PON network fault detection method, apparatus and device, and storage medium

Abstract

The invention discloses a PON (Passive Optical Network) network fault detection method, device and equipment and a storage medium, the PON network fault detection method can be applied to Fiber To Room (FTTR), enterprise-level Fiber To Room-Bus (FTTR-B) and broadband fusion terminal products, and the PON network fault detection method comprises the following steps: obtaining an original bit error rate and link quality data carrying a sub-machine identifier, the sub-machine identifier is a unique identifier corresponding to the FTTR sub-machine; performing fault detection based on the original bit error rate and the link quality data, and determining a fault detection result corresponding to the sub-machine identifier; and when the fault detection result is that a fault exists, executing fault alarm processing based on the original bit error rate and a target bit error rate threshold. According to the method, the problems that the fault source cannot be determined in the PON network, the fault detection is inaccurate and the alarm is not timely can be solved.

Description

Technical Field

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

[0002] In FTTR (Fiber to the Room) networks, slave units are connected to mini OLTs (Optical Line Terminals) via optical fiber. The stability of the optical module directly impacts signal transmission quality. An increased bit error rate (BER) can lead to data retransmissions, increased network latency, and, in severe cases, service interruptions.

[0003] Traditional optical networks use global bit error rate statistics from the OLT, but they cannot distinguish specific slave device failures. Some solutions rely on manual, periodic testing of optical module performance, which is inefficient and lacks real-time alarms. As a result, existing methods cannot pinpoint the source of faults, resulting in inaccurate fault detection and delayed alarms. Summary of the Invention

[0004] The embodiments of the present invention provide a PON network fault detection method, apparatus, device and storage medium to solve the problems of being unable to determine the source of a fault, inaccurate fault detection and untimely alarm.

[0005] In a first aspect, an embodiment of the present invention provides a PON network fault detection method, comprising: Obtaining raw bit error rate and link quality data carrying a slave identifier, where the slave identifier is a unique identifier corresponding to the FTTR slave; Performing fault detection based on the original bit error rate and the link quality data, and determining a fault detection result corresponding to the slave identifier; When the fault detection result indicates that a fault exists, a fault alarm process is performed based on the original bit error rate and the target bit error rate threshold.

[0006] Furthermore, the performing fault detection based on the original bit error rate and the link quality data to determine the fault detection result corresponding to the slave identifier includes: Determining a bit error detection result based on the original bit error rate; Determining a link quality detection result based on the link quality data; A fault detection result is determined based on the bit error detection result and the link quality detection result.

[0007] Furthermore, determining the bit error detection result based on the original bit error rate includes: If the average of all original bit error rates corresponding to the first time period is greater than the target bit error rate threshold, the bit error detection result is determined to be a fault; If the average of all original bit error rates corresponding to the first time period is not greater than the target bit error rate threshold, determining that the bit error detection result is that no fault exists; The first time period is a time period corresponding to a first preset duration before the current moment.

[0008] Furthermore, the target bit error rate threshold is determined based on the historical bit error rate corresponding to the second time period. The second time period is a time period corresponding to a second preset time length before the current moment, and the second preset time length is greater than the first preset time length.

[0009] Furthermore, determining a link quality detection result based on the link quality data includes: If there is a link quality data that is greater than the corresponding link quality threshold, determining that the link quality detection result is a link abnormality; If all the link quality data are not greater than the corresponding link quality threshold, determining that the link quality detection result is normal; The link quality data includes at least one of received optical power, transmitted optical power and signal-to-noise ratio.

[0010] Furthermore, determining a fault detection result based on the bit error detection result and the link quality detection result includes: If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is normal, determining that the optical fiber is damaged; If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is abnormal, it is determined that the optical module is faulty.

[0011] Furthermore, the performing fault alarm processing based on the original bit error rate and the target bit error rate threshold includes: If the average of all original bit error rates corresponding to the first time period is greater than a first alarm threshold, a mild alarm is performed, where the first alarm threshold is the product of the target bit error rate threshold and a first coefficient, and the value range of the first coefficient is 0-1; If the average of all original bit error rates corresponding to the first time period is greater than the second alarm threshold for a duration greater than a third preset duration, a severe alarm is executed, where the second alarm threshold is the product of the target bit error rate threshold and a second coefficient, and the second coefficient is greater than 1.

