Passive optical network fault detection method, device, equipment, medium and program product

Abstract

The application discloses a passive optical network fault detection method, device, equipment, medium and program product, and belongs to the technical field of optical fiber communication. The method comprises the following steps: acquiring device nodes of a target network level in a passive optical network based on a network topology graph of the passive optical network; determining a poor-quality device node according to monitoring data of each device node at the target network level; determining a poor-quality device proportion according to the number of the poor-quality device node and the total number of the device nodes at the target network level; and determining a group obstacle fault according to the poor-quality device proportion, wherein the group obstacle fault is a device node fault caused by a device node fault of an adjacent upper level of the target network level. Through the method, the accurate positioning of a fault source can be realized, and the detection efficiency of the passive optical network fault is improved.

Description

Technical Field

[0001] This application relates to the field of optical fiber communication technology, and in particular to a method, apparatus, equipment, medium and program product for detecting faults in passive optical networks. Background Technology

[0002] With the rapid popularization of high-bandwidth multimedia applications such as video streaming, digital television, and IPTV, users' demands for network bandwidth, stability, and service carrying capacity continue to upgrade. In the context of triple-play integration, Fiber to the Home (FTTH) has become a core access solution due to its high bandwidth and high reliability, while Passive Optical Network (PON), with its outstanding advantages of high bandwidth and low cost, has been deeply applied in multi-service converged access scenarios such as home entertainment and enterprise leased lines, becoming a key technology carrier supporting broadband access.

[0003] PON networks employ a typical tree-like hierarchical topology, resulting in a complex structure. When a node (such as an optical line terminal or optical splitter) fails, all downstream terminal devices connected to that node (such as those with broken links or offline devices) will simultaneously experience network quality anomalies or failures. Relevant detection methods typically involve segment-by-segment testing of suspected fault points, which is not only cumbersome in fault location but also makes it difficult to identify batch node failures caused by a single node failure. Summary of the Invention

[0004] This application provides a passive optical network fault detection method, apparatus, device, medium, and program product to at least solve the problem that related fault detection methods have difficulty identifying batch node faults caused by single node faults.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide an optical network fault detection method, comprising: acquiring device nodes at a target network level in a passive optical network based on a network topology map of the passive optical network; identifying poor-quality device nodes based on monitoring data of each device node at the target network level; the poor-quality device nodes being device nodes whose operating quality does not meet preset conditions; determining the proportion of poor-quality devices based on the number of poor-quality device nodes and the total number of device nodes at the target network level; and determining a group fault based on the proportion of poor-quality devices, wherein the group fault is a device node fault caused by a fault of a device node at the adjacent upper level of the target network level.

[0006] Secondly, embodiments of this application provide an optical network fault detection device, comprising: a node acquisition module, used to acquire device nodes at a target network level in the passive optical network based on a network topology map of the passive optical network; a poor quality detection module, used to determine poor quality device nodes based on monitoring data of each device node at the target network level; the poor quality device nodes are device nodes whose operating quality does not meet preset conditions; a proportion determination module, used to determine the proportion of poor quality devices based on the number of poor quality device nodes and the total number of device nodes at the target network level; and a fault detection module, used to determine group faults based on the proportion of poor quality devices, the group faults being device node faults caused by faults of device nodes at the adjacent upper level of the target network level.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the method described in the first aspect above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect above.

[0009] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of the method described in the first aspect above.

[0010] In this embodiment, based on the network topology diagram of the passive optical network (PON), device nodes at the target network level are obtained. Based on monitoring data of each device node at the target network level, poor-quality device nodes are identified. The proportion of poor-quality devices is determined based on the number of these nodes and the total number of device nodes at the target network level. Group failures are identified based on this proportion, where the group failure is caused by a failure of a device node at the adjacent upper level of the target network. Thus, by determining the proportion of poor-quality devices according to the PON network's network level and identifying group failures caused by devices at the upper level based on this proportion, accurate location of the root cause of the failure can be achieved, improving the detection efficiency of PON faults.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] Figure 1 A flowchart illustrating some embodiments of the optical network fault detection method provided in this application is shown; Figure 2 This application illustrates a network topology diagram of a passive optical network provided in some embodiments; Figure 3 A flowchart illustrating an optical network fault detection method provided in other embodiments of this application is shown; Figure 4 The diagram shows a schematic representation of the optical network fault detection device provided in some embodiments of this application. Figure 5 The diagram shows a schematic representation of the structure of an electronic device provided in some embodiments of this application. Detailed Implementation

[0014] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0015] PON networks employ a typical tree-like hierarchical topology, resulting in a complex structure. When a node (such as an optical line terminal or optical splitter) fails, the downstream terminal devices connected to that node (such as those with broken links or offline devices) will simultaneously experience network quality abnormalities or failures.

