A method and system for automatic fault detection and recovery of FTTR network

Abstract

The present application relates to a method and system for automatic fault detection and recovery of FTTR networks, which belongs to the field of network communication technology. The method includes: constructing a FTTR network topology based on the network nodes in each room; collecting network connection quality data of each network node; calculating the current state parameters of each network node and performing abnormal analysis; marking network nodes with abnormal current state parameters as potential abnormal nodes, and determining the corresponding abnormal level; marking potential abnormal nodes whose abnormal level exceeds the preset level threshold as current fault nodes; performing fault mode analysis on the current fault node to determine the fault source; performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path; obtaining recovery result feedback information and updating it to the node fault history database. The present application can realize efficient and accurate fault detection and recovery in FTTR networks, and improve the stability and reliability of the network.

Description

Technical Field

[0001] The present application relates to the field of network communication technology, and in particular to a method and system for automatic fault detection and recovery of an FTTR network. Background Art

[0002] With the popularity of smart homes and high-bandwidth applications, FTTR (Fiber to the Room) network technology has gradually become an important solution for achieving high-speed network connections in home and commercial environments. However, in actual applications, the operation of FTTR networks faces a variety of technical challenges, especially in terms of network stability and fault handling.

[0003] At present, in traditional FTTR networks, the number and types of network nodes are increasing, and the wireless access points and other network devices deployed by users in various rooms form a complex network topology. Due to the diversity of equipment and different working environments, the quality of network connection is easily affected by many factors, such as signal interference, equipment failure, or abnormalities in the upstream network. These problems cause users to experience poor experiences such as connection interruption and speed drop when using the network. In addition, when a network failure occurs, it is often difficult for users to quickly locate the problem, resulting in prolonged network recovery time and affecting normal use. Therefore, how to achieve efficient and accurate fault detection and recovery in FTTR networks and improve network stability and reliability has become an urgent problem to be solved. Summary of the invention

[0004] In order to improve the stability and reliability of the network, the present application provides a FTTR network automatic fault detection and recovery method and system.

[0005] In the first aspect, the present application provides a method for automatic fault detection and recovery of an FTTR network, which adopts the following technical solution:

[0006] A method for automatic fault detection and recovery of an FTTR network, the method comprising:

[0007] Build FTTR network topology based on network nodes in each room;

[0008] Collect network connection quality data of each network node in real time;

[0009] Calculating the current state parameters of each network node according to the network connection quality data;

[0010] Based on the node historical status database, respectively perform abnormal analysis on the current status parameters of each of the network nodes to obtain abnormal analysis results;

[0011] According to the abnormality analysis result, the network node with abnormal current state parameters is marked as a potential abnormal node, and the abnormality level corresponding to the potential abnormal node is determined;

[0012] Marking the potential abnormal node whose abnormal level exceeds the preset level threshold as a current fault node;

[0013] Based on the node fault history database and the FTTR network topology, a fault mode analysis is performed on the current fault node to determine the corresponding fault source;

[0014] Based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path;

[0015] Obtain recovery result feedback information and update it to the node failure history database.

[0016] By adopting the above technical solutions, in the home network and smart home environment, the network connection quality data of the network nodes in each room are collected in real time, and the anomaly analysis is performed in combination with the historical status database, thereby realizing fine-grained hierarchical detection of network nodes; in the fault analysis process, the historical fault data of the node is used to accurately locate the source of the fault based on the FTTR network topology structure, which can identify the root cause of the problem more quickly and accurately than the existing technology, and improve the fault repair response speed; after the fault is detected, the backup path is selected through the topology structure to ensure that the data flow automatically bypasses the faulty node and switches to an alternative path, thereby realizing automatic detection and recovery of the faulty node, effectively reducing the impact of the fault on the user experience, and improving the reliability and stability of the network.

[0017] Optionally, based on the node fault history database and the FTTR network topology, the step of performing a fault mode analysis on the current fault node to determine the corresponding fault source includes:

[0018] Building and training a fault analysis model based on the node fault history database;

[0019] Determine an upstream node of the current faulty node based on the FTTR network topology;

[0020] Obtaining current state parameters of the current faulty node and the upstream node, and inputting them into the fault analysis model for fault analysis, and identifying fault characteristics of the current faulty node and the upstream node;

[0021] Determine whether the upstream node has a fault characteristic;

[0022] If not, determining that the fault source is the current fault node;

[0023] If so, determining each downstream node corresponding to the upstream node based on the FTTR network topology;

[0024] Acquire current state parameters of each of the downstream nodes, and input them into the fault analysis model to perform fault analysis and identify fault characteristics of the downstream nodes;

[0025] Counting the proportion of downstream nodes with fault characteristics among the downstream nodes, and if the proportion is greater than a preset proportion threshold, determining that the fault source is the upstream node;

[0026] If the ratio is less than a preset ratio threshold, then calculating the similarity of the fault characteristics of the current fault node and the upstream node;

[0027] If the fault feature similarity is greater than a preset similarity threshold, determining that the fault source is the upstream node;

[0028] If the fault feature similarity is less than a preset similarity threshold, it is determined that the fault source includes the current fault node and the upstream node.

[0029] By adopting the above technical solution, a fault analysis model based on historical data is established, combined with the FTTR network topology, the state of the faulty node is gradually analyzed and judged, and the source of the fault can be accurately located. This technical solution not only improves the efficiency and accuracy of fault detection, but also avoids misjudgment by comprehensively analyzing the state of upstream and downstream nodes, ensuring the high availability and stability of the network. By combining proportional statistics and similarity analysis, a multi-level judgment basis is provided for determining the source of the fault. Proportional statistics provide a quantitative assessment of the fault range, and similarity analysis further confirms the attribution of the fault source. This logically clear and systematic judgment method ensures the accurate positioning of the fault source in a complex network environment, thereby significantly improving the fault response capability and making network management more intelligent and automated.

[0030] Optionally, the step of constructing and training a fault analysis model based on the node fault history database includes:

[0031] Based on the node fault history database, a historical sample fault feature set is obtained and preprocessed; wherein the historical sample feature set includes node historical state parameters and corresponding fault feature labels;

[0032] Perform feature extraction on the preprocessed historical sample feature set to obtain a feature vector set;

[0033] Dividing the feature vector set into training samples and verification samples;

[0034] Inputting the training samples into a pre-built random forest model for initial training, optimizing model parameters, and obtaining an initially trained fault analysis model;

[0035] The fault analysis model is verified and model parameters are corrected based on the verification samples until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining the trained fault analysis model.

[0036] By adopting the above technical solutions, the fault analysis model built and trained based on the node fault history database can effectively learn fault characteristics from historical data, automatically identify potential fault sources, and improve the accuracy and efficiency of fault detection. This model is not only highly adaptable and can handle fault conditions in a variety of network environments, but also provides important support for subsequent fault recovery and network optimization.

[0037] Optionally, when the fault source is the upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0038] Determine each downstream node of the upstream node based on the FTTR network topology structure, and obtain a current network path of the upstream node; wherein the current network path includes a network connection path between the upstream node and each downstream node;

[0039] Obtaining the historical average traffic demand of the upstream node;

[0040] Determine the adjacent upstream nodes of the upstream node based on the FTTR network topology structure, and obtain network status parameters of each of the adjacent upstream nodes;

[0041] Performing traffic carrying analysis and performance evaluation analysis on each adjacent upstream node according to the network status parameters to obtain traffic carrying results and performance evaluation results of each adjacent upstream node;

[0042] Filter the adjacent upstream nodes whose traffic carrying results meet the historical average traffic demand to obtain a list of candidate adjacent upstream nodes;

[0043] sorting the candidate adjacent upstream node list according to the performance evaluation result, and selecting the adjacent upstream node with the best performance evaluation result in the candidate adjacent upstream node list as a backup adjacent upstream node;

[0044] Using the network connection path between the backup adjacent upstream node and each of the downstream nodes as the backup network path;

[0045] The data flow of the upstream node is switched to the backup adjacent upstream node.

