A distribution network communication device fault analysis method and system

Abstract

The application discloses a kind of distribution network communication equipment fault analysis method and system, specifically related to communication equipment fault analysis technical field, method includes three steps: first, equipment association topology is constructed and state synchronization is realized, and equipment association is established by dynamic weight calculation;Then, fault propagation analysis and root location are carried out, and the conduction path is verified based on the calculation of fault probability associated with topology;Finally, dynamic diagnosis and collaborative decision are implemented, and diagnostic conclusions are formed by multidimensional diagnostic matrix and weighted voting mechanism.System correspondingly includes topology management module, fault analysis module, diagnostic decision module and feedback optimization module.The application solves the problem of inaccurate fault location and high false alarm rate of traditional methods through equipment association topology modeling and collaborative analysis mechanism, can accurately identify fault root, and significantly improves the fault diagnosis accuracy and operation efficiency of distribution network communication equipment.

Description

Technical Field

[0001] This invention relates to the field of communication equipment fault analysis technology, and more specifically, to a method and system for fault analysis of distribution network communication equipment. Background Technology

[0002] As a core component of smart distribution networks, distribution network communication equipment undertakes crucial functions such as data acquisition, command transmission, and equipment monitoring. With the continuous expansion of distribution network scale and the ongoing improvement of its intelligence level, the number of communication devices has increased dramatically, and the connections between them have become increasingly complex. Against this backdrop, the reliability of communication equipment operation directly affects the safe and stable operation of the entire distribution network. However, existing fault analysis methods mainly rely on parameter monitoring of single devices and alarm mechanisms with fixed thresholds, which are insufficient to meet the actual needs of multi-device collaborative operation in complex distribution network environments.

[0003] Current fault analysis methods for distribution network communication equipment suffer from the following technical shortcomings: First, traditional methods are typically limited to the independent analysis of individual devices, neglecting the interrelationships between devices. When faults propagate between devices, the root cause cannot be accurately located. Second, diagnostic mechanisms based on fixed thresholds lack adaptability and are prone to false alarms or missed alarms due to environmental changes or equipment aging. Third, centralized fault diagnosis models suffer from high response latency and a high risk of single-point failures, making them unsuitable for distribution network communication scenarios with high real-time requirements. These limitations result in significant deficiencies in the accuracy of fault location, diagnostic efficiency, and adaptability of existing systems.

[0004] While recent research has attempted to introduce intelligent algorithms to improve fault diagnosis, most studies remain at the level of data analysis for single devices, lacking in-depth consideration of device-related topology and collaborative diagnostic mechanisms. Some proposed complex models suffer from high computational resource requirements and implementation costs in practical deployments. Therefore, there is an urgent need for a fault analysis method that can effectively utilize device-related information, possesses adaptive capabilities, and is easy to implement, in order to improve the reliability and operational efficiency of distribution network communication systems. Summary of the Invention

[0005] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a method and system for fault analysis of distribution network communication equipment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for fault analysis of distribution network communication equipment includes the following steps: S1. Topology Modeling and State Synchronization: By collecting device configuration information, a device association topology diagram is constructed, dynamic association weights considering device criticality and time decay are defined, and an adaptive state synchronization mechanism is established to ensure that each device can obtain the operating status of associated devices in a timely manner. S2. Fault Propagation and Root Cause Location: When an anomaly is detected by the equipment, a fault propagation model is constructed based on the associated topology. The probability of each associated equipment being the root cause of the fault is calculated by weighted scoring. Uncertainty is introduced to quantify the reliability of the probability. Finally, the most likely fault propagation path is determined through simulation verification. S3. Dynamic Diagnosis and Collaborative Decision-Making: Establish a multi-dimensional diagnostic decision matrix, dynamically adjust the diagnostic threshold and rules of this device according to the status of related devices, adopt a weighted voting mechanism to integrate the diagnostic opinions of this device and related devices to form a final conclusion, and continuously improve the diagnostic accuracy through feedback optimization to form a closed-loop diagnostic optimization mechanism.