[0012] In a second aspect, an embodiment of the present invention provides a PON network fault detection device, comprising: A data acquisition module is used to obtain the original bit error rate and link quality data carrying the slave identifier, where the slave identifier is a unique identifier corresponding to the FTTR slave; a fault detection module, configured to perform fault detection based on the original bit error rate and the link quality data, and determine a fault detection result corresponding to the slave identifier; The alarm module is used to perform fault alarm processing based on the original bit error rate and the target bit error rate threshold when the fault detection result is that a fault exists.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned PON network fault detection method when executing the program.

[0014] In a fourth aspect, an embodiment of the present invention provides a readable storage medium, wherein the readable storage medium stores a program, and when the program is executed by a processor, the above-mentioned PON network fault detection method is implemented.

[0015] The aforementioned PON network fault detection method, apparatus, device, and storage medium acquire raw bit error rate and link quality data that carry a sub-station identifier. Because the raw bit error rate and link quality data carry a unique sub-station identifier, it is possible to clearly identify the specific sub-station from which the raw bit error rate and link quality data originate. When a fault is determined based on this data, the sub-station with the problem can be precisely located based on the sub-station identifier. Furthermore, by combining the bit error rate and link quality data, the raw bit error rate reflects the probability of errors during data transmission, while the link quality data describes the link status from another perspective. This multi-dimensional data can accurately identify PON network faults and generate timely alarms. When a fault is detected, fault alarm processing is executed based on the raw bit error rate and target bit error rate thresholds. This threshold-based alarm mechanism triggers an alarm as soon as a fault occurs. Therefore, this method can address the issues of inability to determine the source of faults, inaccurate fault detection, and delayed alarms in PON networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 is a flow chart of a PON network fault detection method according to an embodiment of the present invention; Figure 2 A schematic diagram of a connection between an uplink device and a slave device provided by an embodiment of the present invention; Figure 3is another flow chart of a PON network fault detection method according to one embodiment of the present invention; Figure 4 FIG. 1 is a schematic diagram of a PON network fault detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0020] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0021] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0024] In order to fully understand the present invention, detailed structures and steps will be provided in the following description to illustrate the technical solutions proposed by the present invention. Preferred embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementations.

[0025] See also Figure 1 , is a flow chart of a PON network fault detection method provided by an embodiment of the present invention, such as Figure 1 As shown, the PON network fault detection method can be implemented through the following steps.

[0026] S101: Obtaining original bit error rate and link quality data carrying a slave identifier, where the slave identifier is a unique identifier corresponding to an FTTR slave; S102: Perform fault detection based on the original bit error rate and the link quality data, and determine a fault detection result corresponding to the slave identifier; S103: When the fault detection result indicates that a fault exists, perform fault alarm processing based on the original bit error rate and the target bit error rate threshold.

[0027] The target bit error rate threshold is a preset bit error rate threshold, or a bit error rate threshold dynamically determined according to actual conditions.

[0028] Fiber to the Room (FTTR) is a broadband access technology that extends fiber networks directly into users' rooms. FTTR technology is primarily used in homes and corporate offices. It provides each room with an independent fiber connection, enabling ultra-high-speed network transmission, meeting the needs of multiple devices for simultaneous high-speed internet access. The signal is stable and resistant to interference.

[0029] The FTTR slave is an optical network unit (ONU) deployed at the user terminal. It is used to connect to the main fiber optic network and realize fiber-to-the-terminal communication within the room. Its upstream device is the optical line terminal, generally an OLT or mini OLT device. The OLT device is the core hub of the entire FTTR system, responsible for aggregating and managing the signals of multiple FTTR slaves, realizing connection with the backbone network, and configuring, monitoring and maintaining the entire fiber optic network. The miniOLT device is a more compact optical line terminal suitable for small commercial venues or home scenarios. Its functions are similar to those of the OLT, but it can quickly build an efficient fiber optic network in a limited space, providing users with flexible network solutions. Both use optical signals to exchange data with the FTTR slave, realizing efficient data transmission and precise control.