[0046] By adopting the above technical solution, when the fault source is an upstream node, efficient backup path switching is achieved through analysis and selection of the adjacent upstream nodes of the upstream node, thereby ensuring that when an upstream node fails, network services are quickly restored, reducing the impact on other downstream nodes, and improving the connection stability and user experience of home smart devices.

[0047] Optionally, the step of obtaining recovery result feedback information and updating the node fault history database includes: receiving recovery result feedback information of the current faulty node and the downstream node, and uploading the information to the node fault history database.

[0048] By adopting the above technical solution, after the data flow of the upstream node is switched to the backup adjacent upstream node, the current faulty node and the downstream nodes are switched to connect to the backup adjacent upstream node. Therefore, by obtaining the network recovery results of the current faulty node and its downstream nodes, the real-time and effectiveness of network fault processing are ensured.

[0049] Optionally, when the fault source is the current fault node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0050] Determine the current network path of the current fault node based on the FTTR network topology structure; wherein the current network path includes a network connection path between an upstream node of the current fault node and the current fault node;

[0051] Obtaining the historical average traffic demand of the current faulty node;

[0052] Determine each downstream node corresponding to the upstream node based on the FTTR network topology structure, and obtain network status parameters of each downstream node;

[0053] Performing traffic carrying analysis and performance evaluation analysis on each downstream node according to the network status parameters to obtain traffic carrying results and performance evaluation results of each downstream node;

[0054] Filter the downstream nodes whose traffic carrying results meet the historical average traffic demand to obtain a candidate downstream node list;

[0055] Sorting the candidate downstream node list according to the performance evaluation result, and selecting the downstream node with the best performance evaluation result in the candidate downstream node list as a standby downstream node;

[0056] Using the network connection path between the backup downstream node and the upstream node as the backup network path;

[0057] The data flow of the current failed node is switched to the standby downstream node.

[0058] By adopting the above technical solution, when the fault source is the current fault node, the system can quickly identify the appropriate backup path according to the location of the current fault node in the FTTR network topology, and effectively switch traffic, thereby ensuring the continuity and reliability of network services. This structured fault handling method not only improves the fault response speed, but also optimizes the utilization of network resources and improves the overall network experience of users through the analysis of historical data and real-time monitoring.

[0059] Optionally, when the fault source includes the current fault node and the upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0060] Determine the current network path between the faulty node and the upstream node based on the FTTR network topology structure; wherein the current network path includes the network connection path between the current faulty node and the upstream node;

[0061] Obtaining the historical average traffic demand of the current network path;

[0062] Determine each adjacent upstream node of the upstream node based on the FTTR network topology structure, and determine the corresponding adjacent downstream node according to the adjacent upstream node to obtain each adjacent network path; wherein the adjacent network path includes a network connection path between the adjacent upstream node and the corresponding adjacent downstream node;

[0063] Acquire network status parameters of each of the adjacent network paths, and perform traffic carrying analysis and performance evaluation analysis on each of the adjacent network paths to obtain traffic carrying results and performance evaluation results of each of the adjacent network paths;

[0064] Filtering adjacent network paths whose traffic carrying results meet the historical average traffic demand to obtain a list of candidate adjacent network paths;

[0065] Sorting the candidate adjacent network path list according to the performance evaluation result, and taking the adjacent network path with the best performance evaluation result in the candidate adjacent network path list as the backup network path;

[0066] The data flow of the current network path between the faulty node and the upstream node is switched to the backup network path.

[0067] By adopting the above technical solution, when both the current faulty node and the upstream node are faulty, the network path of the current faulty node and its upstream node is identified, and the historical traffic demand and the status evaluation of the adjacent network path are combined to ensure that when a fault occurs, it can quickly switch to a suitable backup network path, which not only improves the reliability of the network, but also enhances the efficiency of fault recovery and minimizes the impact on user experience.

[0068] Optionally, the step of obtaining recovery result feedback information and updating the node fault history database includes: generating recovery result feedback information according to the network switching result, and updating the node fault history database.

[0069] By adopting the above technical solution, when the fault source is only the current faulty node or includes the current faulty node, the current faulty node needs to be switched to other downstream nodes during recovery. After the switching is completed, there is no need to obtain the recovery result of the current faulty node. The success or failure of the switching operation can be determined directly by obtaining the network switching result, and then the switching result is updated to the node fault history database, thereby realizing systematic management of network fault processing.

[0070] Optionally, after the step of marking the potential abnormal node whose abnormal level exceeds a preset level threshold as a current fault node, the method further includes:

[0071] Acquire the household appliance corresponding to the current faulty node according to the current faulty node;

[0072] Obtaining working status parameters of the household appliance;

[0073] Determining whether the household appliance has a fault according to the working status parameter;

[0074] If there is no fault, keep the mark of the current faulty node;

[0075] If there is a fault, a maintenance prompt message is sent to the user terminal according to the location information of the home appliance;

[0076] Receiving the household appliance recovery information fed back by the user terminal, performing fault analysis on the current fault node, and determining whether there is a fault feature;

[0077] If yes, keep the mark of the current faulty node;

[0078] If not, the mark of the current faulty node is cancelled.

[0079] By adopting the above technical solutions, the system can effectively distinguish between network failures and home appliance failures, and quickly and accurately locate the source of the problem through status monitoring, feature matching, and cross-validation, thereby formulating targeted recovery strategies and improving the reliability and user experience of the smart home system.

[0080] In the second aspect, the present application provides an FTTR network automatic fault detection and recovery system, which adopts the following technical solution:

[0081] A FTTR network automatic fault detection and recovery system, used to execute the FTTR network automatic fault detection and recovery method according to the first aspect, the system comprising:

[0082] A network building module, used to build the FTTR network topology based on the network nodes in each room;

[0083] Network data collection module, used to collect network connection quality data of each network node in real time;

[0084] A state parameter calculation module, used to calculate the current state parameter of each network node according to the network connection quality data;

[0085] An abnormality analysis module is used to perform abnormality analysis on the current state parameters of each network node based on the node historical state database to obtain abnormality analysis results;

[0086] An abnormal node marking module is used to mark the network node with abnormal current state parameters as a potential abnormal node according to the abnormal analysis result, and determine the abnormal level corresponding to the potential abnormal node;

[0087] A fault node marking module, used to mark the potential abnormal node whose abnormal level exceeds a preset level threshold as a current fault node;

[0088] A fault mode analysis module, used to perform a fault mode analysis on the current fault node based on a node fault history database and the FTTR network topology to determine a corresponding fault source;

[0089] A backup network switching module, configured to detect a backup network path based on the FTTR network topology and the current network path of the fault source, and switch the current network path of the fault source to the backup network path;

[0090] The recovery feedback information updating module is used to obtain the recovery result feedback information and update it to the node failure history database.

[0091] In summary, the present application includes at least one of the following beneficial technical effects: utilizing historical node fault data to accurately locate the source of the fault based on the FTTR network topology structure, which can identify the root cause of the problem more quickly and accurately than the existing technology, and improve the fault repair response speed; after detecting a fault, an alternative path is selected through the topology structure to ensure that the data flow automatically bypasses the faulty node and switches to an alternative path, thereby realizing automatic detection and recovery of the faulty node, effectively reducing the impact of the fault on the user experience, and improving the reliability and stability of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 This is a first flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application.

[0093] Figure 2 This is a second flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application.

[0094] Figure 3 This is a third flow chart of an FTTR network automatic fault detection and recovery method according to one of the embodiments of the present application.

[0095] Figure 4 This is a fourth flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application.

[0096] Figure 5 This is a fifth flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application.

[0097] Figure 6 This is a sixth flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application.

[0098] Figure 7 This is the seventh flow chart of a method for automatic fault detection and recovery of an FTTR network according to one of the embodiments of the present application. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0100] With the rapid development of smart homes and the Internet of Things, FTTR (Fiber To The Room) networks are widely used in home and office environments due to their high bandwidth and low latency. However, frequent network failures have seriously affected user experience and the normal operation of devices.

