WO2024109604A1

WO2024109604A1 - Fault analysis method and apparatus for pon system, and device

Info

Publication number: WO2024109604A1
Application number: PCT/CN2023/131722
Authority: WO
Inventors: 陈爱民; 陆云; 余辰东
Original assignee: 中兴通讯股份有限公司
Priority date: 2022-11-21
Filing date: 2023-11-15
Publication date: 2024-05-30
Also published as: CN118101416A

Abstract

Disclosed in the embodiments of the present application are a fault analysis method and apparatus for a PON system, and a device. The fault analysis method for a PON system comprises: extracting a feature index according to performance index data in a PON system within a set time period; on the basis of a set expert rule, performing fault analysis on the feature index to determine a target fault analysis rule matching the feature index, wherein the expert rule comprises fault types, a fault analysis sequence that is set on the basis of the fault types, and at least one fault analysis rule corresponding to each fault type; and determining a fault location and a fault root cause according to the target fault analysis rule.

Description

A method, device and equipment for fault analysis of PON system

cross reference

The present invention claims priority to a Chinese patent application filed with the Patent Office of China on November 21, 2022, with application number 202211453353.8 and invention name “A fault analysis method, device and equipment for a PON system”. The entire contents of the application are incorporated by reference into the present invention.

Technical Field

The present application relates to the field of optical communications, and in particular to a fault analysis method, device and equipment for a PON system.

Background technique

PON (Passive Optical Network) is a time-division communication system that adopts a P2MP (point-to-multipoint) topology. It consists of central office equipment OLT (Optical Line Terminal), ODN (optical distribution network), and user-side equipment ONU (Optical Network Unit). The OLT device aggregates multiple ONU devices through the PON port, forming a point (OLT) to multipoint (ONU) topology.

In the actual PON system, each PON port can be connected to 1 to 128 ONU devices. Due to the differences in ONUs from different manufacturers and the differences in ODN, a large number of error statistics and alarm information will be generated at the local PON port. These error statistics and alarm information may be caused by failures in one or more of the ONU devices themselves or on the ODN optical link. However, for the operators of the PON system, since it is impossible to accurately identify the real source of the fault from a large amount of error statistics and alarm information, it is impossible to achieve effective dispatch processing, and wrong dispatch will cause a large amount of human resources waste. The current fault maintenance mode is basically a passive operation and maintenance state of dispatching orders based on user reports, so it is urgent to provide a solution that can accurately identify the source of faults in the PON system.

Summary of the invention

The purpose of the embodiments of the present application is to provide a fault analysis method, device and equipment for a PON system, which can improve the accuracy of fault identification in the PON system.

In order to achieve the above objectives, the present application embodiment adopts the following technical solutions:

In a first aspect, a fault analysis method for a passive optical network (PON) system is provided, comprising: extracting characteristic indicators based on performance indicator data in the PON system within a set time period; performing fault analysis on the characteristic indicators based on set expert rules, and determining target fault analysis rules that match the characteristic indicators; wherein the expert rules include fault types, a fault analysis sequence set based on the fault types, and at least one fault analysis rule corresponding to each fault type; and locating the fault location and the root cause of the fault according to the target fault analysis rules.

In a second aspect, a fault analysis device for a passive optical network PON system is provided, comprising: an extraction module, used to extract characteristic indicators based on performance indicator data in the PON system within a set time period; a fault analysis module, used to perform fault analysis on the characteristic indicators based on set expert rules, and determine target fault analysis rules that match the characteristic indicators; wherein the expert rules include fault types, fault analysis sequences set based on fault types, and at least one fault analysis rule corresponding to each fault type; and a positioning module, used to locate the fault location and the root cause of the fault according to the target fault analysis rules.

According to a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and the programs or instructions are executed by the processor to implement the method described in the first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a schematic diagram of the correspondence between a fault type, a fault location and a fault root cause in a PON system provided by an embodiment of the present application;

FIG2 is a schematic flow chart of a fault analysis method for a PON system provided by an embodiment of the present application;

FIG3 is a schematic diagram of a fault analysis sequence in an expert rule provided by an embodiment of the present application;

FIG4 is a schematic diagram of a fault analysis method in a PON system provided by an embodiment of the present application;

FIG5 is a schematic diagram of a performance indicator data fluctuation trend of a degraded PON optical module provided by an embodiment of the present application;

FIG6 is a schematic structural diagram of a fault analysis device for a PON system provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solution and advantages of this application clearer, the technical solution of this application will be clearly and completely described in combination with the specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here. In addition, "and/or" in the specification and claims represents at least one of the connected objects, and the character "/" generally represents that the objects associated with each other are in an "or" relationship.

As mentioned above, the PON system is a time-division communication system that adopts a P2MP topology structure. It consists of the central office equipment OLT, ODN, and the user-end equipment ONU. The OLT equipment is located in the central office, provides a network interface for the ODN and is connected to one or more ODNs. Its function is to provide the necessary transmission mode for the services required by the ONU equipment; the ODN is located between the OLT equipment and the ONU equipment. It is composed of active devices and has a passive distribution function. Its role is to provide an optical transmission channel between the OLT device and the ONU device; the ONU device is located on the user side, provides a user side interface and is connected to the ODN. It is generally located in a building or a user's home, and is used to achieve user access to the optical access network. The ONU device includes an ONU optical module, which is used to receive and transmit light and is an important component of the ONU device. In an actual PON system, the OLT device usually hangs 1 to 16 PON boards, each of which provides 8 to 16 PON ports. Each PON port is equipped with a PON optical module, and each PON port can hang 1 to 128 ONU devices. The PON optical module is a high-performance optical module used in the PON system, also known as a PON module. The PON optical module uses different wavelengths to send and receive signals between the OLT device and the ONU device.