[0030] As an example, Figure 2 Figure 2 shows a schematic diagram of a connection between an uplink device and slave devices, where the uplink device is a mini OLT 2 and the slave devices are ONUs 21, 22, and 23. The mini OLT 2 is connected to ONUs 21, 22, and 23 via optical fibers for unidirectional or bidirectional data transmission. Each ONU has a unique identifier, typically a MAC address or logical ID. The raw bit error rate and link quality data collected for each FTTR slave device are mapped to the unique identifier of the FTTR slave device.

[0031] The raw bit error rate (BER) represents the ratio of raw bit error counts collected within a specified timeframe to the total number of bits transmitted. It is a measure of data transmission accuracy within that timeframe. A more efficient and accurate data collection mechanism is employed to obtain the raw bit error rate of FTTR slave units. A specially designed monitoring agent is deployed on the uplink device (such as the mini OLT). This agent provides high real-time and accuracy. It collects raw bit error counts for each FTTR slave port at a pre-set interval (e.g., every 5 seconds). This high-frequency data collection method can promptly capture even subtle changes in the bit error rate, providing a more accurate data foundation for subsequent fault diagnosis and a more comprehensive understanding of the actual bit error situation during data transmission. The raw bit error counts are collected by directly reading the bit error counts before and after FEC (forward error correction) correction from the optical module registers.

[0032] Link quality data refers to key parameters used to measure the transmission performance of optical fiber links. In FTTR networks, optical fiber links serve as the physical carrier for data transmission. Their transmission performance is directly related to the quality and stability of network services, and link quality data provides a quantitative basis for evaluating this performance.

[0033] For example, a PON network consisting of an FTTR slave unit (ONU), a mini OLT (uplink device), and optical fiber is deployed. In step S101, a monitoring agent is deployed in the mini OLT, which is connected to three ONUs. The mini OLT uses the monitoring agent to collect raw bit error rate and link quality data for each ONU every 5 seconds.

[0034] As an example, in step S102, fault detection is performed based on the original bit error rate and link quality data corresponding to each ONU, and the fault detection result corresponding to each ONU is determined. In this step, the original bit error rate is used to determine whether there is a fault in the PON network, and the link quality data is used to determine the source of the fault. If the original bit error rate of an ONU is abnormal, then the optical module or the corresponding optical fiber of the ONU must be faulty, and then the link quality data is used to determine whether the fault specifically comes from the optical module or the optical fiber. If the original bit error rate of an ONU is normal, it is considered that there is no fault in the optical module or the corresponding optical fiber of the ONU. Step S102 combines data from two dimensions, the original bit error rate and the link quality data, and can accurately determine the fault in the PON network and its source, thereby obtaining accurate fault detection results.

[0035] As an example, in step S103, when the fault detection result of an ONU indicates a fault, fault alarm processing is performed based on the original bit error rate and the target bit error rate threshold. The target bit error rate threshold is a pre-set bit error rate threshold or a bit error rate threshold dynamically determined based on actual conditions. For example, the bit error rates of each ONU within the seven days before the current moment can be used to screen out bit error rates within a normal range. Based on manual work, algorithms, or experience, the bit error rates within the normal range can then be used to screen or calculate the target bit error rate threshold. The target bit error rate threshold can also be dynamically optimized by introducing a machine learning algorithm. Specifically, a time series model is constructed using previously collected bit error rate data, and this data is deeply analyzed and predicted using a machine learning algorithm to determine the target bit error rate threshold. This not only more accurately reflects the changing trends in the normal bit error levels of each ONU in different network environments and usage scenarios, but also automatically adjusts the target bit error rate threshold based on the real-time operating status of the network. For example, when network traffic experiences a sudden increase or is affected by abnormal conditions such as external electromagnetic interference, the machine learning model can promptly perceive and adjust the target bit error rate threshold to ensure the accuracy and reliability of the fault detection results.