[0101] Based on this, an embodiment of the present application discloses a method for automatic fault detection and recovery of an FTTR network.

[0102] Reference Figure 1 , a FTTR network automatic fault detection and recovery method, comprising:

[0103] Step S101, constructing a FTTR network topology structure based on the network nodes in each room;

[0104] Among them, the FTTR (Fiber to the Room) network usually contains multiple network nodes (such as wireless access points or fiber optic terminals) distributed in various rooms. Through the network node information of each room, a complete network topology structure can be established, that is, the connection relationship between each node.

[0105] For example, in a home environment, network nodes are set up in the living room, bedroom and study respectively, and each network node is connected to the central router. The system forms a home network topology by recording the connection status and position relationship of each network node.

[0106] It is understandable that building the FTTR network topology provides basic information for subsequent detection of the connection status of each node, discovery of potential anomalies, and path switching. Through the precise network structure, the system can quickly locate the nodes in each room and their upstream relationships, improving the accuracy of fault location and path switching.

[0107] Step S102, collecting network connection quality data of each network node in real time;

[0108] Among them, the network connection quality data of each network node, such as signal strength, delay and jitter, is monitored in real time to continuously grasp the network operation status. These data are the basis for judging the health status of the network and discovering anomalies. For example, the network node in a room records the signal strength, delay and jitter every 1 second, and these data are transmitted to the central controller through the network. If the signal strength at a certain moment is lower than the preset signal threshold, the system records the node as having poor signal quality.

[0109] Step S103, calculating the current state parameters of each network node according to the network connection quality data;

[0110] Among them, by processing the collected network connection quality data, the system calculates the current state parameters of each node, such as average signal strength, average delay, etc. This parameter can simplify the data and make it easier for the system to judge the node status. For example, assuming that in the past 10 seconds, the average signal strength of the network node in a room is -60dBm and the average delay is 50ms, the system sets the current state parameters of the node to these two values ​​for comparison with historical data.

[0111] It is understandable that by calculating the current status parameters, complex real-time data can be converted into concise indicators, which makes it easier for the system to quickly analyze whether the node is operating normally, helping to shorten the time for fault diagnosis.

[0112] Step S104, based on the node historical status database, respectively perform abnormal analysis on the current status parameters of each network node to obtain abnormal analysis results;

[0113] The system compares the current state parameters of each node with the data in the historical state database, calculates the difference from the historical average value, and determines whether the current parameters are abnormal. For example, if the historical average delay of a network node in a room is 30ms, and the current delay is 50ms, the system will determine that the delay is abnormal and mark the node as a potential abnormal node.

[0114] Step S105, according to the abnormality analysis result, marking the network nodes with abnormal current state parameters as potential abnormal nodes, and determining the abnormality level corresponding to the potential abnormal nodes;

[0115] Specifically, based on the results of the abnormality analysis, the system will mark the network nodes that have detected abnormalities as "potential abnormal nodes" and set the level of potential abnormal nodes according to the degree of abnormality of the abnormal parameters (such as the size of the difference value) so that different fault handling solutions can be adopted later. For example, if the delay parameter of the network node in a room is higher than the historical average by 30%, it will be marked as "mild abnormality"; if it is higher than 50%, it will be marked as "severe abnormality".

[0116] It is understandable that by setting abnormal nodes to different levels, such as mild abnormality (network fluctuations), moderate (network interruption) and severe (complete network connection loss), the system can more effectively determine the severity of the fault so as to take appropriate recovery measures. This grading mechanism ensures that fault handling resources are reasonably allocated.

[0117] Step S106, marking a potential abnormal node whose abnormality level exceeds a preset level threshold as a current fault node;

[0118] Among them, when the abnormal level of a potential abnormal node exceeds the preset threshold, the system will identify it as a current fault node and make it the direct object of subsequent fault processing. For example, if the delay abnormality of a network node in a room exceeds the severe abnormality threshold of 50%, the system will mark it as a "current fault node".

[0119] It should be noted that the preset level threshold can be pre-set according to the actual situation. By setting the threshold, the faulty nodes that need to be dealt with immediately can be accurately screened out to ensure that the most serious network problems are solved first when resources are limited.

[0120] Step S107, based on the node fault history database and the FTTR network topology, perform a fault mode analysis on the current fault node to determine the corresponding fault source;

[0121] The fault source includes the current fault node or the upstream node of the current fault node;

[0122] In some embodiments, the system can use the fault history database to pre-establish a fault analysis model, and analyze the fault mode of the faulty node in combination with the FTTR network topology to determine whether the fault source is in the current faulty node or its upstream node. Since the upstream node failure may cause other downstream nodes to fail to work properly, it is necessary to distinguish the fault source in the current node and the upstream node to improve the repair efficiency and avoid resource waste caused by misjudgment. For example, if the system detects similar abnormal patterns on the network node and the upstream node (router) in a room, it may be a fault in the upstream node.

[0123] Step S108, based on the FTTR network topology, perform backup network path detection according to the current network path of the fault source, and switch the current network path of the fault source to the backup network path;

[0124] After the fault source is confirmed, the system detects feasible backup paths based on the network topology to ensure that data flows can maintain connections through alternative paths, thereby providing an alternative solution for fault recovery and ensuring that network services can be restored in a timely manner when a node fails, thereby reducing the impact on users. For example, if a network node in a room fails and is identified as the source of the fault, the system will detect the connection between other adjacent nodes and the central routing to determine whether there is a feasible alternative path.

[0125] It should be noted that when determining the backup network paths, it is also necessary to perform performance evaluation on all backup network paths. By using the collected network status parameters (such as bandwidth, delay, load, etc.) for analysis and comparing the performance data of each backup network path, the backup network paths that can meet the traffic requirements and have good performance are preferentially selected based on the performance evaluation results.

[0126] Furthermore, once the backup path is determined, the system will switch the network connection of the fault source to the backup network path to restore the normal operation of the network. This process can be achieved through dynamic routing or redirection technology. By automatically switching to the backup path, the system can quickly restore the network connection and improve the user experience.

[0127] In some specific embodiments, if the fault source is the upstream node of the current faulty node, then when switching the backup network path, the data flow of the current faulty node can be switched from the faulty upstream router node path to the network path of other adjacent upstream router nodes in normal state, so that the data is transmitted to the core network through the alternative path. If the network of the upstream node of the current faulty node is normal, the network of other downstream nodes of the upstream node is also normal, and the other downstream nodes are all connected to the upstream node normally, it indicates that the fault source is the current faulty node, and when switching the backup network path, the data flow can be switched from the current faulty node to other downstream nodes in normal state, which can be achieved by setting a dynamic routing policy or changing the network configuration.

[0128] In addition, if both the current faulty node and the upstream node are faulty, the fault source includes both the current faulty node and the upstream node. When switching the backup network path, the data flow can be switched from the current faulty node to other normal downstream nodes to ensure that the user's network experience is not affected. At the same time, the data flow of the upstream node can be switched to other normal routers or switches to prevent its fault from affecting other parts of the network.

[0129] Step S109, obtaining recovery result feedback information and updating it to the node failure history database.

[0130] Specifically, after the network path is switched, recovery result feedback information is obtained, such as the fault source location, fault level, switching path, network recovery result, recovery time, etc., and this information is recorded in the node fault history database for updating historical fault records to form a data closed loop, so as to facilitate preventive maintenance of future network failures and improve the system's fault tracing and analysis capabilities.

[0131] In the above implementation, in the home network and smart home environment, the network connection quality data of the network nodes in each room are collected in real time, and the anomaly analysis is performed in combination with the historical status database, thereby realizing fine-grained hierarchical detection of network nodes; in the fault analysis process, the node historical fault data is used to accurately locate the source of the fault based on the FTTR network topology structure, which can more quickly and accurately identify the root cause of the problem and improve the fault repair response speed compared to the existing technology; after the fault is detected, the backup path is selected through the topology structure to ensure that the data flow automatically bypasses the faulty node and switches to an alternative path, thereby realizing automatic detection and recovery of the faulty node, effectively reducing the impact of the fault on the user experience, and improving the reliability and stability of the network.