[0007] Specifically, S1 includes: Collect device configuration information to construct a device association topology diagram; Define the association weights between devices, which are dynamically calculated based on the association type, device criticality level, and historical fault data; Establish a device status synchronization mechanism and dynamically adjust the status synchronization cycle according to network load.

[0008] Specifically, S2 includes: When the device detects abnormal parameters, a fault impact propagation model is constructed based on the associated topology. Calculate the probability value of each associated device as the root cause of the fault. The probability calculation takes into account factors such as fault indicators, time sequence and parameter correlation. Verify the fault propagation path and generate a fault impact analysis report.

[0009] Specifically, S3 includes: Establish a multi-dimensional diagnostic decision matrix and dynamically adjust diagnostic thresholds based on the status of associated devices; A weighted voting mechanism was used to synthesize the diagnostic opinions of this equipment and related equipment. The system parameters are optimized based on the diagnostic results to form a closed-loop diagnostic mechanism.

[0010] Specifically, the probability calculation also introduces an uncertainty quantification mechanism: It provides a reliability assessment of the probability calculation results and optimizes the failure propagation path verification strategy based on the uncertainty assessment results.

[0011] Specifically, the state synchronization mechanism includes: Monitor the quality indicators of communication links between devices; The frequency and amount of data for status synchronization are dynamically adjusted based on communication quality. Initiate event-triggered synchronization when an anomaly is detected.

[0012] A fault analysis system for distribution network communication equipment includes: The topology management module is used to build device association topologies and manage the state synchronization between devices; The fault analysis module is used to perform fault propagation analysis and locate the root cause of the fault based on the associated topology. The diagnostic decision module is used to perform dynamic diagnostics and execute collaborative decisions based on the status of associated devices. The feedback optimization module is used to optimize system parameters based on diagnostic results.

[0013] Specifically, the topology management module includes: Topology building unit, used to collect device configuration information and build a dynamically updated associated topology graph; The weight calculation unit is used to calculate the dynamic correlation weights that take into account the criticality level of the equipment and historical fault data. The synchronization control unit is used to dynamically adjust the state synchronization strategy according to the network load.

[0014] The fault analysis module includes: The propagation analysis unit is used to construct a fault impact propagation model and calculate the probability of fault root causes. The verification execution unit is used to verify the fault propagation path through simulation testing. The report generation unit is used to generate an analysis report that includes the root cause of the fault and its propagation path.

[0015] The technical effects and advantages of this invention are as follows: By establishing a device association topology and status synchronization mechanism, this invention enables precise location and rapid diagnosis of faults in distribution network communication equipment. Compared with existing technologies, this invention can effectively identify the fault transmission relationship between devices, accurately distinguish between faults in the device itself and anomalies caused by associated devices, improve the accuracy of fault root cause location by more than 40%, and significantly improve the accuracy and efficiency of fault diagnosis.

[0016] By introducing dynamic weight calculation, uncertainty quantification, and closed-loop optimization mechanisms, this invention possesses excellent adaptability and continuous improvement characteristics. The system can automatically adjust its diagnostic strategy based on network conditions, effectively reducing the false alarm rate by more than 30%. Simultaneously, it continuously improves diagnostic performance through feedback optimization, providing strong support for the reliable operation of the distribution network communication system. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the steps of a fault analysis method for distribution network communication equipment are as follows: Step 1: Topology Modeling and State Synchronization: By collecting device configuration information, a device association topology diagram is constructed. Dynamic association weights that consider device criticality and time decay are defined, and an adaptive state synchronization mechanism is established to ensure that each device can obtain the operating status of associated devices in a timely manner, providing a data foundation for subsequent fault analysis. Collect equipment configuration information of the distribution network communication system, including equipment type, physical location, communication link connection relationship, and power supply relationship basic data, and construct an equipment association topology map, where nodes represent communication equipment and edges represent the association relationship between equipment, including direct communication link connection, same power supply branch, same control domain, etc.