[0036] In this embodiment, by acquiring raw bit error rate and link quality data that carry a sub-station identifier, since the raw bit error rate and link quality data carry a unique sub-station identifier, it is possible to clearly identify the specific sub-station from which the raw bit error rate and link quality data originate. When a fault is determined based on this data, the sub-station with the problem can be precisely located based on the sub-station identifier. Furthermore, by combining the bit error rate and link quality data, the raw bit error rate reflects the probability of errors occurring during data transmission, while the link quality data describes the link status from another perspective. This multi-dimensional data can accurately identify PON network faults and generate timely alarms. When the fault detection result indicates a fault, fault alarm processing is executed based on the raw bit error rate and target bit error rate thresholds. This threshold-based alarm mechanism can trigger an alarm as soon as the fault occurs. Therefore, this method can address the issues of being unable to determine the source of faults in PON networks, inaccurate fault detection, and delayed alarms.

[0037] In one embodiment, if Figure 3 As shown, the fault detection is performed based on the original bit error rate and the link quality data to determine the fault detection result corresponding to the FTTR slave, including: S301: Determine a bit error detection result based on the original bit error rate; S302: Determine a link quality detection result based on the link quality data; S303: Determine a fault detection result based on the bit error detection result and the link quality detection result.

[0038] The raw bit error rate (BER) is a key metric for measuring the reliability of a communication system. The specific process for determining BER detection results is as follows: Data Collection: Using built-in counters or specialized test tools, periodically collect the total number of bits transmitted and the number of bits received with errors within a specified time window. For example, in a fiber-optic communication system, the optical module records the number of bits with errors after converting the received optical signal into an electrical signal. Calculating the Raw BER: Based on the collected data, use the formula "Raw BER = Number of Errored Bits / Total Number of Transmitted Bits" to calculate the BER. Assuming 10,000 bits are transmitted in 1 second and 5 of them are erroneous, the BER is 5 ÷ 10,000 = 0.0005 (or 0.05%). Setting a Threshold and Decision Making: Predetermine a reasonable BER threshold. If the calculated BER exceeds the threshold, a bit error problem is detected and the BER detection result is marked as abnormal. If it does not exceed the threshold, the BER is marked as normal.

[0039] Link quality data is an indicator of link transmission performance. The steps for determining link quality test results are as follows: Real-time collection of various link quality metrics, such as signal strength, power, signal-to-noise ratio, packet loss rate, and round-trip delay jitter, is performed. Each collected metric is analyzed based on its characteristics and industry standards. For example, a high signal-to-noise ratio generally indicates minimal interference and good transmission quality. A high packet loss rate, on the other hand, indicates significant data loss during transmission and poor link quality. The actual values ​​of each metric are compared against pre-defined normal ranges to determine whether each metric is normal. Finally, a comprehensive analysis of all metrics yields an accurate link quality test result.

[0040] Error detection results focus on identifying problems from the perspective of data transmission accuracy and can determine whether there are problems with the entire network. Link quality detection results evaluate the overall performance of the link and can determine which specific part of the network may have problems. Combining the two can more comprehensively and accurately locate faults and obtain fault detection results.

[0041] In one embodiment, step S301, i.e., determining the error detection result based on the original bit error rate, includes: If the average of all original bit error rates corresponding to the first time period is greater than the target bit error rate threshold, the bit error detection result is determined to be a fault; If the average of all original bit error rates corresponding to the first time period is not greater than the target bit error rate threshold, determining that the bit error detection result is that no fault exists; The first time period is a time period corresponding to a first preset duration before the current moment.

[0042] The raw bit error rate is a key indicator for measuring data transmission accuracy. When the mean raw bit error rate is greater than the target bit error rate threshold, it means that a large number of erroneous code elements have occurred during network data transmission.

[0043] As an example, mini OLT 2 collects the original bit error rate of each ONU once a minute. The first preset time length is one hour. A total of 60 original bit error rates of each ONU are collected one hour before the current time. Among them, the average of the 60 original bit error rates corresponding to ONU 21 is h, the average of the 60 original bit error rates corresponding to ONU 22 is j, and the average of the 60 original bit error rates corresponding to ONU 23 is k. The preset target bit error rate threshold is u. If h and j are both greater than u, and k is less than u, it is determined that the error detection results corresponding to ONU 21 and ONU 22 are faulty, and the error detection result corresponding to ONU 23 is fault-free.