[0132] In addition, the recovery result feedback information finally uploaded provides data support for subsequent fault prediction and mode optimization, enabling the system to have the potential for self-learning and optimization, thus ensuring the long-term stability of the FTTR network.

[0133] Reference Figure 2 As an implementation method of step S107, based on the node fault history database and the FTTR network topology structure, the steps of performing a fault mode analysis on the current fault node and determining the corresponding fault source include:

[0134] Step S201, building and training a fault analysis model based on a node fault history database;

[0135] Specifically, we use the data in the node fault history database to build a machine learning model to identify fault characteristics and patterns. By training the model, the system can automatically learn and adapt to various fault conditions. By providing a data-driven analysis tool, we can effectively identify potential fault sources and improve the accuracy of fault detection.

[0136] For example, using historical failure data, the model can learn the failures that are often accompanied by packet loss or connection interruption when the network delay exceeds a certain threshold.

[0137] Step S202, determining an upstream node of a current faulty node based on the FTTR network topology;

[0138] Among them, the upstream node refers to the node located on the data transmission path in the data flow, which is responsible for sending data to the downstream node. Since the status of the upstream node may directly affect the downstream node, the direct upstream node of the current fault node can be identified according to the FTTR network topology to facilitate subsequent fault source analysis. For example, in a three-layer network, the current fault node is located in room A, and its upstream node may be router B.

[0139] Step S203, obtaining current state parameters of the current faulty node and the upstream node, and inputting them into the fault analysis model for fault analysis, and identifying fault features of the current faulty node and the upstream node;

[0140] Specifically, the status parameters of the current node and upstream nodes, such as delay and packet loss rate, are monitored in real time, and these parameters are input into the fault analysis model for analysis to identify fault characteristics.

[0141] Step S204, determine whether the upstream node has fault characteristics; if not, jump to step S205; if yes, jump to step S206;

[0142] The direction of subsequent analysis is determined by quickly identifying the status of the upstream node; for example, if the model determines that the delay of the upstream node exceeds the normal range, it is considered that the upstream node has fault characteristics.

[0143] Step S205, determining that the fault source is the current fault node;

[0144] Specifically, if the upstream node is normal, the fault source is directly determined to be the current faulty node. By quickly locking the fault source, subsequent analysis steps are reduced and response efficiency is improved.

[0145] Step S206, determining each downstream node corresponding to the upstream node based on the FTTR network topology structure;

[0146] Among them, the downstream node refers to the node that receives data from the upstream node, which is usually the node closer to the user device; if there is an abnormality in the upstream node, all downstream nodes corresponding to the upstream node are further analyzed to further determine the impact range of the upstream node and determine whether the current faulty node is caused by the abnormality of the upstream node.

[0147] Step S207, obtaining current state parameters of each downstream node, and inputting them into the fault analysis model to perform fault analysis and identify fault characteristics of the downstream node;

[0148] Specifically, the status parameters of all downstream nodes are obtained and input into the model for fault analysis, providing data support for subsequent fault source determination;

[0149] Step S208, counting the proportion of downstream nodes with fault characteristics among the downstream nodes;

[0150] Step S209, determining whether the ratio is greater than a preset ratio threshold, if so, jumping to step S210; if not, jumping to step S211;

[0151] Specifically, the proportion of nodes with fault characteristics in all downstream nodes is calculated, and by quantifying the fault impact of upstream nodes, it is helpful to make a more accurate fault source judgment. Among them, the preset proportion threshold can be pre-configured and adjusted according to actual conditions, for example, it can be set to 50%.

[0152] Step S210, determining that the fault source is an upstream node;

[0153] In some embodiments, if 4 out of 5 downstream nodes display fault characteristics (such as delays and packet loss, etc.), the ratio is 80%, which exceeds the preset ratio threshold. It can be determined that the fault of the upstream node has a wide impact, and the upstream node can be determined to be the source of the fault.

[0154] Step S211, calculating the similarity of fault characteristics between the current fault node and the upstream node;

[0155] Among them, the fault feature similarity can be based on the calculation of state parameters (such as delay, packet loss, etc.) to reflect whether the fault manifestations of two nodes are consistent.

[0156] It can be understood that if the abnormal proportion of downstream nodes is less than the preset proportion threshold, it is inferred that the upstream node may have a local fault or an occasional fault, which has not affected most of the downstream nodes. Therefore, the fault source is further confirmed by calculating the similarity of the fault characteristics between the current faulty node and the upstream node.

[0157] Step S212, determine whether the fault feature similarity is greater than a preset similarity threshold; if so, jump to step S210; if not, jump to step S213;

[0158] Specifically, the preset similarity threshold can be pre-configured and adjusted according to the actual situation. If the fault characteristics of the current fault node are similar to those of the upstream node, it can be determined that the current fault node is affected by the upstream node, and therefore the upstream node is the fault source. For example, assuming that the similarity of the fault characteristics of the two is calculated to be 75%, and the set similarity threshold is 70%, it can be determined that the upstream node is the fault source.

[0159] It can be understood that similarity analysis ensures that the judgment of the fault source does not only rely on absolute data, but also considers the similarity of fault manifestations between different nodes, making the analysis more comprehensive and accurate, and improving the reliability of fault source identification.

[0160] Step S213: determine that the fault source includes the current faulty node and the upstream node.

[0161] Specifically, if the abnormal proportion of downstream nodes does not exceed the threshold, and the similarity of the fault characteristics of the current faulty node and the upstream node does not exceed the threshold, this means that both the current faulty node and the upstream node may be the source of the fault. Through this dual judgment mechanism, the fault source can be located more accurately, avoiding misjudgment and missed judgment, and ensuring the reliability of the network.

[0162] In the above implementation, a fault analysis model based on historical data is established, combined with the FTTR network topology, and the state of the faulty node is gradually analyzed and judged, so that the source of the fault can be accurately located. This technical solution not only improves the efficiency and accuracy of fault detection, but also avoids misjudgment by comprehensively analyzing the state of upstream and downstream nodes, ensuring the high availability and stability of the network. By combining proportional statistics and similarity analysis, a multi-level judgment basis is provided for determining the source of the fault. Proportional statistics provide a quantitative assessment of the fault range, and similarity analysis further confirms the attribution of the fault source. This logically clear and systematic judgment method ensures the accurate positioning of the fault source in a complex network environment, thereby significantly improving the fault response capability and making network management more intelligent and automated.

[0163] It should be noted that when a local fault occurs in an upstream node, such as a configuration error, temporary network congestion, or hardware problem, these problems may not affect the downstream node; for example, if the upstream router temporarily loses packets due to overload, but the connection of the downstream node is normal, the downstream node may not show any abnormal characteristics. In addition, the fault characteristics of the upstream node may also be intermittent, such as network interference or unstable signals, resulting in occasional abnormalities. If the downstream node is in a normal state during monitoring, the downstream node will not show abnormalities. Special situations similar to the above are a challenge for network management and fault detection. Since they may lead to misjudgment and neglect of downstream nodes, in order to accurately diagnose the source of the fault, the present application can detect and analyze this situation and combine multiple monitoring indicators to comprehensively evaluate the network status.

[0164] Reference Figure 3 As an implementation of step S201, the steps of constructing and training a fault analysis model based on a node fault history database include:

[0165] Step S301, based on the node fault history database, obtain a historical sample fault feature set and perform preprocessing;

[0166] Among them, the historical sample feature set includes the node historical state parameters and the corresponding fault feature labels;

[0167] Specifically, relevant historical data are extracted from the node fault history database, including node status parameters (such as delay, packet loss rate, bandwidth, etc.) and corresponding fault feature labels (such as normal, fault type, etc.).

[0168] In addition, the preprocessing step is mainly to clean the data, remove noise and outliers, ensure that the samples used for training are of high quality and accuracy, and lay a good foundation for subsequent feature extraction and model training.