[0016] The main PON fault detection methods are as follows: (1) Manual on-site troubleshooting method: Relying on maintenance personnel to carry testing instruments such as optical power meters to test suspected fault points segment by segment, and combine experience to judge the location of the fault.

[0017] (2) Network element alarm data analysis and log query method: locate faulty nodes by local alarms (e.g., device power failure, disconnection) and system logs (e.g., authentication timeout, device offline) of devices such as optical line terminal (OLT) and optical network unit (ONU).

[0018] (3) Network element performance threshold analysis method: Collect network element performance values ​​(e.g., optical power, bandwidth utilization, etc.) and identify faulty nodes by threshold judgment.

[0019] The above-mentioned detection methods usually test suspected fault points segment by segment, which is not only cumbersome to locate faults, but also difficult to identify batch node faults caused by a single node fault.

[0020] To address the aforementioned problems in PON fault detection, this application provides a passive optical network fault detection method. This method determines the proportion of poor-quality devices according to the network hierarchy of the passive optical network, and determines the group fault caused by the device nodes of the next higher level based on the proportion of poor-quality devices, so as to achieve accurate location of the fault root cause and improve the detection efficiency of passive optical network faults.

[0021] Please see Figure 1 , Figure 1 This document illustrates a flowchart of an optical network fault detection method provided in some embodiments of this application. The method can be executed by a terminal device or a server. The terminal device can be a personal computer, a mobile terminal device such as a mobile phone or tablet, or a user-used terminal device. The server can be a standalone server or a server cluster composed of multiple servers. Furthermore, the server can be a backend server for a specific service, or a backend server for a platform or application (e.g., a PON network management system, a network fault diagnosis system, an optical network equipment monitoring platform, etc.). This embodiment uses a server as the executing entity for illustration. For terminal devices, the following related content can be used for processing, which will not be elaborated upon here. Figure 1 As shown, the method 100 may include the following steps: Step 101: Based on the network topology diagram of the passive optical network, obtain the device nodes of the target network layer in the passive optical network.

[0022] In one exemplary embodiment, basic information such as device model, deployment location, port configuration, and ownership of node devices (ONU, OLT, etc.) in the Passive Optical Network (PON) can be collected in advance. After data cleaning and correlation integration of this basic information, a network topology map of the PON is generated. Based on this network topology map, device nodes at the target network level in the PON are obtained. This target network level can be a network level selected according to actual needs, or it can be the network level where the target device node sending the poor quality alarm is located.

[0023] Step 102: Based on the monitoring data of each device node in the target network layer, identify the device nodes with poor quality.

[0024] Among them, the poor quality equipment node is the equipment node whose operating quality does not meet the preset conditions.

[0025] Continuing with the above embodiments, monitoring data such as optical power, bandwidth utilization, power outages, port disconnections, and link breaks are obtained for each device node at the target network layer. Based on this monitoring data, poor-quality device nodes are identified. For example, the performance of a device node is determined based on monitoring data such as optical power and bandwidth utilization. When the performance of a device node fails to meet preset performance indicators, that device node is identified as a poor-quality device node. As another example, the alarm level of a device node is determined based on monitoring data such as power outages, port disconnections, and link breaks. When the alarm level of a device node exceeds the security level, that device node is identified as a poor-quality device node.

[0026] Step 103: Determine the proportion of poor-quality devices based on the number of poor-quality device nodes and the total number of device nodes in the target network layer.

[0027] Continuing with the above embodiments, the ratio of the number of poor-quality device nodes to the total number of device nodes in the target network layer can be determined as the proportion of poor-quality devices; alternatively, a weighting coefficient can be determined based on the distribution characteristics of poor-quality device nodes in the target network layer, and the proportion of poor-quality devices can be determined based on the weighting coefficient and the above ratio.

[0028] Step 104: Determine the group failure based on the proportion of poor-quality equipment.