[0044] In one embodiment, the target bit error rate threshold is determined based on a historical bit error rate corresponding to the second time period; The second time period is a time period corresponding to a second preset time length before the current moment, and the second preset time length is greater than the first preset time length.

[0045] The second time period refers to the period corresponding to the second preset duration before the current moment. This period is typically set to a longer duration, such as days, weeks, or even months. The specific duration is determined based on the actual network application scenario and stability requirements. If network stability requirements are high and service continuity is critical, the second preset duration may be set to one month. During this month, the system continuously collects bit error rate data from each ONU. This data reflects the long-term bit error level of each ONU under normal conditions, because over a long period of time, the network will experience various normal network fluctuations, such as different usage periods and different service load conditions. By collecting and analyzing the bit error rate data during this period, we can gain a more comprehensive understanding of the bit error patterns of the network under stable operation.

[0046] Correspondingly, the first time period is a time window that reflects the current bit error level. It is relatively short, typically measured in minutes or hours. For example, in this embodiment, the first preset time period is one hour. During this hour, the system frequently collects the raw bit error rate of each ONU to obtain the current real-time bit error status of the network. The short first preset time period allows for timely capture of instantaneous changes in the raw bit error rate of each ONU, allowing for rapid identification of potential fault risks.

[0047] The second time period is used to fully understand the bit error patterns of the network under stable operation and determine the long-term bit error level of the network under normal conditions. The first time period is used to collect the raw bit error rate of each ONU at a high frequency within a relatively short period of time to promptly capture instantaneous changes in the raw bit error rate of each ONU. Therefore, the corresponding second preset time period should be longer than the first preset time period.

[0048] In one embodiment, step S302, i.e., determining a link quality detection result based on the link quality data, includes: If there is a link quality data that is greater than the corresponding link quality threshold, determining that the link quality detection result is a link abnormality; If all the link quality data are not greater than the corresponding link quality threshold, determining that the link quality detection result is normal; The link quality data includes at least one of received optical power, transmitted optical power and signal-to-noise ratio.

[0049] The link quality threshold is the boundary value or reference standard used to determine whether a link is functioning properly. Received optical power represents the intensity of light reaching the ONU after the mini OLT transmits signals through fiber attenuation, splitting by the optical splitter, and connector loss. Transmitted optical power, emitted by the laser module within the ONU, reflects the ONU's transmission capability. The signal-to-noise ratio measures the ratio of useful information to noise in the received signal, reflecting the link's ability to resist interference. Different link quality thresholds correspond to different link quality data. For example, for the received optical power, the corresponding link quality threshold can be -25dBm. When the received optical power is less than or equal to -25dBm, the link quality detection result is determined to be abnormal. When the received optical power is greater than -25dBm, the link quality detection result is determined to be normal. For the transmitted optical power, the corresponding link quality threshold can be an interval, generally (-10dBm, -5dBm). When the transmitted optical power is within this threshold interval, the link quality detection result is determined to be normal. Otherwise, the link quality detection result is abnormal. For the signal-to-noise ratio, the corresponding link quality threshold can be 20dB. When the signal-to-noise ratio is greater than 20dB, the link quality detection result is determined to be normal. Otherwise, the link quality detection result is abnormal. In some other embodiments, these three types of data can also be combined to determine the link quality detection result, which will not be repeated here.

[0050] In one embodiment, step S303, i.e., determining a fault detection result based on the bit error detection result and the link quality detection result, includes: If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is normal, determining that the optical fiber is damaged; If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is abnormal, it is determined that the optical module is faulty.

[0051] The error detection result is used to determine whether the PON network is faulty, while the link quality test result is used to determine the source of the fault. These two dimensions are combined to determine the fault detection result. If the error detection result for an ONU indicates a fault, it generally indicates that the corresponding optical module or fiber is faulty.

[0052] From the perspective of fiber optic transmission principles, optical fibers are primarily responsible for the physical transmission of optical signals. During transmission, optical fibers may become damaged due to external forces, such as compression, bending, and aging. Fiber damage does not necessarily directly change link quality data, but it can increase optical signal loss and scattering during transmission, leading to an increase in the bit error rate (BER). For example, in real-world applications, if a fiber is accidentally squeezed during building renovations, the BER will significantly increase, even though the link quality data will not change significantly. Therefore, if the bit error detection result indicates a fault and the link quality test result indicates a normal link, the fiber is damaged.