[0169] Step S302, extracting features from the preprocessed historical sample feature set to obtain a feature vector set;

[0170] Among them, key features are extracted from the cleaned data to form feature vectors. This step can determine important features through statistical analysis, correlation analysis and domain knowledge.

[0171] For example, by analyzing the node status parameters, "network delay", "packet loss rate" and "bandwidth utilization rate" are identified as important features to form a feature vector, for example: feature vector = [delay, packet loss rate, bandwidth utilization rate]. By extracting effective feature vectors, the model's ability to predict faults can be enhanced, enabling it to better identify potential faults.

[0172] Step S303, dividing the feature vector set into training samples and verification samples;

[0173] Specifically, the feature vector set is divided into different data sets to ensure the effectiveness of model training, verification and final testing. Generally, 70% are used as training samples and 30% are used as verification samples. By reasonably dividing the data set, overfitting can be effectively avoided and the generalization ability of the model on unseen data can be ensured.

[0174] Step S304, inputting the training samples into a pre-built random forest model for initial training, optimizing the model parameters, and obtaining an initially trained fault analysis model;

[0175] Specifically, the random forest algorithm is used to perform preliminary training on the training samples. The random forest model improves the prediction accuracy by integrating multiple decision trees. By inputting the training samples into the model and randomly selecting features to construct multiple decision trees, the model can obtain the fault prediction results for each sample through a voting mechanism and optimize the model parameters based on the results, so that the model can learn the patterns in the data and thus improve its ability to identify faults.

[0176] In one of the embodiments of the present application, a random forest model is pre-constructed as the basis of the fault analysis model. The random forest model can help identify the key parameters that cause faults by analyzing the importance of features, provide a clear basis for fault analysis, and can be used for classification and regression tasks. It has strong adaptability and can be adjusted as needed to meet different analysis requirements.

[0177] Specifically, the model structure of the fault analysis model based on the random forest model in the embodiment of the present application mainly includes the following parts: a data output layer, which is used to receive preprocessed feature vectors, including state parameters of network nodes (such as delay, packet loss rate, bandwidth utilization, etc.); a feature selection mechanism, in which the random forest model randomly selects feature subsets during training to construct multiple decision trees, thereby increasing the diversity of the model and reducing correlation; a decision tree integration layer, which consists of multiple decision trees, each of which learns and predicts independently. Each decision tree is constructed based on random sampling and feature selection of its training set; a voting mechanism, in which each decision tree classifies or regresses the input sample, and finally obtains the prediction result of the overall model through voting (classification) or averaging (regression); an output layer, which is used to generate fault analysis results, including the fault prediction probability and fault type label of each node (such as "normal", "delay fault", "packet loss fault", etc.); model evaluation and optimization: the performance of the model is evaluated using cross-validation and validation sets, and parameter tuning (such as the number of trees, depth, etc.) is performed based on indicators such as the accuracy and recall rate of the model.

[0178] Among them, the random forest model was selected for fault analysis due to its high accuracy, noise resistance and interpretability. Its model structure constitutes an effective fault analysis system through data input layer, feature selection mechanism, decision tree integration layer, voting mechanism and output layer.

[0179] Step S305 , verifying the fault analysis model and correcting model parameters based on the verification sample until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining a trained fault analysis model.

[0180] The validation samples are used to evaluate the performance of the model and are adjusted according to the loss function. The iterative process is repeated until the preset conditions are met or the maximum number of iterations is reached. Through continuous validation and parameter correction, the performance consistency of the model on different data sets is ensured, thereby improving the robustness and accuracy of the model. For example, during the validation process, if the accuracy of the model on the validation samples is found to be less than 80%, the model parameters (such as the number and depth of trees) are adjusted and the model is retrained until the accuracy is improved.

[0181] In the above implementation, the fault analysis model built and trained based on the node fault history database can effectively learn fault characteristics from historical data, automatically identify potential fault sources, and improve the accuracy and efficiency of fault detection. This model is not only highly adaptable and can handle fault conditions in a variety of network environments, but also provides important support for subsequent fault recovery and network optimization.

[0182] Reference Figure 4As one implementation of step S108, when the fault source is an upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0183] Step S401, determining each downstream node of the upstream node based on the FTTR network topology structure, and obtaining the current network path of the upstream node;

[0184] The current network path includes the network connection path between the upstream node and each downstream node;

[0185] Specifically, the current network path of the upstream node is obtained to clarify the data flow direction and its impact range, providing a basis for subsequent traffic analysis and path switching.

[0186] Step S402, obtaining the historical average flow demand of the upstream node;

[0187] Among them, through historical data, the system can understand the traffic demand of upstream nodes during normal operation, which is the key to evaluating whether the backup path can meet the demand.

[0188] Step S403, determining the adjacent upstream nodes of the upstream node based on the FTTR network topology structure, and obtaining the network status parameters of each adjacent upstream node;

[0189] Among them, adjacent upstream nodes refer to other upstream nodes that are directly connected to the currently faulty upstream node. These nodes can also transmit data to downstream nodes and maintain connections with other upstream nodes. Specifically, network status parameters such as latency, bandwidth, packet loss rate, etc., can help with subsequent traffic carrying and performance evaluation analysis by fully understanding the status of adjacent upstream nodes.

[0190] Step S404, performing traffic carrying analysis and performance evaluation analysis on each adjacent upstream node according to the network state parameters, and obtaining traffic carrying results and performance evaluation results of each adjacent upstream node;

[0191] The selected backup path is determined to be able to support the traffic demand and meet the performance requirements based on the traffic carrying result and performance evaluation result of the adjacent upstream node.

[0192] Step S405, screening the adjacent upstream nodes whose traffic carrying results meet the historical average traffic demand, and obtaining a list of candidate adjacent upstream nodes;

[0193] Among them, the selection range is narrowed by screening the traffic carrying results, thereby improving the success rate after switching.

[0194] Step S406, sorting the candidate adjacent upstream node list according to the performance evaluation result, and selecting the adjacent upstream node with the best performance evaluation result in the candidate adjacent upstream node list as a backup adjacent upstream node;

[0195] The performance of the selected candidate adjacent upstream nodes is evaluated, including bandwidth utilization, delay, and packet loss rate, etc. The performance evaluation results are sorted and the best node is selected as the backup adjacent upstream node.

[0196] Step S407, using the network connection path between the backup adjacent upstream node and each downstream node as a backup network path;

[0197] It should be noted that the downstream node in the above steps refers to the downstream node corresponding to the currently faulty upstream node, and the backup network path here means that after the currently faulty upstream node is replaced with the backup adjacent upstream node, each downstream node needs to be connected to the backup adjacent upstream node to ensure that the traffic can be transmitted smoothly, thereby forming a backup network path.

[0198] Step S408: Switch the data flow of the upstream node to the backup adjacent upstream node.

[0199] After determining the backup network path, the data flow of the upstream node is switched to a new backup adjacent upstream node to ensure the continuity of the network service.

[0200] In the above implementation, when the fault source is an upstream node, efficient backup path switching is achieved by analyzing and selecting the adjacent upstream nodes of the upstream node, thereby ensuring that when an upstream node fails, network services are quickly restored, reducing the impact on other downstream nodes, and improving the connection stability and user experience of home smart devices.

[0201] Furthermore, as an implementation method of step S109, when the fault source is an upstream node, the step of obtaining recovery result feedback information and updating it to the node fault history database includes: receiving recovery result feedback information of the current faulty node and the downstream node, and uploading it to the node fault history database.

[0202] In the above implementation, after the data flow of the upstream node is switched to the backup adjacent upstream node, the current faulty node and the downstream nodes are switched to connect to the backup adjacent upstream node. Therefore, by obtaining the network recovery results of the current faulty node and its downstream nodes, the real-time and effectiveness of network fault processing are ensured.