In actual PON systems, a large amount of error statistics and alarm information will be generated at the central office PON port, but it is impossible to accurately identify the real fault source from the large amount of error statistics and alarm information. The current popular method for processing such a large amount of error statistics and alarm information is to use AI (Artificial Intelligence) algorithms based on big data analysis, and to establish AI models through a large number of samples of feature data to achieve fault classification and identification. However, due to the lack of a large amount of effective sample data in the existing network, the use of AI models alone for fault analysis and identification is still far from practical, and there is still no substantial breakthrough in fault demarcation and positioning of the entire PON system.

In the actual operation and maintenance of PON systems, operators have their own set of troubleshooting experience based on the basic principles of PON systems and personal experience when dealing with PON system faults. However, manual processing efficiency is too low, experience sharing has personal understanding bias, lack of practical cognition and other issues, making it difficult to quickly improve the skills of operators in a short period of time.

In view of this, the embodiment of the present application aims to provide a fault analysis method for a PON system, by which the accuracy of fault identification in the PON system can be improved. In the embodiment of the present application, by converting expert experience into rules and supporting user-defined rules, the PON system is equipped with expert intelligence for handling faults, which can achieve rapid fault diagnosis and root cause location of most faults. On the basis of rule analysis, AI technology can also be used for fault learning and reasoning for those difficult faults that are difficult to identify through rule design, which can enhance the ability to identify the root cause of the fault, thereby forming a complete An expert analysis system for automatic fault diagnosis that supports self-learning.

The main technical concepts of this application include the following contents, please refer to Figure 1:

First, according to the basic principles and fault distribution of the PON system, PON system faults (optical access network faults) are divided into four types, including: PON optical module faults, ONU equipment faults, ODN structure faults, and ODN quality faults. Among them, PON optical module faults and ONU equipment faults can be collectively referred to as equipment failures.

Secondly, since equipment failure and ODN structure failure will cause ONU disconnection, ONU bit error, weak light reception, too strong light reception and other problems, in order to distinguish ODN quality failures, the fault analysis order should be set based on the fault type. The fault analysis order is from first to last: equipment failure analysis, ODN structure failure analysis, and ODN quality failure analysis; among them, equipment failure analysis includes PON optical module failure analysis and ONU equipment failure analysis. It should be noted that there is no need to set a sequence between PON optical module failure analysis and ONU equipment failure analysis. PON optical module failure analysis can be performed first, or ONU equipment failure analysis can be performed first, or both can be performed at the same time. The fault analysis order is strongly related to the network architecture of the PON system. From the root cause bottom layer to the upper layer, when there is packet loss and disconnection in the optical link, it may be caused by equipment failure first, and secondly by unreasonable ODN structure. Only by eliminating the above-mentioned fault causes can it be determined that it belongs to the ODN optical link quality cause.

Again, for each fault type, a list of possible root causes of faults is determined, and a corresponding fault analysis method is designed for each root cause of faults. The fault analysis method is used to indicate the fault identification conditions that need to be met by specific performance indicators. The root causes of faults and the corresponding fault analysis methods can form a fault analysis rule; the fault type, the fault analysis sequence set based on the fault type, and at least one fault analysis rule corresponding to each fault type together constitute an expert rule for fault analysis. Each fault type can also indicate the corresponding fault location.

Finally, a comprehensive analysis is performed based on the fault analysis results obtained by analyzing the four fault types in sequence to determine whether it is a device fault, an ODN structure fault, or an ODN quality fault, as well as the root cause and location of the fault.

It should be understood that the fault analysis method of the PON system provided in the embodiment of the present application can be implemented by the PON The system is executed by the network management server corresponding to the system or by the software installed in the network management server, and can also be executed by the OLT device in the PON system or by the software installed in the OLT device. The fault analysis method of the PON system provided in the embodiment of the present application is applicable to any PON (Passive Optical Network) communication system that adopts a P2MP (point-to-multipoint) topology in the optical access network. The adoption of the P2MP topology is a notable feature of the PON system, and according to the working mechanism, it can be divided into EPON (Ethernet Passive Optical Network, Ethernet-based passive optical network) system, G-PON (Gigabit-capable Passive Optical Network) system, and passive optical network systems with other working mechanisms. These passive optical network systems have the same architecture, and all include OLT equipment, ODN and ONU equipment.

The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

Please refer to FIG. 2 , which is a flow chart of a method for fault analysis of a PON system provided by an embodiment of the present application. The method may include:

S201. Extract characteristic indicators according to performance indicator data in a PON system within a set time period.

Specifically, characteristic indicators can be extracted from basic data. Basic data refers to performance indicator data in the PON system within a period of time (such as the last 4 days). The performance indicator data in the PON system can include PON performance indicator data and ONU performance indicator data. Usually, PON port is used as a unit to collect PON performance indicator data and ONU performance indicator data under each PON port, such as upstream and downstream service traffic, bit errors (packet loss, wrong packet, BIP, etc.), received light power, luminous power, current, voltage, temperature, and alarms (including offline alarms, power-off alarms, signal degradation alarms, optical module type mismatch alarms, etc.) of PON optical modules and ONU devices. The extraction of characteristic indicators can be achieved by classifying and normalizing these basic data.