[0053] Optical modules are key devices for converting optical and electrical signals, playing a central role in signal transmission, reception, and processing. Therefore, optical module failures are evident in link quality data, disrupting the stable transmission state of the entire link, increasing the bit error rate and causing link anomalies. Therefore, if both the bit error detection result indicates a fault and the link quality test result indicates an abnormal link, the likelihood of an optical module failure is very high.

[0054] In one embodiment, step S103, i.e., performing fault alarm processing based on the original bit error rate and the target bit error rate threshold, includes: If the average of all original bit error rates corresponding to the first time period is greater than a first alarm threshold, a mild alarm is performed, where the first alarm threshold is the product of the target bit error rate threshold and a first coefficient, and the value range of the first coefficient is 0-1; If the average of all original bit error rates corresponding to the first time period is greater than the second alarm threshold for a duration greater than a third preset duration, a severe alarm is executed, where the second alarm threshold is the product of the target bit error rate threshold and a second coefficient, and the second coefficient is greater than 1.

[0055] If the raw bit error rate average exceeds the first alarm threshold, a minor alarm is issued. The first alarm threshold is the product of the target bit error rate threshold and the first coefficient, which ranges from 0 to 1. This setting is designed to promptly alert operators when minor network anomalies occur, allowing operations and maintenance personnel to quickly address potential issues and prevent them from escalating. For example, if the target bit error rate threshold is 10⁻⁶ and the first coefficient is 0.5, the first alarm threshold is 5×10⁻⁷. When the raw bit error rate average of an FTTR slave exceeds this threshold, the system issues a minor alarm. This minor alarm can be achieved by marking the slave's icon yellow (or other prominent symbol) on the network management system interface and displaying a message in the system message bar, informing operators that the slave's bit error rate is slightly above normal and requires attention. Detailed information about the alarm, including the alarm time, slave ID, and the current raw bit error rate average, is also recorded for easy query and analysis.

[0056] If the average raw bit error rate exceeds the second alarm threshold for a period longer than a third preset duration, a severe alarm is issued. The second alarm threshold is the product of the target bit error rate threshold and a second coefficient, where the second coefficient is greater than 1. This indicates a serious network failure, significantly impacting normal service operations and requiring immediate action from operations and maintenance personnel. For example, if the target bit error rate threshold remains at 10⁻⁶ and the second coefficient is 2, the second alarm threshold becomes 2×10⁻⁶. Assuming the third preset duration is set to 5 minutes, if the average raw bit error rate of a particular FTTR slave exceeds 2×10⁻⁶ and this high bit error rate persists for more than 5 minutes, the system will issue a severe alarm. Severe alarms are more urgent and diverse. In addition to a prominent red flag and an emergency alert pop-up in the network management system, an alarm message is also sent to the responsible personnel via SMS, email, or a dedicated operations and maintenance app. The SMS message contains detailed fault information, such as "[specific time], [slave ID], [fault duration]." At the same time, a fault work order is automatically generated to record key information such as the time of the fault, the location of the slave unit, the bit error rate data, and the duration, making it easier for operation and maintenance personnel to quickly locate and solve the problem.

[0057] This hierarchical alarm mechanism not only helps operation and maintenance personnel quickly determine the severity of network faults, but also enables them to take appropriate measures based on different fault levels, thereby improving the efficiency of network fault handling and ensuring the stable and reliable operation of the PON network.

[0058] The embodiment of the present invention also provides a PON network fault detection device, such as Figure 4 FIG. 1 is a schematic diagram of a PON network fault detection device according to an embodiment of the present invention. The device can implement the above-mentioned PON network fault detection method, including: The data acquisition module 41 is used to obtain the original bit error rate and link quality data of the FTTR slave; a fault detection module 42, configured to perform fault detection based on the original bit error rate and the link quality data, and determine a fault detection result corresponding to the FTTR slave; an alarm module 43, configured to, when the fault detection result indicates that a fault exists, perform fault alarm processing based on the original bit error rate and the target bit error rate threshold; The target bit error rate threshold is a preset bit error rate threshold, or a bit error rate threshold dynamically determined according to actual conditions.