[0203] It should be noted that recording this information in the node fault history database not only provides data support for subsequent fault analysis, but also provides an important basis for optimizing network management and improving network reliability. By continuously updating the database, the operation team can identify and solve potential network problems in advance and enhance the overall stability and reliability of the network.

[0204] Reference Figure 5 As another implementation of step S108, when the fault source is the current fault node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0205] Step S501, determining the current network path of the current fault node based on the FTTR network topology structure;

[0206] The current network path includes a network connection path between an upstream node of the current faulty node and the current faulty node;

[0207] Specifically, the topology of the FTTR (Fiber To The Room) network is used to identify the location of the current faulty node and its connected upstream node. For example, if the current faulty node is an access point (AP) in a room, its upstream node is the router connected to the AP.

[0208] Step S502, obtaining the historical average flow demand of the current fault node;

[0209] Specifically, the traffic demand of the current faulty node can be calculated by analyzing historical data. This information helps the subsequent steps to evaluate the traffic carrying capacity of the standby node. For example, the average traffic of the node in the past period of time can be calculated, that is, the total traffic divided by the length of the period.

[0210] Step S503, determining each downstream node corresponding to the upstream node based on the FTTR network topology structure, and obtaining the network status parameters of each downstream node;

[0211] Among them, by tracing back from the upstream node, the downstream nodes directly connected to it are identified, and the status parameters of these nodes (such as latency, bandwidth, packet loss rate, etc.) are monitored in real time. By fully understanding the status of the downstream nodes, it is helpful for subsequent traffic carrying and performance evaluation analysis.

[0212] Step S504, performing traffic carrying analysis and performance evaluation analysis on each downstream node according to the network status parameters, and obtaining traffic carrying results and performance evaluation results of each downstream node;

[0213] Among them, the collected network status parameters are analyzed to evaluate the traffic carrying capacity of each downstream node (that is, to determine the actual available bandwidth, which can be obtained by subtracting the bandwidth data in use from the total bandwidth data) and comprehensive performance indicators (which can be obtained by integrating status parameters such as delay, packet loss rate, and bandwidth).

[0214] Specifically, we can first define multiple key performance indicators, such as bandwidth utilization, latency, and packet loss rate, and set weights for each indicator; then, collect network status parameters of each downstream node in real time, calculate the standardized value of each indicator; then, use the weighted average method to combine these standardized values ​​to obtain a comprehensive performance score to reflect the overall performance of the node. This comprehensive indicator will help to quickly identify downstream nodes with good performance during the fault recovery process to support traffic switching decisions.

[0215] Step S505, screening downstream nodes whose traffic carrying results meet the historical average traffic demand, and obtaining a candidate downstream node list;

[0216] Among them, nodes that can meet historical traffic requirements are screened out from all downstream nodes to form a candidate list to ensure that the network path after switching can meet the traffic requirements after network recovery.

[0217] Step S506, sorting the candidate downstream node list according to the performance evaluation result, and selecting the downstream node with the best performance evaluation result in the candidate downstream node list as a backup downstream node;

[0218] Specifically, candidate downstream nodes are sorted and the nodes with the best performance are selected to ensure that reliable paths are selected during fault recovery, thereby improving network stability and user experience.

[0219] Step S507, using the network connection path between the backup downstream node and the upstream node as a backup network path;

[0220] After the backup node is confirmed, a backup network path is formed based on the connection between the backup node and the upstream node.

[0221] Step S508: Switch the data flow of the current faulty node to the backup downstream node.

[0222] Specifically, a traffic switching operation is performed to redirect the data flow of the current faulty node to the selected backup downstream node, providing users with a seamless network switching experience and ensuring that users continue to enjoy network services, thereby reducing the impact of partial node failures on user experience.

[0223] In the above implementation, when the fault source is the current fault node, the system can quickly identify the appropriate backup path according to the location of the current fault node in the FTTR network topology, and effectively switch traffic, thereby ensuring the continuity and reliability of network services. This structured fault handling method not only improves the fault response speed, but also optimizes the utilization of network resources and improves the overall network experience of users through the analysis of historical data and real-time monitoring.

[0224] Reference Figure 6 As another implementation of step S108, when the fault source includes the current fault node and the upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path includes:

[0225] Step S601, determining the current network path of the faulty node and the upstream node based on the FTTR network topology;

[0226] The current network path includes the network connection path between the current fault node and the upstream node;

[0227] Specifically, in the FTTR network, the network connection path between the current faulty node (such as an access point) and the upstream node (such as the main router) needs to be clarified. By analyzing the network topology, the connection relationship between these nodes can be determined. By clarifying the network path of the fault source, it can provide a basis for subsequent steps and help to quickly locate the problem.

[0228] Exemplarily, assuming that the upstream node is router A and the current faulty node is access point B, the current network path is: router A→access point B→intelligent device C.

[0229] Step S602, obtaining the historical average flow demand of the current network path;

[0230] Among them, historical traffic data can help the system determine the bandwidth required under normal circumstances, so as to select the appropriate backup path when a fault occurs. By using the historical average traffic demand as a reference value, it is ensured that the backup path can meet the traffic demand when the fault is restored, avoiding performance bottlenecks.

[0231] Step S603, determining each adjacent upstream node of the upstream node based on the FTTR network topology structure, and determining the corresponding adjacent downstream nodes according to the adjacent upstream nodes, to obtain each adjacent network path;

[0232] Wherein, the adjacent network path includes a network connection path between an adjacent upstream node and a corresponding adjacent downstream node;

[0233] Specifically, in the FTTR network topology, the adjacent network path consists of the adjacent upstream nodes of the upstream node and the adjacent downstream nodes (i.e., the downstream nodes of the adjacent upstream nodes). By analyzing the topology, these nodes are identified in order to find backup paths, so as to broaden the possible backup path options and improve network redundancy.

[0234] Exemplarily, if the adjacent upstream node of router A is router D, and router D is connected to access point E, then the adjacent network path is: router D→access point E→intelligent device F.

[0235] Step S604, obtaining network status parameters of each adjacent network path, and performing traffic carrying analysis and performance evaluation analysis on each adjacent network path to obtain traffic carrying results and performance evaluation results of each adjacent network path;

[0236] The status of each adjacent network path is monitored, and its traffic carrying capacity and performance are evaluated to determine whether it is suitable as a backup path. For example, network status parameters (such as latency, bandwidth utilization, packet loss rate, etc.) can be collected to perform traffic carrying analysis and performance evaluation, so as to make more informed decisions and ensure that the selected backup path has good performance.

[0237] Step S605, screening the adjacent network paths whose traffic carrying results meet the historical average traffic demand, and obtaining a list of candidate adjacent network paths;

[0238] Among them, after evaluating the traffic carrying capacity of adjacent network paths, it is necessary to screen out paths that can meet historical traffic needs to ensure that there is no insufficient bandwidth during switching, that is, to ensure that when the fault is restored, the traffic carrying capacity of the backup path is consistent with user needs to avoid service interruption.

[0239] Step S606, sorting the candidate adjacent network path list according to the performance evaluation result, and taking the adjacent network path with the best performance evaluation result in the candidate adjacent network path list as the backup network path;

[0240] Among them, according to the performance evaluation results, the candidate paths that meet the traffic requirements are sorted to select the adjacent network path with the best performance as the backup path to ensure that the network continues to operate efficiently after switching.

[0241] Step S607: Switch the data flow of the current network path of the faulty node and the upstream node to the backup network path.

[0242] Among them, after determining the backup path, the data flow of the current faulty node and the upstream node needs to be switched to the new backup network path to achieve fault recovery, reduce the impact of the fault on users, and ensure stable operation of the system.

[0243] In the above implementation, when both the current faulty node and the upstream node are faulty, the network path of the current faulty node and its upstream node is identified, and the historical traffic demand and the status evaluation of the adjacent network path are combined to ensure that a suitable backup network path can be quickly switched to when a fault occurs. This not only improves the reliability of the network, but also enhances the efficiency of fault recovery and minimizes the impact on user experience.