[0029] Among them, the aforementioned group failure refers to a device node failure caused by a failure of a device node in the next higher level of the target network layer.

[0030] Continuing with the above embodiments, the proportion of poor-quality devices can be compared with a preset threshold, and the comparison result can be used to determine whether it is a group failure. The group failure is a device node failure caused by the failure of the device node in the adjacent upper level of the target network layer.

[0031] In some possible implementations, after determining that the upper level is a group failure, the previous level can be used as the target network level, and steps 101 to 104 above can be re-executed to continue determining whether the upper level is a group failure.

[0032] In this way, by using the above-mentioned vertical penetration-type fault tracing method for PON multi-level topology, the distribution of poor-quality devices can be analyzed level by level, enabling cross-level data connectivity and accurate identification of group faults, thereby achieving accurate location of the root cause of the fault and improving the detection efficiency of passive optical network faults.

[0033] In some embodiments, the network topology diagram described above is generated based on the node attribute information and port affiliation information of each optical network device in the passive optical network.

[0034] The node attribute information includes device serial number, device type, device model, deployment location, etc.; the port attribution information includes port link mapping relationship, uplink and downlink port correspondence, port service attribution, bandwidth configuration information, etc.

[0035] In one exemplary embodiment, such as Figure 2 As shown, based on the node attribute information and port affiliation information of each optical network device in the passive optical network, a network topology diagram consisting of user terminal equipment, optical network unit, optical distribution network (ODN), optical line terminal (OLT) PON port, OLT, and Broadband Remote Access Server (BRAS) can be generated. The optical distribution network includes optical splitters, optical distribution network (ODN), optical distribution boxes, etc.

[0036] In some embodiments, the device nodes at the target network layer mentioned above include target device nodes; in step 101, obtaining the device nodes at the target network layer in the passive optical network based on the network topology map of the passive optical network includes: In response to receiving a quality deterioration alarm from the target device node, the upstream device node with a direct topological relationship to the target device node is identified based on the network topology map of the passive optical network. Based on the network topology map and the upstream device node, all downstream device nodes in the passive optical network that belong to the upstream device node are identified as device nodes at the target network level.

[0037] In one exemplary embodiment, after receiving a poor quality alarm message from a target device node, the system analyzes the poor-quality devices layer by layer based on the target network hierarchy associated with the target device node and performs fault location. For example, based on the network topology diagram of the passive optical network, it identifies upstream device nodes with a direct topological relationship to the target device node and determines the same-level device nodes that need to be checked, such as: (1) ONU layer: The collection of all terminal devices under the same ONU; (2) Splitter Port Layer: The set of all ONUs under the same splitter port; (3) Beam splitter convergence layer: a collection of multiple branches under the same beam splitter; (3) OLT layer: The set of all PON ports under the same OLT; (4) BRAS layer: All OLT sets under the same BRAS.

[0038] This method allows for a more comprehensive acquisition of device nodes at the target network level, thereby improving the accuracy of group fault identification.

[0039] In some embodiments, the monitoring data mentioned above includes at least one of performance metrics and alarm data; in step 102 above, determining the poor-quality device nodes based on the monitoring data of each device node under the target network layer includes at least one of the following: Step 1021: Based on the performance indicators, the corresponding benchmark values ​​of the performance indicators, and the indicator weights of the performance indicators, determine the performance prediction value, and identify the device nodes with performance prediction values ​​lower than the first threshold in each device node of the standard network layer as poor quality device nodes.

[0040] In an exemplary embodiment, the aforementioned performance metrics include uplink and downlink optical power, bit error rate, packet loss rate, bandwidth utilization, etc. Performance estimates are calculated for each device node i under the target network layer, using the following formulas:

[0041] in, The weights of each performance indicator are: For the normalized scoring function, The index value of the performance indicator. A normalized score is output based on the comparison between the performance index value and the corresponding benchmark value.

[0042] The performance estimates of each device node under the above target network level are calculated. Below the first threshold The device node was identified as a poor-quality device node.

[0043] Step 1022: Based on the number of alarms, alarm duration, alarm correlation coefficient, and alarm level weight in the alarm data, determine the alarm prediction value, and identify the device nodes with alarm prediction values ​​higher than the second threshold in each device node under the target network level as poor-quality device nodes.