The extracted characteristic indicators may include multiple characteristic data corresponding to at least one performance indicator, and the multiple characteristic data corresponding to each performance indicator are used to reflect the characteristics of the performance indicator. For different types of performance indicators, the corresponding multiple characteristic data can be flexibly set. For basic performance data, it can include the maximum value, minimum value, average value, parameter values of the previous N times, parameter values of the most recent M times, parameter values that exceed the limit, number of times of exceeding the limit, volatility, etc. of the performance indicator; For alarm data, the alarm time interval, alarm number, alarm frequency, etc. of the performance indicators may be included. For example, the performance indicator is PON luminous power. If the basic data is collected once an hour, there will be 96 data in 4 days. Among the 96 PON luminous power time series data, the extracted multiple characteristic data corresponding to the PON luminous power may include the maximum value, minimum value, average value, variance, the first power value, the power values of the last three times, the power value that exceeds the limit (for example, greater than 10dBm or less than -35dBm can be regarded as exceeding the limit), the number of times of exceeding the limit, stability, volatility, etc.

S202. Perform fault analysis on the characteristic indicator based on set expert rules to determine a target fault analysis rule that matches the characteristic indicator; wherein the expert rules include a fault type, a fault analysis sequence set based on the fault type, and at least one fault analysis rule corresponding to each fault type.

In an optional implementation, the fault types in the expert rules may include PON optical module fault, ONU device fault, ODN structure fault, and ODN quality fault; the fault analysis sequence may be device fault analysis, ODN structure fault analysis, and ODN quality fault analysis, wherein the device fault analysis includes PON optical module fault analysis and ONU device fault analysis. Optionally, each fault type may also indicate a corresponding fault location.

Specifically, according to the fault analysis order, at least one fault analysis rule corresponding to each fault type can be extracted in turn to perform fault analysis on the characteristic indicator; if there is a target fault analysis rule matching the characteristic indicator in the current fault type, the fault analysis ends; if there is no target fault analysis rule matching the characteristic indicator in the current fault type, at least one fault analysis rule corresponding to the next fault type is extracted to perform fault analysis on the characteristic indicator, until a target fault analysis rule matching the characteristic indicator is determined.

For example, as shown in FIG3 , when fault analysis is performed based on expert rules, the PON optical module and the ONU device may be analyzed first to see if they are faulty. If at least one of the PON optical module and the ONU device is faulty, the fault analysis ends. If these two parts are not faulty, the ODN structure is analyzed to see if it is faulty. If so, the fault analysis ends. If the first three parts are not faulty, the final analysis is to see if there is a faulty ODN structure. ODN quality failure, that is, the ODN optical link quality is poor.

In an optional implementation, each fault analysis rule may include a root cause of the fault and a corresponding fault analysis method, and the fault analysis method is used to indicate the fault identification conditions that a specific performance indicator needs to meet. Please refer to Figure 4. For each fault type, the corresponding at least one fault analysis rule may include two categories, namely, fault identification using a threshold-based configuration rule and fault identification using an AI model, or only one of them. The threshold-based configuration rule can be flexibly configured and managed according to actual needs. For faults that are difficult to identify by threshold using configuration rules, an AI model can be used for fault identification. The basic data for fault analysis mainly depends on the performance indicator data of each PON optical module and the corresponding ONU device over a period of time. These basic data can be classified and normalized to extract feature indicators, and then the configuration rules are used to identify the fault and locate the root cause of the fault; for feature indicators that cannot be determined to have a fault, an AI model can be further used to identify whether there is a fault. The AI model can select general models such as neural networks and decision trees to train fault samples, and can be continuously expanded through the learning of fault samples. For faults that are difficult to identify intuitively, the root cause of the fault can be determined by combining AI learning with image analysis of fluctuation frequency, decline amplitude, etc. Finally, the fault analysis results obtained by analyzing the four fault types in sequence are combined to conduct a comprehensive analysis to locate the fault location and root cause.

Specifically, the PON optical module fault analysis mainly identifies at least one of the following faults:

The PON optical module is broken; the PON optical module's luminous power is too high; the PON optical module's luminous power is too low; the PON optical module's luminous power is unstable; the PON optical module's interface status is abnormal; the PON optical module's background noise is too high; the PON optical module has quality problems.

Please refer to Table 1 for the fault location of the PON optical module fault indication and the corresponding at least one fault analysis rule.

Table 1

For faults that are difficult to identify through thresholds using configuration rules, AI models can be used for identification. For PON optical module quality faults, PON optical module fault identification models can be used for AI learning and identification. Please refer to Figure 5, which is a schematic diagram of the fluctuation trend of performance indicator data of a degraded PON optical module. All performance indicators (temperature, bias current, luminous power, voltage) are normal. It is difficult to judge that the PON optical module is faulty through threshold-based configuration rules. However, by analyzing the fluctuation trend of performance indicator data, it can be found that there is a situation where the temperature rises sharply and the luminous power decreases at the same time, indicating that the automatic adjustment mechanism inside the PON optical module has partially failed and cannot maintain a stable output of luminous power in high temperature scenarios. It is a faulty PON optical module, but this kind of fault cannot be judged by thresholds and is more suitable for identification through AI models.

Specifically, ONU equipment fault analysis mainly identifies at least one of the following faults:

ONU power supply failure; ONU optical module light power is too high; ONU optical module light power is too low; ONU optical module light is unstable; ONU optical module is always on; ONU optical module light is random; ONU equipment quality failure.

Please refer to Table 2 for the fault location of the ONU device fault indication and the corresponding at least one fault analysis rule.

Table 2

For ONU equipment quality failures, the ONU equipment fault identification model can be used for AI learning and identification.

Specifically, the ODN structure fault analysis mainly identifies at least one of the following faults: the splitting ratio is too large; the splitting ratio is too small; the PON port light receiving dynamic range is too large.