[0020] The equipment configuration information of the distribution network communication system is collected in the following ways: access the database of the distribution network management system (DMS) or network management platform (NMS), and automatically read the neighbor discovery protocol (such as LLDP), routing table information, physical port connection relationship, and predefined power supply relationship and logical group relationship in the system configuration file through SNMP (Simple Network Management Protocol), NETCONF (Network Configuration Protocol) or vendor private API interface.

[0021] The specific process of constructing a device association topology graph is as follows: using devices as nodes and communication links, power lines, or logical affiliations as edges, a dynamic network topology graph is constructed and maintained in memory using graph theory algorithms (such as adjacency lists or adjacency matrices). This topology graph supports real-time updates, and automatically triggers topology reconstruction when a new device is detected to be online or when the connection relationships of existing devices change.

[0022] Define device association weights and set influence coefficients based on the association type and distance between devices. For example, the weight of devices with direct communication connections is set to 0.8, the weight of devices on the same power supply branch but not directly connected is set to 0.5, and the weight of devices that are physically adjacent but not directly related is set to 0.3. The weight values ​​are obtained through statistical analysis of historical fault data.

[0023] The device association weights are defined not only based on static association type and distance, but also by introducing dynamic influence factors. The initial weight W... initialThe weight is set according to the association type (e.g., 0.8 for direct communication connections). Then, it is adjusted based on the historical fault co-occurrence rate: weight W = W initial +α×R co Among them, R co This represents the proportion of times two devices simultaneously fail within the same time window (e.g., within 5 minutes) out of the total number of failures in historical data; α is a correction coefficient (e.g., 0.1) used to balance the influence of the initial value and historical data. Furthermore, if the device is in standby mode, its association weight is temporarily reduced to 0.1.

[0024] Based on the existing weighting calculation, a key equipment level factor and a time decay factor are introduced: Equipment critical level factor A basic criticality coefficient is assigned to each device based on its importance to network services. This includes: core routing / switching devices. Regional convergence equipment: 0; Edge access devices: 0.8; Time decay factor: The influence of historical fault data should not be permanent. An exponential decay mechanism is introduced to ensure that older fault co-occurrence records have a smaller impact on current weights. For each historical fault co-occurrence record, its contribution after decay over time is... Represented as: ; in: The decay rate constant is This is the time difference between the current time and the time the fault occurred. Comprehensive weight calculation: The above factors are combined to obtain the final dynamic correlation weight. : ; in: This is the sum of the time-decayed contributions of all co-occurring fault events in the history of the device.

[0025] A device status synchronization mechanism is established, whereby each device periodically sends its own status parameters (including device operating status, key performance indicators, and fault flags) to associated devices via the communication network. The synchronization period is dynamically adjusted according to network load, set to 5 minutes under normal conditions and shortened to 30 seconds when an anomaly is detected. The dynamic adjustment of the synchronization period follows the following quantitative strategy: With the basic period T base (e.g., 5 minutes) as a baseline. Real-time monitoring of the round-trip time (RTT) and packet loss rate of the communication link between this device and associated devices; When RTT < 50ms and Loss < 1%, the network load is considered light, and the synchronization period remains T. base .

[0026] When 50ms ≤ RTT ≤ 200ms or 1% ≤ Loss ≤ 5%, it is considered that the network load is within the range, and the synchronization period is adjusted to T. base ×2.

[0027] When RTT > 200ms or Loss > 5%, the network load is considered heavy, and the synchronization period is adjusted to T. base ×4, and only synchronize key status flags to reduce network pressure.

[0028] When any associated device reports a fault flag or the device detects an abnormal local parameter, regardless of the network load, event-triggered synchronization is immediately initiated for that device group, with the cycle temporarily shortened to 10 seconds, and resumed after 3 cycles.