[0044] Continuing with the above embodiments, the alarm prediction value can be determined using the following formula:

[0045] in, For example, the alarm level weights are: Emergency Alarm Level = 4, Important Alarm Level = 3, General Alarm Level = 2, and Warning Alarm Level = 1.

[0046] For example, a coefficient corresponding to the number of alarms. , This represents the number of alarms of the same type. Understandably, the more alarms of the same type there are, the greater the likelihood of a failure, and thus the higher this coefficient. At the same time, an upper limit is set to prevent excessive amplification.

[0047] For example, the alarm duration coefficient. , This refers to the duration of the alarm. It's understandable that the longer the alarm duration, the higher the certainty of the fault, and thus the larger this coefficient.

[0048] For example, the alarm correlation coefficient. , The number of associated alarms, This represents the total number of alarms. It's understandable that the more associated alarms there are, the wider the scope of the fault's impact, and thus the larger this coefficient will be.

[0049] This is the maximum alarm estimate.

[0050] The alarm prediction value of each device node under the target network level Above the second threshold The device node was identified as a poor-quality device node.

[0051] In some possible implementations, the poor quality status of a device node can be determined using the following formula:

[0052] in, This indicates that the device node has been determined to be in a poor quality state, i.e., it is a poor quality device node. This indicates that the device node has been determined to be in a normal state.

[0053] In this way, through the aforementioned hierarchical alarm scoring mechanism, alarms are quantitatively scored by comprehensively considering four parameters: alarm level weight, alarm quantity, alarm duration, and alarm correlation coefficient. This allows for the determination of fault severity and impact range at different network levels. This mechanism can provide a more accurate basis for fault priority judgment in complex scenarios where alarm information is insufficient or where there are overlapping alarms.

[0054] In this embodiment, by performing a dual-dimensional analysis of performance indicators and alarm data, performance parameters such as optical power and bandwidth utilization are cross-correlated with alarm information such as equipment power failure, disconnection, and link breakage. This enables fault diagnosis to be both sensitive to performance anomalies and direct to alarm events, thereby improving the comprehensive judgment capability in areas such as group fault identification, fault tracing, and hidden danger warning.

[0055] In some embodiments, step 104 above, determining the group failure based on the proportion of poor-quality devices, includes: When the proportion of poor-quality equipment is greater than or equal to a dynamic threshold, it is determined to be a group failure; the dynamic threshold is determined based on the standard deviation of the historical poor quality rate.

[0056] In one exemplary embodiment, the number of poor-quality device nodes in the target network layer is obtained by the following formula:

[0057] in, This represents the number of poor-quality device nodes.

[0058] The number of poor-quality device nodes The total number of device nodes under the target network layer The ratio determines the proportion of poor-quality equipment. The formula is as follows:

[0059] The dynamic threshold is determined based on the standard deviation of historical quality defect rates. For example, the dynamic threshold is determined in the following way:

[0060] in, This is the base threshold, which can be set according to actual needs. For fluctuation coefficient, This represents the standard deviation of the historical quality deviation rate.

[0061] like The fault is identified as a group fault, which is a device node fault caused by a fault of a device node in the next higher level of the target network layer.

[0062] In this way, through the above-mentioned dynamic threshold determination mechanism, the sensitivity of quality defect judgment can be dynamically changed with business load and operational fluctuations. Compared with the fixed threshold method, the embodiments of this application can significantly reduce the false judgment rate and the false negative rate, and improve the ability to identify group faults and provide early warning.

[0063] In other embodiments, a fault is determined when the proportion of poor-quality devices is less than a dynamic threshold; the fault is a failure of a device node under the target network layer.

[0064] Continuing with the above embodiments, if The fault is identified as an individual fault; the individual fault is a failure of a device node at the target network level.

[0065] In some possible implementations, after determining that a fault is an individual failure, device-level detection operations are also performed on the poor-quality device nodes at the target network level.

[0066] In one exemplary embodiment, as described above Figure 2 Taking the network topology in the example, such as Figure 3 As shown, the above-mentioned passive optical network fault detection method may include the following steps: Step 301: When a quality deterioration alarm message is received from a user terminal device, check whether other device nodes under the same ONU are in a quality deterioration state, i.e., calculate the proportion of quality deterioration devices under the same ONU. .