Please refer to Table 3 for the fault location of the ODN structure fault indication and the corresponding at least one fault analysis rule.

table 3

ODN link quality fault analysis mainly identifies the fault category of poor ODN optical link quality. If you want to more carefully identify whether it belongs to the fault category of interface contamination, optical fiber macrobend, optical fiber skin breakage, poor welding, etc., you can use the ODN quality fault identification model for AI learning and identification. For the fault category of poor ODN optical link quality, you can perform cluster analysis based on the fault distribution or combine it with ODN resource data to further locate the fault location, whether it is the trunk section, branch section or home section.

Please refer to Table 4 for the fault location of the ODN link quality fault indication and the corresponding at least one fault analysis rule.

Table 4

Since the characteristic index includes multiple characteristic data corresponding to at least one performance index, for each performance index, the corresponding multiple characteristic data may be the maximum value, the minimum value, the parameter value of the most recent N times, the parameter value that exceeds the limit, the number of times the limit is exceeded, etc. When determining the target fault analysis rule that matches the characteristic index, the characteristic index may be compared with each fault analysis rule corresponding to the current fault type, and if the multiple characteristic data corresponding to the specific performance index in the characteristic index conform to the fault analysis method in the current fault analysis rule (used to indicate the fault identification condition that the specific performance index needs to meet), the current fault analysis rule is determined as the target fault analysis rule that matches the characteristic index.

S203: Locate the fault location and the root cause of the fault according to the target fault analysis rule.

After completing the fault analysis of the four fault types, the final conclusion is output based on the fault analysis results to determine whether it is a PON optical module fault, ONU equipment fault, ODN structure fault or ODN quality fault, and locate the fault location and root cause.

Specifically, according to the determined target fault analysis rule, the fault location is the fault location indicated by the fault type corresponding to the target fault analysis rule, and the root cause of the fault is the target fault analysis rule. The root cause of the fault indicated by the analysis rule.

Through the above-mentioned fault analysis method of the PON system, for characteristic indicators that are relatively easy to identify, the configuration rules in the fault analysis rules can be used to quickly and accurately locate faults through thresholds, which can reduce the complexity and data volume of AI model learning, making the content of AI model learning more concise and the analysis results more accurate. Regardless of whether there is fault sample data or not, the complete PON system fault intelligent analysis and accurate positioning can be achieved, and it can also support the learning and intelligent analysis capabilities of difficult faults.

The fault analysis method corresponding to each fault type is described in detail below in conjunction with the embodiments.

1. PON optical module failure analysis

In the case where the fault type is a PON optical module fault, the fault location indicated by the fault type is the PON optical module; at least one fault analysis rule corresponding to the PON optical module fault includes one or any combination of the following, that is, if one of the following rules is met, it can be determined as a PON optical module fault:

1) When the root cause of the fault is a bad PON optical module, the corresponding fault analysis method is that the PON light emitting power is less than the set no-light power threshold, and the bias current is less than the set current threshold.

For example, if the most recent PON light emitting power is less than a no-light power threshold (eg, -35 dBm) and the bias current is less than a current threshold (eg, 2 mA), it can be determined that the PON optical module is broken.

2) When the root cause of the fault is that the light power of the PON optical module is too high, the corresponding fault analysis method is that the light power of the PON optical module is greater than the set first high power threshold.

Exemplarily, if the most recent PON light power is greater than a high power threshold (such as 10 dBm), it can be determined that the light power of the PON optical module is too high.

3) When the root cause of the fault is that the PON optical module light power is too low, the corresponding fault analysis method is that the PON light power is less than the set first low power threshold and greater than or equal to the set no light power threshold.

Exemplarily, if the most recent PON light power is less than a low power threshold (such as 2 dBm) but greater than a no light power threshold (such as -35 dBm), it can be determined that the light power of the PON optical module is too low.

4) When the root cause of the fault is unstable light emission of the PON optical module, the corresponding fault analysis method is that the fluctuation amplitude between two adjacent PON light emission powers is greater than the set first amplitude threshold. The number and ratio are greater than the set first number threshold and first ratio threshold.

For example, if the basic data is collected once an hour, there will be 96 data in 4 days. In these 96 PON luminous power time series data, the number and proportion of the fluctuation amplitude of the two adjacent PON luminous power is greater than the amplitude threshold (such as 1dB) exceeds the number threshold and the proportion threshold (such as 4 times, 5%), which can be determined as the PON optical module's luminescence is unstable.

5) When the root cause of the fault is that the interface status of the PON optical module is abnormal, the corresponding fault analysis method is that the interface status of the PON optical module is not ready.

Exemplarily, if the interface of the PON optical module is in an unprepared state most recently, it can be determined that the state of the PON optical module interface is abnormal.

6) When the root cause of the fault is that the background noise of the PON optical module is too large, the corresponding fault analysis method is that the background noise of the PON optical module is greater than the set noise threshold.

Exemplarily, if the background noise of the PON optical module is greater than the noise threshold value most recently, it can be determined that the background noise of the PON optical module is too large.

7) When the root cause of the fault is a quality fault of the PON optical module, the corresponding fault analysis method is to input the characteristic indicator into a pre-trained PON optical module fault identification model to identify the PON optical module quality fault when other fault analysis rules do not match, and the output identification result is that there is a quality fault.

For example, if it is impossible to determine whether the PON optical module is faulty through other fault analysis rules, such as faults other than the first 6 types mentioned above, a PON optical module fault identification model can be used to further identify whether there are PON optical module degradation characteristics. If the identification result is yes, it can be determined as a PON optical module quality fault.