[0244] Furthermore, as another implementation of step S109, when the fault source is the current fault node or the fault source includes the current fault node and an upstream node, the step of obtaining recovery result feedback information and updating it to the node fault history database includes: generating recovery result feedback information based on the network switching result, and updating it to the node fault history database.

[0245] In the above implementation, when the fault source is only the current faulty node or includes the current faulty node, the current faulty node needs to be switched to other downstream nodes during recovery. After the switch is completed, there is no need to obtain the recovery result of the current faulty node. The success or failure of the switching operation can be determined directly by obtaining the network switching result, and then the switching result is updated to the node fault history database, thereby realizing systematic management of network fault processing.

[0246] Reference Figure 7 As a further implementation of the present application, after marking the potential abnormal node whose abnormal level exceeds the preset level threshold as the current fault node in step S106, the following further includes:

[0247] Step S701, obtaining the household appliance corresponding to the current fault node according to the current fault node;

[0248] Among them, the current fault node can be a subnet segment of the home network, and the home appliances directly connected to the current fault node, such as smart TVs, air conditioners, etc., are identified. By identifying these devices as targets that need attention, subsequent fault analysis can target specific devices and concentrate resources to inspect and handle possible fault sources.

[0249] Step S702, obtaining the working status parameters of the household appliance;

[0250] Specifically, by real-time monitoring of the status parameters of household appliances, the operating parameters of the equipment can be obtained through network protocols or local sensors, such as power status, response time, working mode, etc., thereby providing the necessary data support for judging equipment failures and ensuring that the basis for judgment is the latest and most accurate.

[0251] Step S703, judging whether the household appliance has a fault according to the working status parameters; if not, jumping to step S704; if yes, jumping to step S705;

[0252] Specifically, the set thresholds or models can be applied to analyze the working status parameters of household appliances to determine whether the equipment has abnormalities or faults. The accuracy of fault identification can be improved through data-driven methods, the risk of misjudgment can be reduced, and effective fault management can be ensured.

[0253] In some embodiments, when certain smart home appliances malfunction, the following abnormal conditions may occur: abnormal increase in network traffic or sending of a large number of invalid data packets, causing network node overload, thereby affecting their normal operation; causing network address conflicts (such as multiple devices requesting the same IP address), which will make network nodes unable to correctly identify and process data streams, resulting in network failures; internal failures of home appliances lead to unstable power supply, which may affect network devices in the same circuit (such as routers or access points) and cause them to malfunction; error signals at the protocol layer may be generated, which will interfere with the normal communication of network nodes and cause the network nodes to fail to work normally.

[0254] Step S704, keep the mark of the current faulty node;

[0255] Specifically, if it is determined that there is no abnormality in the home appliance, it is determined that the fault is in the network node itself (such as a router hardware failure, a firmware problem, etc.), and the node continues to be marked and waits for subsequent repair.

[0256] Step S705, sending maintenance reminder information to the user terminal according to the location information of the home appliance;

[0257] Specifically, once a fault is identified, the system should immediately notify the user and use the device's location information to generate a specific notification so that the user can take timely action. For example, for a home appliance failure, the user may need to be guided to restart, update firmware, or perform repairs.

[0258] It is understandable that by promptly detecting home appliance failures and providing troubleshooting prompts, the user experience and satisfaction can be improved, and the interaction between users and smart home systems can be enhanced, allowing them to deal with problems in a timely manner.

[0259] Step S706, receiving the household appliance recovery information fed back by the user terminal, and performing fault analysis on the current fault node;

[0260] Step S707, determine whether there is a fault feature; if so, jump to step S704; if not, jump to step S708;

[0261] Specifically, after the user repairs and restores the home appliance, the system receives feedback from the user that the device has returned to normal, records the information, and checks whether the current faulty node still displays a faulty state.

[0262] Among them, if the home appliance has been repaired and restored to normal, but the current faulty node still has fault characteristics, it is determined that the fault is the network node itself (such as router hardware failure, firmware problem, etc.), and the node continues to be marked and waits for subsequent repairs.

[0263] Step S708, cancel the mark of the current faulty node.

[0264] If the home appliance is restored to normal after maintenance, and the current faulty node is also restored to normal, it means that the failure of the network node is caused by the failure of the home appliance, so the mark of the current faulty node is revoked.

[0265] In the above implementation, the system can effectively distinguish whether it is a network failure or a home appliance failure, and quickly and accurately locate the source of the problem through status monitoring, feature matching and cross-validation, so as to formulate targeted recovery strategies, thereby improving the reliability and user experience of the smart home system.

[0266] It is understandable that in a home, the FTTR network may not only connect network devices but also include smart appliances. In the present application, by comparing the data of each node, it is determined whether it is a network failure or an appliance failure. For example, if an appliance anomaly causes the network node to be inaccessible or faulty, an appliance maintenance prompt can be issued; otherwise, it is determined to be a network node failure and the subsequent network repair is awaited.

[0267] In actual application, this application can not only effectively reduce the risk of business interruption caused by network failures, but also help network managers optimize network architecture and resource allocation through historical data analysis. In addition, with the increase in the number of smart devices, the automated fault detection and recovery system can effectively reduce the burden on operation and maintenance personnel, allowing them to focus on higher-level network optimization and strategy formulation, thereby promoting the further development of the smart home and Internet of Things industries.

[0268] The embodiment of the present application also discloses an automatic fault detection and recovery system for an FTTR network.

[0269] A FTTR network automatic fault detection and recovery system, used to execute the above-mentioned FTTR network automatic fault detection and recovery method, the system comprises:

[0270] A network building module, used to build the FTTR network topology based on the network nodes in each room;

[0271] Network data collection module, used to collect network connection quality data of each network node in real time;

[0272] A state parameter calculation module, used to calculate the current state parameter of each network node according to the network connection quality data;

[0273] The abnormality analysis module is used to perform abnormality analysis on the current state parameters of each network node based on the node historical state database to obtain abnormality analysis results;

[0274] The abnormal node marking module is used to mark the network nodes with abnormal current state parameters as potential abnormal nodes according to the abnormal analysis results, and determine the abnormal level corresponding to the potential abnormal nodes;

[0275] A fault node marking module is used to mark potential abnormal nodes whose abnormality level exceeds a preset level threshold as current fault nodes;

[0276] The fault mode analysis module is used to analyze the fault mode of the current fault node based on the node fault history database and the FTTR network topology to determine the corresponding fault source;

[0277] A backup network switching module is used to detect a backup network path according to the current network path of the fault source based on the FTTR network topology structure, and switch the current network path of the fault source to the backup network path;

[0278] The recovery feedback information update module is used to obtain the recovery result feedback information and update it to the node fault history database.

[0279] In the above implementation, by building a comprehensive FTTR network automatic fault detection and recovery system, rapid identification and automatic recovery of network faults are achieved. From network topology construction to real-time data collection, state parameter calculation, anomaly analysis, fault node marking and backup network switching, a closed-loop fault handling process is formed. This system can significantly reduce fault recovery time, improve network reliability and stability, and thus improve overall user satisfaction.

[0280] An FTTR network automatic fault detection and recovery system of an embodiment of the present application can implement any of the above-mentioned FTTR network automatic fault detection and recovery methods, and the specific working process of each module in the FTTR network automatic fault detection and recovery system can refer to the corresponding process in the above-mentioned method embodiment.