[0067] (1) If the proportion of poor-quality equipment does not exceed the dynamic threshold, i.e. The detection result indicates a fault, meaning that the current user terminal device itself is faulty or its terminal-side network is abnormal, and the device-level detection operation is initiated. (2) If the proportion of poor-quality equipment exceeds the dynamic threshold, i.e. The fault is clustered to this ONU, and then proceed to step 302.

[0068] Step 302: Check whether multiple ONUs under the same splitter output port are in a poor quality state, and calculate the proportion of poor quality ONUs under the same splitter output port. .

[0069] (1) If the proportion of poor-quality equipment does not exceed the dynamic threshold, i.e. This indicates a possible fault in a single ONU or a downstream user-side connection; check the ONU's performance metrics and alarms (such as ONU voluntary offline or power failure). If the combination of alarms and poor performance determines the appropriate strategy. If the alarm is detected, it is determined to be an ONU malfunction, and the anomaly is further located based on the alarm and performance indicators; otherwise, it is inferred to be a fault on the downstream user side or that the ONU may have potential faults, and an alarm message is sent to the target device to prompt relevant personnel to conduct on-site investigation.

[0070] (2) If the proportion of poor-quality equipment exceeds the dynamic threshold, i.e. The faults are clustered to the spectrophotometers of the branch to which they belong, and then proceed to step 303.

[0071] Step 303: Check whether multiple ODN branches under the same splitter are in a poor quality state, and calculate the proportion of poor quality devices among multiple ODN branches under the same splitter. .

[0072] (1) If the proportion of poor-quality equipment does not exceed the dynamic threshold, i.e. Then proceed to ODN indicator troubleshooting (such as abnormal optical power, fiber break alarm, splitter port damage, and excessive fiber splice loss). If the combination of alarms and performance is poor, a decision-making strategy will be implemented. If the error is detected, it is determined to be an ODN branch fault, and the anomaly is further located based on alarms and performance indicators; otherwise, it is inferred that there may be potential faults in the ODN branch fiber optic link, and alarm information is sent to the target device.

[0073] (2) If the proportion of poor-quality equipment exceeds the dynamic threshold, i.e. The faults are clustered to the corresponding OLT PON port nodes, and then proceed to step 304.

[0074] Step 304: Check whether other PON ports under the same OLT are in a poor quality state, that is, calculate the proportion of poor quality devices among the PON ports under the OLT to which this PON port belongs. .

[0075] (1) If the proportion of poor-quality equipment does not exceed the dynamic threshold, i.e. Then check the performance indicators and alarms of the OLTPON port (such as signal loss, multiple power outages of downstream ONUs, and link interruptions). If the combination of alarms and performance is poor, determine the scoring strategy. If the error is detected, it is determined to be an OLT PON port failure, and the anomaly is further located based on alarms and performance indicators; otherwise, it is suspected that there may be a potential fault in the OLT PON port, and an alarm message is sent to the target device.

[0076] (2) If the proportion of poor-quality equipment exceeds the dynamic threshold, i.e. The fault is clustered to the OLT to which the PON port belongs, and then proceeds to step 305.

[0077] Step 305: Check whether other OLTs under the same BRAS are in a poor quality state, that is, calculate the proportion of poor quality equipment among the OLTs under the BRAS to which this OLT belongs. .

[0078] (1) If the proportion of poor-quality equipment does not exceed the dynamic threshold, i.e. Then check the OLT's performance metrics and alarms (such as equipment malfunctions, signal problems, etc.). If the combination of alarms and performance is poor, determine the scoring strategy. If the alarm is detected, it is determined to be an OLT malfunction, and the anomaly is further located based on the alarm and performance indicators; otherwise, it is suspected that there may be a potential fault in the OLT, and an alarm message is sent to the target device.

[0079] (2) If the proportion of poor-quality equipment exceeds the dynamic threshold, i.e. The BRAS to which this OLT belongs may have malfunctioned and requires further investigation.

[0080] In this embodiment, starting from the user terminal device, the fault location link is formed by sequentially aggregating upwards to nodes such as ONU, ODN, OLT PON port, OLT, and BRAS, calculating the proportion of poor-quality devices layer by layer, and then clustering and moving the clusters upwards. This method breaks down the analysis barriers between different levels, enabling accurate tracing of batch fault nodes and significantly improving the efficiency of group fault location. It can also provide early warning in the early stages of a fault, significantly reducing service interruption time and improving network service stability and user satisfaction.