It can be understood that the first 6 items mentioned above belong to fault identification using threshold-based configuration rules, and the 7th item belongs to fault identification using an AI model.

2. ONU equipment failure analysis

In the case where the fault type is an ONU device fault, the fault location indicated by the fault type is the ONU device; at least one fault analysis rule corresponding to the ONU device fault includes one of the following Or any combination, that is, if any of the following rules are met, it can be determined as an ONU device failure:

1) When the root cause of the fault is an ONU power failure, the corresponding fault analysis method is that the number of times the time interval between two adjacent ONU power-off alarms is less than a set first time threshold satisfies a set first number condition.

Exemplarily, if the number of ONU power failure alarms with a short duration (e.g., less than 1 minute) is greater than a threshold value (e.g., 10 times) and occurs for multiple consecutive days (e.g., greater than or equal to 2 days), it can be determined as an ONU power failure.

2) When the root cause of the fault is that the ONU optical module's luminous power is too high, the corresponding fault analysis method is that the ONU luminous power is greater than the set second highest power threshold.

Exemplarily, if the most recent ONU luminous power is greater than a power threshold (such as 9 dBm), it can be determined that the luminous power of the ONU optical module is too high.

3) When the root cause of the fault is that the light power of the ONU optical module is too low, the corresponding fault analysis method is that the light power of the ONU is less than the set second lowest power threshold.

Exemplarily, if the most recent ONU luminous power is less than a power threshold (eg, -10 dBm), it can be determined that the luminous power of the ONU optical module is too low.

4) When the root cause of the fault is unstable light emission of the ONU optical module, the corresponding fault analysis method is that the number and proportion of the fluctuation amplitude between two adjacent ONU light emission powers greater than the set second amplitude threshold meet the set second number threshold and second proportion threshold.

For example, if the basic data is collected once an hour, there will be 96 data in 4 days. In these 96 ONU luminous power time series data, the number of times and ratio of the ONU luminous power fluctuation amplitude of 2 adjacent powers is greater than the amplitude threshold (such as 1dB) exceeds the threshold (such as 4 times, 5%), which can be determined as the ONU optical module luminescence is unstable.

5) When the root cause of the fault is the prolonged light emission of the ONU optical module, the corresponding fault analysis method is that among the most recent online time and the most recent offline time of each ONU device under the same PON port, there is a situation where other ONU devices greater than the set first number automatically go online within the second time threshold set after a certain ONU device goes offline.

Exemplarily, the data of the most recent online time and the most recent offline time of each ONU under the same PON port are analyzed. If a certain ONU goes offline and multiple other ONUs (for example, more than 4) automatically go online within a short period of time (for example, within 5 minutes), it can be determined that the ONU optical module is long-lighting.

6) When the root cause of the fault is the random light emission of the ONU optical module, the corresponding fault analysis method is that among the most recent online time and the most recent offline time of each ONU device under the same PON port, there is a situation where other ONU devices greater than the set second number go offline one after another within the third time threshold set after a certain ONU device goes online.

Exemplarily, the most recent online time and the most recent offline time data of each ONU under the same PON port are analyzed. If after a certain ONU goes online, multiple other ONUs (for example, more than 4) go offline one after another within a short period of time (for example, within 5 minutes), it can be determined that the ONU optical module is emitting light indiscriminately.

7) When the root cause of the fault is an ONU equipment quality fault, the corresponding fault analysis method is to input the characteristic indicator into a pre-trained ONU equipment fault identification model to identify the ONU equipment quality fault when other fault analysis rules do not match, and the output identification result is that there is a quality fault.

For example, if it is impossible to determine whether the ONU device is faulty through other fault analysis rules, such as faults other than the first 6 types mentioned above, the ONU device fault identification model can be used to further identify whether there are ONU poor quality characteristics. If the identification result is yes, it can be determined as an ONU device quality failure.

3. ODN structure failure analysis

In the case where the fault type is an ODN structure fault, the fault location indicated by the fault type includes the ODN optical splitting structure; at least one fault analysis rule corresponding to the ODN structure fault includes one of the following or any combination thereof, that is, under the premise of a non-PON optical module fault and a non-ONU device fault, a fault that meets one of the following rules can be determined as an ODN structure fault:

1) When the root cause of the fault is that the splitting ratio is too large, the corresponding fault analysis method is The uplink attenuation and the downlink attenuation are both greater than a set first attenuation threshold.

When the most recent uplink attenuation and downlink attenuation of an ONU are both greater than an attenuation threshold (for example, 33 dB), it can be determined that the splitting ratio of the ONU is too large.

2) When the root cause of the fault is that the splitting ratio is too small, the corresponding fault analysis method is that the ONU received optical power is greater than the set first power threshold, or the PON received optical power is greater than the set second power threshold.

Exemplarily, when the most recent ONU received optical power is greater than a power threshold (eg, −10 dBm) or the PON received optical power is greater than a power threshold (eg, −12 dBm), it can be determined that the splitting ratio is too small.

3) When the root cause of the fault is that the dynamic range of the PON port light reception is too large, the corresponding fault analysis method is that the absolute difference between the maximum and minimum values of the ONU light reception power under the PON port exceeds the set power difference threshold.

Exemplarily, when the absolute difference between the maximum and minimum values of the received optical power of each ONU under the PON port exceeds the power difference threshold (such as 15 dB), it can be determined that the PON ports where these ONUs are located have a problem of excessive received optical dynamic range.

It can be understood that the above three items all belong to fault identification using threshold-based configuration rules.