[0029] Each device maintains a local associated device status table, recording the real-time status information, status update timestamp, and association weight of the associated devices. When the status information has not been updated within a valid time (such as 3 synchronization cycles), the associated device status is marked as unknown.

[0030] Status data verification is implemented. The receiving device performs integrity and logical checks on the status data sent by the associated device. When data anomalies are detected, a retransmission mechanism is initiated to ensure the reliability of status information.

[0031] Step 2, Fault Propagation and Root Cause Location: When an anomaly is detected by the device, a fault propagation model is constructed based on the associated topology. The probability of each associated device being the root cause of the fault is calculated by weighted scoring, and uncertainty is introduced to quantify the reliability of the probability. Finally, the most likely fault propagation path is determined through simulation verification to achieve accurate root cause location.

[0032] When this device detects abnormal parameters, it initiates a cross-device impact analysis process. First, it reads the status table of associated devices to obtain the current status and historical status change sequence of all associated devices.

[0033] A fault propagation model is constructed to analyze the propagation path of abnormal parameters between devices based on the device association topology and weights. For example, for signal quality degradation anomalies, the impact on upstream devices is analyzed along the communication link; for power supply anomalies, the impact on power supply-side devices is analyzed along the power supply branch. The constructed fault propagation model is a causal inference model based on a probabilistic graphical model (such as a Bayesian network). The network nodes represent device states (normal / abnormal), and the edges represent the associations between devices and their weights. When device D... i When an anomaly is detected, the model starts from that node and performs reverse reasoning along the edges of the topology graph to calculate the posterior probability of the abnormal state of all upstream nodes (i.e., possible cause devices).

[0034] The root cause probability calculation is performed. For the current anomaly, the probability value of each associated device being the root cause of the fault is calculated. The probability calculation is based on the following factors: whether the associated device has reported a fault, the time order of the anomaly (the device that experienced the anomaly earlier has a higher probability), the association weight (the larger the weight, the higher the probability of influence), and the correlation of the anomaly parameters (the higher the consistency of the parameter change trend, the greater the correlation). A weighted scoring function is used to perform the root cause probability calculation. For each associated device C... j The score S, as the root cause of the fault j The calculation formula is as follows: S j =W j ×[β1×F j +β2×T j +β3×P j ] Among them: W j Equipment C j The association weight with this device; F j : Boolean value, C j Is a fault currently reported (1 if yes, 0 otherwise); T j : Time sequence factor, if C j If the anomaly occurs earlier than this device, then T j =1, otherwise T j =0.5; P j Parameter correlation, calculated by comparing this device with C j The outlier parameters are obtained from the Pearson correlation coefficient on the time series, with a value range of [0,1]; β1, β2, β3: are the weight coefficients of each factor, which are obtained through training with historical data, and can be set to 0.5, 0.3, 0.2 respectively.

[0035] S of all associated devices j Normalization yields the probability value of each device as a root cause of a fault.

[0036] In the original weighted scoring function S j =W j ×[β1×F j +β2×T j +β3×P j In this context, each input variable may inherently possess uncertainty; therefore, an uncertainty coefficient is introduced for each variable. 0 indicates complete certainty, and 1 indicates complete uncertainty.

[0037] Calculate the total score S using the uncertainty propagation law. j Synthesis uncertainty The formula is: ; For historical reliability calculations based on equipment status reports, To calculate based on timestamp accuracy and synchronization error, Pearson correlation coefficient The uncertainty is measured by confidence intervals or sample size; Each associated device is output as a root cause of the fault, with a single probability value. Extended to a representation with uncertainty: ; in The coverage factor (usually 1 or 2); or directly record the binary tuples. ; The larger the value, the lower the reliability of the probability conclusion; To make S j The base probability value obtained after normalization.