[0081] Please refer to Figure 4. Figure 4 This application provides schematic diagrams of the structure of an optical network fault detection device according to some embodiments. This optical network fault detection device can achieve the following: Figure 1 or Figure 3 The optical network fault detection device 400, as shown in all or part of the embodiments, includes: The node acquisition module 410 is used to acquire device nodes at the target network level in the passive optical network based on the network topology map of the passive optical network. The poor quality detection module 420 is used to determine the poor quality device nodes based on the monitoring data of each device node under the target network layer; the poor quality device nodes are device nodes whose operating quality does not meet the preset conditions. The ratio determination module 430 is used to determine the ratio of poor-quality devices based on the number of poor-quality device nodes and the total number of device nodes under the target network layer. The fault detection module 440 is used to determine the group fault based on the proportion of the poor-quality equipment. The group fault is a device node fault caused by the fault of the device node in the adjacent upper level of the target network layer.

[0082] In some embodiments, the network topology diagram described above is generated based on the node attribute information and port affiliation information of each optical network device in the passive optical network.

[0083] In some embodiments, the device nodes at the target network layer include target device nodes; the node acquisition module 410, when acquiring device nodes at the target network layer in the passive optical network based on the network topology map of the passive optical network, is specifically used for: In response to receiving the poor quality alarm information from the target device node, the upstream device node with a direct topological relationship to the target device node is determined based on the network topology map of the passive optical network. Based on the network topology and the upstream device nodes, all downstream device nodes belonging to the upstream device nodes in the passive optical network are identified as device nodes at the target network level.

[0084] In some embodiments, the monitoring data mentioned above includes at least one of performance indicators and alarm data; the poor quality detection module 420, when determining the poor quality device node based on the monitoring data of each device node under the target network layer, is specifically used for: Based on the performance indicators, the corresponding benchmark values, and the weights of the performance indicators, a performance prediction value is determined, and device nodes with performance prediction values ​​lower than a first threshold in each device node of the target network layer are identified as poor-quality device nodes; and / or, Based on the number of alarms, alarm duration, alarm correlation coefficient, and alarm level weight in the alarm data, the alarm prediction value is determined, and the device nodes with alarm prediction values ​​higher than the second threshold in each device node under the target network layer are identified as poor-quality device nodes.

[0085] In some embodiments, when the fault detection module 440 is used to determine a group fault based on the proportion of poor-quality equipment, it is specifically used for: When the proportion of poor-quality devices is greater than or equal to a dynamic threshold, it is determined to be a group failure; the dynamic threshold is determined based on the standard deviation of the historical poor-quality rate.

[0086] In some possible implementations, the fault detection module 440 is also used for: When the proportion of poor-quality devices is less than the dynamic threshold, it is determined to be an individual fault; the individual fault is a failure of a device node under the target network layer.

[0087] This application provides a passive optical network (PON) fault detection device, including a node acquisition module, a quality degradation detection module, a ratio determination module, and a fault detection module. The node acquisition module acquires device nodes at a target network level in the PON based on the PON's network topology. The quality degradation detection module identifies the device nodes with poor quality based on monitoring data of each device node at the target network level. The ratio determination module determines the ratio of device nodes with poor quality based on the number of such nodes and the total number of device nodes at the target network level. The fault detection module determines group faults based on the ratio of device nodes with poor quality, where the group fault is caused by a fault in a device node at the adjacent upper level of the target network level. By determining the ratio of device nodes with poor quality according to the PON's network level and determining the group faults caused by the device nodes at the upper level based on the ratio of device nodes with poor quality, the root cause of the fault can be accurately located, improving the detection efficiency of PON faults.

[0088] Figure 5The diagram illustrates the structure of an electronic device according to some embodiments of this application. Referring to the diagram, at the hardware level, the electronic device 500 includes a processor 510, and optionally includes an internal bus 520, a network interface 530, and a memory. The memory may include main memory 541, such as high-speed random-access memory (RAM), and may also include non-volatile memory 542, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0089] The processor 510, network interface 530, and memory can be interconnected via an internal bus 520. This internal bus 520 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus.

[0090] The memory stores programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory 541 and non-volatile memory 542, and provides instructions and data to the processor 510.