4. ODN quality failure analysis

In the case where the fault type is an ODN quality fault, the fault location indicated by the fault type includes an ODN optical link; the at least one fault analysis rule corresponding to the ODN quality fault includes one of the following or any combination thereof, that is, under the premise of a non-PON optical module fault, a non-ONU device fault, and a non-ODN structure fault, if one of the following rules is met, it can be determined as an ODN quality fault, and the root cause of the fault is poor quality of the ODN optical link, and the corresponding fault analysis method is at least one of the following:

1) The ONU received optical power is less than the set third power threshold.

Exemplarily, the most recent ONU received optical power is less than a power threshold (eg, -27 dBm).

2) The PON received optical power is less than the set fourth power threshold.

Exemplarily, the most recent PON received optical power is less than a power threshold (eg, -29 dBm).

3) The number of times and the proportion of the fluctuation amplitude between two adjacent ONU received optical powers that is greater than the set third amplitude threshold are greater than the set third number threshold and third proportion threshold.

Exemplarily, the number of times and the proportion of the ONU received optical power fluctuation amplitude (eg, -27 dBm) exceeding the limit are greater than the number threshold and the proportion threshold (eg, 3 times, 5%).

4) The number of times and the ratio of the fluctuation amplitude between two adjacent PON received optical powers being greater than the set fourth amplitude threshold are greater than the set fourth number threshold and fourth ratio threshold.

Exemplarily, the number of times and the ratio of the PON received optical power fluctuation amplitude (eg, −29 dBm) exceeding the limit are greater than the number threshold and the ratio threshold (eg, 3 times, 5%).

5) The upstream attenuation of the ONU is greater than the set second attenuation threshold.

Exemplarily, the most recent uplink attenuation of the ONU is greater than an attenuation threshold (eg, 29 dB).

6) The downstream attenuation of the ONU is greater than the set third attenuation threshold.

Exemplarily, the most recent downstream attenuation of the ONU is greater than an attenuation threshold (eg, 29 dB).

7) The number of ONU offline alarms meets the set second number condition.

Exemplarily, the average number of offline alarms of the ONU per day is greater than a number threshold (for example, 1 time) and the total number is greater than a number threshold (for example, 6 times).

8) The number of ONU power-off alarms meets the set third number condition.

Exemplarily, the average number of power-off alarms of the ONU per day is greater than a number threshold (for example, 4 times) and the total number is greater than a number threshold (for example, 10 times).

9) The number and proportion of each type of ONU bit error exceeding the limit are greater than the set fifth number threshold and fifth proportion threshold.

Exemplarily, the number and proportion of each type of ONU bit error exceeding the limit are greater than the number threshold and the proportion threshold (for example, the number of times the BIP exceeds 100 is greater than 5 times, and the proportion of the exceeding limit times to the total times is greater than 30%).

10) The ONU ranging length is greater than the set length threshold.

Exemplarily, the ONU ranging length is greater than a length threshold (eg, 15 km).

11) The absolute difference between the upstream attenuation and the downstream attenuation of the ONU is greater than the set attenuation difference threshold.

Exemplarily, the absolute difference between the downstream attenuation and the upstream attenuation of the ONU is greater than an attenuation difference threshold (for example, 6 dB).

It can be understood that the above 11 items all belong to fault identification using threshold-based configuration rules.

If you want to analyze in more detail whether the ODN quality fault belongs to the fault categories such as interface contamination, optical fiber macrobend, optical fiber breakage, poor fusion, etc., you can use the ODN quality fault identification model for AI learning and identification. When other fault analysis rules do not match, the characteristic index is input into the pre-trained ODN quality fault identification model for ODN quality fault identification, and the ODN quality fault category output by the ODN quality fault identification model is obtained. The root cause of the fault is indicated as the corresponding ODN quality fault category, and the ODN quality fault category may include interface contamination, optical fiber macrobend, optical fiber breakage, and poor fusion.

The fault analysis method of the PON system provided in the embodiment of the present application introduces expert rules, which include fault types, fault analysis sequences set based on fault types, and at least one fault analysis rule corresponding to each fault type; after extracting characteristic indicators according to the performance indicator data in the PON system, the characteristic indicators can be analyzed in sequence according to the fault analysis sequence based on the set expert rules, and the target fault analysis rules matching the characteristic indicators can be determined, and then the fault location and root cause of the fault can be located according to the target fault analysis rules. The fault type and the fault analysis sequence set based on the fault type are consistent with the fault distribution of the PON system and the network architecture, and the characteristic indicators are analyzed in sequence according to the fault analysis sequence. Based on rule matching, the fault in the PON system can be quickly identified, and the root cause and location of the fault can be accurately located, thereby improving the accuracy of fault identification in the PON system.

In addition, corresponding to the fault analysis method of the PON system shown in FIG. 2 above, an embodiment of the present application further provides a fault analysis device for a PON system. An embodiment of the present application provides a fault analysis device 600 for a PON system, as shown in FIG. 6 , comprising:

The extraction module 601 is used to extract characteristic indicators according to the performance indicator data in the PON system within a set time period; the fault analysis module 602 is used to perform fault analysis on the characteristic indicators based on the set expert rules and determine the target fault analysis rules that match the characteristic indicators; wherein the The expert rules include fault types, fault analysis sequences set based on the fault types, and at least one fault analysis rule corresponding to each fault type; the positioning module 603 is used to locate the fault location and the root cause of the fault according to the target fault analysis rules.

Specifically, the fault types may include PON optical module failure, ONU equipment failure, ODN structure failure, and ODN quality failure; the fault analysis sequence may be equipment failure analysis, ODN structure failure analysis, and ODN quality failure analysis, and the equipment failure analysis includes PON optical module failure analysis and ONU equipment failure analysis.