[0281] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0282] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0283] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for automatic fault detection and recovery of FTTR network, characterized in that: The method comprises: Build FTTR network topology based on network nodes in each room; Collect network connection quality data of each network node in real time; Calculating the current state parameters of each network node according to the network connection quality data; Based on the node historical status database, respectively perform abnormal analysis on the current status parameters of each of the network nodes to obtain abnormal analysis results; According to the abnormality analysis result, the network node with abnormal current state parameters is marked as a potential abnormal node, and the abnormality level corresponding to the potential abnormal node is determined; Marking the potential abnormal node whose abnormal level exceeds the preset level threshold as a current fault node; Based on the node fault history database and the FTTR network topology, the fault mode analysis is performed on the current fault node to determine the corresponding fault source; based on the FTTR network topology, the backup network path detection is performed according to the current network path of the fault source, and the current network path of the fault source is switched to the backup network path; Obtaining recovery result feedback information and updating it to the node failure history database; Based on the node fault history database and the FTTR network topology, the steps of performing a fault mode analysis on the current fault node and determining the corresponding fault source include: Building and training a fault analysis model based on the node fault history database; Determine an upstream node of the current faulty node based on the FTTR network topology; Obtaining current state parameters of the current faulty node and the upstream node, and inputting them into the fault analysis model for fault analysis, and identifying fault characteristics of the current faulty node and the upstream node; Determine whether the upstream node has a fault characteristic; If not, determining that the fault source is the current fault node; If so, determining each downstream node corresponding to the upstream node based on the FTTR network topology; Acquire current state parameters of each of the downstream nodes, and input them into the fault analysis model to perform fault analysis and identify fault characteristics of the downstream nodes; Counting the proportion of downstream nodes with fault characteristics among the downstream nodes, and if the proportion is greater than a preset proportion threshold, determining that the fault source is the upstream node; If the ratio is less than a preset ratio threshold, then calculating the similarity of the fault characteristics of the current fault node and the upstream node; If the fault feature similarity is greater than a preset similarity threshold, determining that the fault source is the upstream node; If the fault feature similarity is less than a preset similarity threshold, it is determined that the fault source includes the current fault node and the upstream node.

2. A method for automatic fault detection and recovery of FTTR network according to claim 1, characterized in that: Based on the node fault history database, the steps of building and training a fault analysis model include: Based on the node fault history database, a historical sample fault feature set is obtained and preprocessed; wherein the historical sample fault feature set includes node historical state parameters and corresponding fault feature labels; Extract features from the preprocessed historical sample fault feature set to obtain a feature vector set; Dividing the feature vector set into training samples and verification samples; Inputting the training samples into a pre-built random forest model for initial training, optimizing model parameters, and obtaining an initially trained fault analysis model; The fault analysis model is verified and model parameters are corrected based on the verification samples until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining the trained fault analysis model.

3. The method for automatic fault detection and recovery of FTTR network according to claim 1, characterized in that: When the fault source is the upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path comprises: Determine each downstream node of the upstream node based on the FTTR network topology structure, and obtain a current network path of the upstream node; wherein the current network path includes a network connection path between the upstream node and each downstream node; Obtaining the historical average traffic demand of the upstream node; Determine the adjacent upstream nodes of the upstream node based on the FTTR network topology structure, and obtain network status parameters of each of the adjacent upstream nodes; Performing traffic carrying analysis and performance evaluation analysis on each adjacent upstream node according to the network status parameters to obtain traffic carrying results and performance evaluation results of each adjacent upstream node; Filter the adjacent upstream nodes whose traffic carrying results meet the historical average traffic demand to obtain a list of candidate adjacent upstream nodes; sorting the candidate adjacent upstream node list according to the performance evaluation result, and selecting the adjacent upstream node with the best performance evaluation result in the candidate adjacent upstream node list as a backup adjacent upstream node; Using the network connection path between the backup adjacent upstream node and each of the downstream nodes as the backup network path; The data flow of the upstream node is switched to the backup adjacent upstream node.

4. A method for automatic fault detection and recovery of FTTR network according to claim 3, characterized in that: The step of obtaining recovery result feedback information and updating it to the node fault history database includes: receiving recovery result feedback information of the current fault node and the downstream node, and uploading it to the node fault history database.

5. The method for automatic fault detection and recovery of FTTR network according to claim 1, characterized in that: When the fault source is the current fault node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path comprises: Determine the current network path of the current fault node based on the FTTR network topology structure; wherein the current network path includes a network connection path between an upstream node of the current fault node and the current fault node; Obtaining the historical average traffic demand of the current faulty node; Determine each downstream node corresponding to the upstream node based on the FTTR network topology structure, and obtain network status parameters of each downstream node; Performing traffic carrying analysis and performance evaluation analysis on each downstream node according to the network status parameters to obtain traffic carrying results and performance evaluation results of each downstream node; Filter the downstream nodes whose traffic carrying results meet the historical average traffic demand to obtain a candidate downstream node list; Sorting the candidate downstream node list according to the performance evaluation result, and selecting the downstream node with the best performance evaluation result in the candidate downstream node list as a standby downstream node; Using the network connection path between the backup downstream node and the upstream node as the backup network path; The data flow of the current failed node is switched to the standby downstream node.

6. The method for automatic fault detection and recovery of FTTR network according to claim 1, characterized in that: When the fault source includes the current fault node and the upstream node, based on the FTTR network topology, performing backup network path detection according to the current network path of the fault source, and switching the current network path of the fault source to the backup network path comprises: Determine the current network path between the faulty node and the upstream node based on the FTTR network topology structure; wherein the current network path includes the network connection path between the current faulty node and the upstream node; Obtaining the historical average traffic demand of the current network path; Determine each adjacent upstream node of the upstream node based on the FTTR network topology structure, and determine the corresponding adjacent downstream node according to the adjacent upstream node to obtain each adjacent network path; wherein the adjacent network path includes a network connection path between the adjacent upstream node and the corresponding adjacent downstream node; Acquire network status parameters of each of the adjacent network paths, and perform traffic carrying analysis and performance evaluation analysis on each of the adjacent network paths to obtain traffic carrying results and performance evaluation results of each of the adjacent network paths; Filtering adjacent network paths whose traffic carrying results meet the historical average traffic demand to obtain a list of candidate adjacent network paths; Sorting the candidate adjacent network path list according to the performance evaluation result, and taking the adjacent network path with the best performance evaluation result in the candidate adjacent network path list as the backup network path; The data flow of the current network path between the faulty node and the upstream node is switched to the backup network path.

7. A method for automatic fault detection and recovery of FTTR network according to any one of claims 5 to 6, characterized in that: The step of obtaining recovery result feedback information and updating the node fault history database includes: generating recovery result feedback information according to the network switching result, and updating the node fault history database.

8. The method for automatic fault detection and recovery of FTTR network according to claim 1, characterized in that: After the step of marking the potential abnormal node whose abnormal level exceeds the preset level threshold as the current fault node, the step further includes: Acquire the household appliance corresponding to the current faulty node according to the current faulty node; Obtaining working status parameters of the household appliance; Determining whether the household appliance has a fault according to the working status parameter; If there is no fault, keep the mark of the current faulty node; If there is a fault, a maintenance prompt message is sent to the user terminal according to the location information of the home appliance; Receiving the household appliance recovery information fed back by the user terminal, performing fault analysis on the current fault node, and determining whether there is a fault feature; If yes, keep the mark of the current faulty node; If not, the mark of the current faulty node is cancelled.

9. An FTTR network automatic fault detection and recovery system, used to execute an FTTR network automatic fault detection and recovery method according to any one of claims 1 to 8, characterized in that: The system comprises: A network building module, used to build the FTTR network topology based on the network nodes in each room; Network data collection module, used to collect network connection quality data of each network node in real time; A state parameter calculation module, used to calculate the current state parameter of each network node according to the network connection quality data; An abnormality analysis module is used to perform abnormality analysis on the current state parameters of each network node based on the node historical state database to obtain abnormality analysis results; An abnormal node marking module is used to mark the network node with abnormal current state parameters as a potential abnormal node according to the abnormal analysis result, and determine the abnormal level corresponding to the potential abnormal node; A fault node marking module, used to mark the potential abnormal node whose abnormal level exceeds a preset level threshold as a current fault node; A fault mode analysis module, used to perform a fault mode analysis on the current fault node based on a node fault history database and the FTTR network topology to determine a corresponding fault source; A backup network switching module, configured to detect a backup network path based on the FTTR network topology and the current network path of the fault source, and switch the current network path of the fault source to the backup network path; The recovery feedback information updating module is used to obtain the recovery result feedback information and update it to the node failure history database.

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