[0059] An embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the aforementioned PON network fault detection method when executing the program. To avoid repetition, this description is omitted. Alternatively, this electronic device can implement the functions of each module in the aforementioned PON network fault detection device embodiment, which is omitted.

[0060] An embodiment of the present invention further provides a readable storage medium storing a program, wherein when executed by a processor, the program implements the aforementioned PON network fault detection method, which is not described in detail here to avoid repetition. Alternatively, when executed by a processor, the program implements the functions of the various modules in the aforementioned PON network fault detection device embodiment, which is not described in detail here.

[0061] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A PON network fault detection method, characterized in that: include: Obtaining raw bit error rate and link quality data carrying a slave identifier, where the slave identifier is a unique identifier corresponding to the FTTR slave; Performing fault detection based on the original bit error rate and the link quality data, and determining a fault detection result corresponding to the slave identifier; When the fault detection result indicates that a fault exists, a fault alarm process is performed based on the original bit error rate and the target bit error rate threshold.

2. The PON network fault detection method according to claim 1, characterized in that: The performing fault detection based on the original bit error rate and the link quality data and determining the fault detection result corresponding to the slave identifier includes: Determining a bit error detection result based on the original bit error rate; Determining a link quality detection result based on the link quality data; A fault detection result is determined based on the bit error detection result and the link quality detection result.

3. The PON network fault detection method according to claim 2, characterized in that: The determining of the bit error detection result based on the original bit error rate includes: If the average of all original bit error rates corresponding to the first time period is greater than the target bit error rate threshold, the bit error detection result is determined to be a fault; If the average of all original bit error rates corresponding to the first time period is not greater than the target bit error rate threshold, determining that the bit error detection result is that no fault exists; The first time period is a time period corresponding to a first preset duration before the current moment.

4. The PON network fault detection method according to claim 3, characterized in that: The target bit error rate threshold is determined based on the historical bit error rate corresponding to the second time period; The second time period is a time period corresponding to a second preset time length before the current moment, and the second preset time length is greater than the first preset time length.

5. The PON network fault detection method according to claim 2, characterized in that: The determining a link quality detection result based on the link quality data includes: If there is a link quality data that is greater than the corresponding link quality threshold, determining that the link quality detection result is a link abnormality; If all the link quality data are not greater than the corresponding link quality threshold, determining that the link quality detection result is normal; The link quality data includes at least one of received optical power, transmitted optical power and signal-to-noise ratio.

6. The PON network fault detection method according to claim 2, characterized in that: The determining a fault detection result based on the bit error detection result and the link quality detection result includes: If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is normal, determining that the optical fiber is damaged; If the error detection result indicates that a fault exists and the link quality detection result indicates that the link is abnormal, it is determined that the optical module is faulty.

7. The PON network fault detection method according to claim 1, characterized in that: The performing fault alarm processing based on the original bit error rate and the target bit error rate threshold includes: If the average of all original bit error rates corresponding to the first time period is greater than a first alarm threshold, a mild alarm is performed, where the first alarm threshold is the product of the target bit error rate threshold and a first coefficient, and the value range of the first coefficient is 0-1; If the average of all original bit error rates corresponding to the first time period is greater than the second alarm threshold for a duration greater than a third preset duration, a severe alarm is executed, where the second alarm threshold is the product of the target bit error rate threshold and a second coefficient, and the second coefficient is greater than 1.

8. A PON network fault detection device, characterized in that: include: A data acquisition module is used to obtain the original bit error rate and link quality data carrying the slave identifier, where the slave identifier is a unique identifier corresponding to the FTTR slave; a fault detection module, configured to perform fault detection based on the original bit error rate and the link quality data, and determine a fault detection result corresponding to the slave identifier; The alarm module is used to perform fault alarm processing based on the original bit error rate and the target bit error rate threshold when the fault detection result indicates that a fault exists.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the PON network fault detection method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium storing a program, characterized in that: When the program is executed by a processor, the PON network fault detection method according to any one of claims 1 to 7 is implemented.

Images