In an optional implementation, a possible structure of the fault analysis module 602 includes: a sequential control submodule 621, which is used to extract at least one fault analysis rule corresponding to each fault type in turn according to the fault analysis sequence and send it to the fault analysis submodule 622 for fault analysis; if there is a target fault analysis rule matching the characteristic indicator in the current fault type, the fault analysis ends; if there is no target fault analysis rule matching the characteristic indicator in the current fault type, at least one fault analysis rule corresponding to the next fault type is extracted and sent to the fault analysis submodule 622 for fault analysis, until the target fault analysis rule matching the characteristic indicator is determined; a fault molecule module 622, which is used to perform fault analysis on the characteristic indicator according to at least one fault analysis rule corresponding to the current fault type, and determine whether there is a target fault analysis rule matching the characteristic indicator in the current fault type.

Specifically, the characteristic indicator includes multiple characteristic data corresponding to at least one performance indicator; the fault analysis rule includes a root cause of the fault and a corresponding fault analysis method, and the fault analysis method is used to indicate the fault identification conditions that need to be met by a specific performance indicator; the fault analysis submodule 622 is specifically used to: compare the characteristic indicator with each fault analysis rule corresponding to the current fault type, if the multiple characteristic data corresponding to the specific performance indicator in the characteristic indicator conform to the fault analysis method in the current fault analysis rule, then determine the current fault analysis rule as a target fault analysis rule that matches the characteristic indicator.

In an optional implementation, each fault type indicates a corresponding fault location; the positioning module 603 is specifically used to: locate the fault location according to the target fault analysis rule. The fault location indicated by the fault type corresponding to the target fault analysis rule, and the located fault root cause is the fault root cause indicated by the target fault analysis rule.

Obviously, the fault analysis device of the PON system of the embodiment of the present application can be used as the execution subject of the fault analysis method of the PON system shown in Figure 2, so the method can realize the functions realized in Figure 2. Since the principles are the same, they will not be repeated here.

The fault analysis device of the PON system provided in the embodiment of the present application can quickly identify faults in the PON system, accurately locate the root cause and the location of the fault, thereby improving the accuracy of fault identification in the PON system.

Optionally, as shown in Figure 7, an embodiment of the present application also provides an electronic device 700, including a processor 701 and a memory 702, and the memory 702 stores a program or instruction that can be executed on the processor 701. When the program or instruction is executed by the processor 701, the various steps of the fault analysis method of the above-mentioned PON system are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

It should be noted that the electronic devices in the embodiments of the present application include mobile electronic devices and non-mobile electronic devices.

An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned PON system fault analysis method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned PON system fault analysis method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned PON system fault analysis method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

A fault analysis method for a passive optical network (PON) system, comprising:

Extract characteristic indicators based on performance indicator data in the PON system within a set time period;

Performing fault analysis on the characteristic indicator based on the set expert rules to determine a target fault analysis rule that matches the characteristic indicator; wherein the expert rules include a fault type, a fault analysis sequence set based on the fault type, and at least one fault analysis rule corresponding to each fault type;

The fault location and the root cause of the fault are located according to the target fault analysis rule.
The method according to claim 1, wherein the fault types include PON optical module fault, optical network unit (ONU) equipment fault, optical distribution network (ODN) structure fault, and ODN quality fault;

The fault analysis sequence is equipment fault analysis, ODN structure fault analysis, and ODN quality fault analysis. The equipment fault analysis includes PON optical module fault analysis and ONU equipment fault analysis.
The method according to claim 2, wherein the performing fault analysis on the characteristic indicator based on the set expert rules and determining the target fault analysis rule matching the characteristic indicator specifically comprises:

According to the fault analysis sequence, at least one fault analysis rule corresponding to each fault type is extracted in sequence to perform fault analysis on the characteristic indicator;

If there is a target fault analysis rule matching the characteristic index in the current fault type, the fault analysis ends;

If there is no target fault analysis rule matching the characteristic indicator in the current fault type, at least one fault analysis rule corresponding to the next fault type is extracted to perform fault analysis on the characteristic indicator until a target fault analysis rule matching the characteristic indicator is determined.
The method according to claim 3, wherein the characteristic index includes at least one The fault analysis rule includes a fault root cause and a corresponding fault analysis method, and the fault analysis method is used to indicate a fault identification condition that needs to be met by a specific performance indicator;

The method for determining a target fault analysis rule matching the characteristic indicator comprises:

The characteristic indicator is compared with each fault analysis rule corresponding to the current fault type. If multiple characteristic data corresponding to specific performance indicators in the characteristic indicator conform to the fault analysis method in the current fault analysis rule, the current fault analysis rule is determined as the target fault analysis rule that matches the characteristic indicator.
The method according to claim 4, wherein each fault type indicates a corresponding fault location;

The locating the fault location and the root cause of the fault according to the target fault analysis rule specifically includes:

According to the target fault analysis rule, the located fault position is the fault position indicated by the fault type corresponding to the target fault analysis rule, and the located fault root cause is the fault root cause indicated by the target fault analysis rule.
The method according to claim 5, wherein, in the case where the fault type is a PON optical module fault, the fault location indicated by the fault type includes the PON optical module;

The at least one fault analysis rule corresponding to the PON optical module fault includes one or any combination of the following:

If the root cause of the fault is a bad PON optical module, the corresponding fault analysis method is that the PON light power is less than the set no-light power threshold, and the bias current is less than the set current threshold;

When the root cause of the fault is that the PON optical module has excessive light power, the corresponding fault analysis method is that the PON light power is greater than the set first high power threshold;

