of all ports under the OLT device. If an OLT device has any port of the OLT uplink port type, the OLT device is determined to be an OLT device without an uplink port. The remaining devices with OLT uplink ports are OLT devices with uplink ports. For each OLT device with an OLT uplink port, the following three situations are analyzed: Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device; Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports; Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device; For OLT devices of the normal uplink port OLT device type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports with the same port rate of the port object, and verify whether the boards to which all ports with the same rate belong are the same. If they are the same, the board belongs to the uplink single-board OLT device.

5. The OLT device uplink common board hidden danger analysis method according to claim 1, characterized in that: When risk assessment and prediction are made, response measures include enhanced monitoring, advance maintenance, and replacement of faulty boards; when risk report decisions are made, the risk assessment results and response measures are presented in the form of charts and images in the hidden danger analysis report.

6. An OLT equipment uplink common board hidden danger analysis system, characterized in that: It includes data collection module, feature screening module, model building module, hidden danger identification module, risk assessment and prediction module and risk reporting decision module; The data collection module is used to perform the following: collect the operating status data of the OLT device in real time, and perform data preprocessing on the collected operating status data to obtain the preprocessed operating status data, wherein the operating status data includes key performance indicators related to the operating status and network elements whose life cycle status field is a non-de-networked state and whose network element type field is an OLT, wherein the key performance indicators include bandwidth utilization, packet loss rate, delay and error rate of the uplink; The feature screening module is used to perform the following: extracting port attribute features based on the preprocessed running status data, the port attribute features include port life cycle status features, port board features, port rate features and port networking types, and for all ports with the same rate, constructing a feature subset of the port attribute features corresponding to the ports belonging to the same board; The model building module is used to perform the following: construct a hidden danger identification model based on a decision tree algorithm and perform model training on the hidden danger identification model to obtain a trained hidden danger identification model, wherein the hidden danger identification model is used to predict the hidden danger type of the output OLT uplink common board based on the input features; The hidden danger identification module is used to perform the following: collect the operation status data of the existing network and perform data preprocessing on the operation status data, perform feature screening based on the preprocessed operation status data to obtain a feature subset, predict the hidden danger type of the output OLT uplink board through the trained hidden danger identification model based on the feature subset, and trigger the early warning mechanism based on the output hidden danger type, and push the early warning information to the relevant operation and maintenance personnel through the early warning mechanism; The risk assessment and prediction module is used to perform the following: conduct risk assessment on the identified hidden danger types and construct response measures based on the assessment results; The risk report decision module is used to perform the following: construct a hidden danger analysis report based on risk assessment results and response measures, and display the hidden danger analysis report.

7. The OLT equipment uplink common board hidden danger analysis system according to claim 6, characterized in that: When the collected running status data is preprocessed, the data collection module is used to clean, denoise and standardize the running status data through data preprocessing.

8. The OLT equipment uplink common board hidden danger analysis system according to claim 6, characterized in that: When training the hidden danger identification model, the model construction module is used to call the data acquisition module to obtain the historical operating status data of the OLT equipment during failure and normal operation, and to mark the hidden danger types corresponding to the historical operating status data, perform data preprocessing on the historical operating status data, and call the feature screening module to perform feature screening on the preprocessed historical operating status data, so as to obtain the feature subset as the sample feature, which is used to train the constructed hidden danger identification model based on the sample features and the corresponding hidden danger types.

9. The OLT equipment uplink common board hidden danger analysis system according to claim 6, characterized in that: The hidden danger identification model is used to perform the following hidden danger type identification: Determine whether there is an OLT uplink port according to the port networking type field of all ports under the OLT device. If an OLT device has any port of the OLT uplink port type, the OLT device is determined to be an OLT device without an uplink port. The remaining devices with OLT uplink ports are OLT devices with uplink ports. For each OLT device with an OLT uplink port, the following three situations are analyzed: Case 1: If the resource identifiers of the boards to which all ports of the OLT device belong are all empty or cannot be associated with the board object, the potential risk type of the current OLT device is: none of the upstream ports belong to the board OLT device; Case 2: If the port rate fields of all port objects are empty, the potential risk type of the current OLT device is: there is no rate OLT device on the uplink ports; Case 3: If neither case 1 nor case 2 exists, the current OLT device type is: normal uplink port OLT device; For OLT devices of the normal uplink port OLT device type, analyze the ports whose port networking type field is the OLT uplink port, aggregate the ports with the same port rate of the port object, and verify whether the boards to which all ports with the same rate belong are the same. If they are the same, the board belongs to the uplink single-board OLT device.

10. The OLT equipment uplink common board hidden danger analysis system according to claim 6, characterized in that: When risk assessment and prediction are made, response measures include enhanced monitoring, advance maintenance, and replacement of faulty boards; when risk report decisions are made, the risk assessment results and response measures are presented in the form of charts and images in the hidden danger analysis report.

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