[0091] Processor 510 reads the corresponding computer program from non-volatile memory 542 into memory and then runs it, forming a device for locating the target user at the logical level. Processor 510 executes the program stored in memory and specifically performs the following: Figure 1 or Figure 3 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0092] The above is as stated in this application. Figure 1 or Figure 3The methods disclosed in the illustrated embodiments can be applied to or implemented by processor 510. Processor 510 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the hardware of processor 510 or by instructions in software form. The processor 510 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0093] The computer device can also execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0094] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0095] This application also proposes a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1 or Figure 3 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0096] The computer-readable storage medium includes read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0097] Furthermore, embodiments of this application also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, implement the following process: Figure 1 or Figure 3 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0098] The embodiments of this application can be applied to various scenarios of electronic device collaboration or interconnection, including: collaboration and interconnection between mobile phones and laptops / tablets; collaboration and interconnection between mobile terminals and smart TVs / monitors; collaboration and interconnection between mobile phones or tablets and in-vehicle entertainment systems; collaboration and interconnection between mobile terminals and smart conferencing systems, etc. This satisfies users' diverse needs in smart home, smart office, and smart travel scenarios.

[0099] In summary, the above description is merely a preferred embodiment of this application and does not limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0100] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0101] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0103] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A method for fault detection in a passive optical network, characterized in that, include: Based on the network topology diagram of the passive optical network, obtain the device nodes of the target network layer in the passive optical network; Based on the monitoring data of each device node under the target network layer, poor-quality device nodes are identified; the poor-quality device nodes are those whose operating quality does not meet the preset conditions. The proportion of poor-quality devices is determined based on the number of poor-quality device nodes and the total number of device nodes under the target network layer. The group failure is determined based on the proportion of poor-quality devices. The group failure is a device node failure caused by a device node failure in the next higher level of the target network layer.

2. The method according to claim 1, characterized in that, The network topology diagram is generated based on the node attribute information and port affiliation information of each optical network device in the passive optical network.

3. The method according to claim 1, characterized in that, The target network layer device nodes include target device nodes; the process of obtaining the target network layer device nodes in the passive optical network based on the network topology map of the passive optical network includes: In response to receiving the poor quality alarm information from the target device node, the upstream device node with a direct topological relationship to the target device node is determined based on the network topology map of the passive optical network. Based on the network topology and the upstream device nodes, all downstream device nodes belonging to the upstream device nodes in the passive optical network are identified as device nodes at the target network level.

4. The method according to claim 1, characterized in that, The monitoring data includes at least one of performance indicators and alarm data; the step of determining the poor-quality device nodes based on the monitoring data of each device node under the target network layer includes: Based on the performance indicators, the corresponding benchmark values, and the weights of the performance indicators, a performance prediction value is determined, and device nodes with performance prediction values ​​lower than a first threshold in each device node of the target network layer are identified as poor-quality device nodes; and / or, Based on the number of alarms, alarm duration, alarm correlation coefficient, and alarm level weight in the alarm data, the alarm prediction value is determined, and the device nodes with alarm prediction values ​​higher than the second threshold in each device node under the target network layer are identified as poor-quality device nodes.

5. The method according to any one of claims 1 to 4, characterized in that, The step of determining group failures based on the proportion of poor-quality equipment includes: When the proportion of poor-quality devices is greater than or equal to a dynamic threshold, it is determined to be a group failure; the dynamic threshold is determined based on the standard deviation of the historical poor-quality rate.

6. The method according to claim 5, characterized in that, Also includes: When the proportion of poor-quality devices is less than the dynamic threshold, it is determined to be an individual fault; the individual fault is a failure of a device node under the target network layer.

7. A passive optical network fault detection device, characterized in that, include: The node acquisition module is used to acquire device nodes at the target network level in the passive optical network based on the network topology map of the passive optical network. The poor quality detection module is used to identify poor quality device nodes based on the monitoring data of each device node under the target network layer; the poor quality device node is a device node whose operating quality does not meet the preset conditions. The proportion determination module is used to determine the proportion of poor-quality devices based on the number of poor-quality device nodes and the total number of device nodes under the target network layer; The fault detection module is used to determine group faults based on the proportion of poor-quality devices. The group faults are device node faults caused by device node faults in the adjacent upper level of the target network layer.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the steps of the method as described in any one of claims 1 to 6.

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