When the root cause of the fault is that the PON optical module light power is too low, the corresponding fault analysis method is that the PON light power is less than the set first low power threshold and greater than or equal to the set no light power threshold;

When the root cause of the fault is unstable light emission of the PON optical module, the corresponding fault analysis method is: The number of times and the ratio of the fluctuation amplitude between two adjacent PON light emitting powers being greater than the set first amplitude threshold are greater than the set first number threshold and first ratio threshold;

When the root cause of the fault is the abnormal state of the PON optical module interface, the corresponding fault analysis method is that the interface state of the PON optical module is not ready;

When the root cause of the fault is that the background noise of the PON optical module is too large, the corresponding fault analysis method is that the background noise of the PON optical module is greater than the set noise threshold;

When the root cause of the fault is a quality failure of the PON optical module, the corresponding fault analysis method is to input the characteristic indicator into a pre-trained PON optical module fault identification model to identify the PON optical module quality failure when other fault analysis rules do not match, and the output identification result is that a quality failure exists.
The method according to claim 5, wherein, in the case where the fault type is an ONU device fault, the fault location indicated by the fault type includes the ONU device;

The at least one fault analysis rule corresponding to the ONU device fault includes one or any combination of the following:

In the case where the root cause of the fault is an ONU power failure, the corresponding fault analysis method is that the number of times the time interval between two adjacent ONU power failure alarms is less than the set first time threshold satisfies the set first number condition;

If the root cause of the fault is that the ONU optical module's light power is too high, the corresponding fault analysis method is that the ONU light power is greater than the set second highest power threshold;

If the root cause of the fault is that the ONU optical module's light power is too low, the corresponding fault analysis method is that the ONU light power is less than the set second lowest power threshold;

When the root cause of the fault is unstable light emission of the ONU optical module, the corresponding fault analysis method is that the number of times and the ratio of the fluctuation amplitude between two adjacent ONU light emission powers that are greater than the set second amplitude threshold value meet the set second number threshold value and second ratio threshold value;

If the root cause of the fault is that the ONU optical module is always on, the corresponding fault analysis method is to use the most recent online time and the most recent offline time of each ONU device under the same PON port. There is a situation where a certain ONU device goes offline and other ONU devices greater than the set first number automatically go online within a set second time threshold;

When the root cause of the fault is the random light emission of the ONU optical module, the corresponding fault analysis method is that among the most recent online time and the most recent offline time of each ONU device under the same PON port, there is a situation where other ONU devices greater than the set second number are offline one after another within the set third time threshold after a certain ONU device goes online;

When the root cause of the fault is an ONU equipment quality fault, the corresponding fault analysis method is to input the characteristic indicator into a pre-trained ONU equipment fault identification model to identify the ONU equipment quality fault when other fault analysis rules do not match, and the output identification result is that there is a quality fault.
The method according to claim 5, wherein, in the case where the fault type is an ODN structure fault, the fault location indicated by the fault type includes an ODN optical splitting structure;

The at least one fault analysis rule corresponding to the ODN structure fault includes one or any combination of the following:

When the root cause of the fault is that the splitting ratio is too large, the corresponding fault analysis method is that the upstream attenuation and downstream attenuation of the ONU are both greater than the set first attenuation threshold;

When the root cause of the fault is that the splitting ratio is too small, the corresponding fault analysis method is that the ONU received optical power is greater than the set first power threshold, or the PON received optical power is greater than the set second power threshold;

When the root cause of the fault is that the dynamic range of the PON port light reception is too large, the corresponding fault analysis method is that the absolute difference between the maximum and minimum values of the ONU light reception power under the PON port exceeds the set power difference threshold.
The method according to claim 5, wherein, in the case where the fault type is an ODN quality fault, the fault location indicated by the fault type includes an ODN optical link;

The at least one fault analysis rule corresponding to the ODN quality fault includes at least one of the following:

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the ONU received optical power is less than the set third power threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the PON received optical power is less than the set fourth power threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the number of times and the proportion of the fluctuation amplitude between two adjacent ONU received optical powers that are greater than the set third amplitude threshold are greater than the set third number threshold and third proportion threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the number of times and the ratio of the fluctuation amplitude between two adjacent PON received optical powers that are greater than the set fourth amplitude threshold are greater than the set fourth number threshold and fourth ratio threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the upstream attenuation of the ONU is greater than the set second attenuation threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the downstream attenuation of the ONU is greater than the set third attenuation threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the number of ONU offline alarms meets the set second number condition;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the number of ONU power-off alarms meets the set third number condition;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the number and proportion of each type of ONU bit error exceeding the limit are greater than the set fifth number threshold and fifth proportion threshold;

When the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the ONU ranging length is greater than the set length threshold;

In the case where the root cause of the fault is poor quality of the ODN optical link, the corresponding fault analysis method is that the absolute difference between the upstream attenuation and the downstream attenuation of the ONU is greater than the set attenuation difference threshold.
A fault analysis device for a PON system, comprising:

An extraction module, used to extract characteristic indicators based on performance indicator data in the PON system within a set time period;

A fault analysis module is used to perform fault analysis on the characteristic indicators based on set expert rules. Determine a target fault analysis rule that matches the characteristic indicator; wherein the expert rule includes a fault type, a fault analysis sequence set based on the fault type, and at least one fault analysis rule corresponding to each fault type;

The positioning module is used to locate the fault location and the root cause of the fault according to the target fault analysis rule.
An electronic device comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and the program or instruction is executed by the processor to implement the method as claimed in any one of claims 1 to 9.
A readable storage medium, wherein a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the method according to any one of claims 1 to 9 is implemented.