[0038] Decision optimization based on uncertainty: Transmission path verification phase: Prioritize verifying those probabilities High and uncertain Low-probability paths; for high-probability but high-uncertainty paths, reduce the weight of their verification conclusions; in the analysis report generation stage: clearly mark high-uncertainty conclusions in the report, prompting maintenance personnel to pay extra attention or supplement information. Collaborative diagnostic scenario: the device will express the uncertainty of its own conclusions. The data is sent together, and the recipient dynamically adjusts the trust weight of the conclusion accordingly.

[0039] Fault propagation path verification is performed by simulating the fault propagation process and comparing actual anomalies with theoretical propagation results to verify the rationality of the fault propagation path. Specifically, this involves changing the state parameters of related devices and observing the degree of impact on the abnormal parameters of the device itself; a higher degree of impact indicates a more reliable propagation path. The specific method involves using the network management system to send signals to the suspected related device C... j Sending a simulated query command (such as a high-frequency status read request) will slightly increase C. j The CPU load or network traffic is monitored. Simultaneously, changes in abnormal parameters (such as response latency) are continuously monitored on this device. If synchronous, interpretable fluctuations in these abnormal parameters are observed on this device, it verifies the connection from C. j The fault propagation path to this device is reliable. The extent of the impact is quantified by the product of the amplitude and duration of parameter fluctuations.

[0040] Generate a fault impact analysis report, including a list of suspected fault source devices, a fault propagation path diagram, and a credibility score for each path. Share the analysis results with related devices for collaborative diagnostic decision-making.

[0041] Step 3: Dynamic Diagnosis and Collaborative Decision-Making: Establish a multi-dimensional diagnostic decision matrix, dynamically adjust the diagnostic threshold and rules of this device according to the status of related devices, use a weighted voting mechanism to integrate the diagnostic opinions of this device and related devices to form a final conclusion, and continuously improve the diagnostic accuracy through feedback optimization to form a closed-loop diagnostic optimization mechanism.

[0042] A multi-dimensional diagnostic decision matrix is ​​established. Matrix parameters include the status parameters of the local device, status parameters of associated devices, association weights, and time-series features. Each parameter has a base weight, determined through device importance and parameter sensitivity analysis. The established multi-dimensional diagnostic decision matrix is ​​a mapping table between feature vectors and decision outputs. The matrix input is a feature vector V=[L,A,W,TS]. Here, L represents the standardized values ​​of the local device parameters, A is the set of abnormal status flags for associated devices, W is the corresponding set of association weights, and TS is the time-series feature (such as the variance of recent parameters). The specific strategy for dynamically adjusting diagnostic thresholds and rules is as follows: Set a base threshold Th for each diagnostic parameter of this device (such as received signal strength). base When upstream device U reports an anomaly, the correlation weight W of U is used to determine the appropriate action. u The effective threshold for this parameter of this device is dynamically adjusted according to the following formula: Th effective =Th base ×(1+SC×W u ). Among them, Th effective The adjusted effective threshold is SC, which is a relaxation coefficient (e.g., 0.1). When an upstream device with a higher correlation weight experiences an anomaly, the alarm threshold for this device for this parameter is appropriately relaxed to avoid secondary false alarms. W u The association weight between the upstream device U that reports anomalies and this device.

[0043] Implement associated status awareness to monitor the status changes of associated devices in real time, especially critical status changes such as fault status, performance degradation status, and communication interruption status. When a sudden change in the status of associated devices is detected, the diagnostic strategy adjustment process is initiated.

[0044] Dynamically adjust diagnostic thresholds and rules, and optimize fault judgment conditions of this device based on the status of related devices. For example, when upstream communication equipment reports abnormal signal transmission power, appropriately relax the alarm threshold for the received signal strength of this device; when power supply side equipment reports voltage fluctuations, adjust the sensitivity of power monitoring of this device.

[0045] Collaborative diagnostic decision-making is implemented, integrating the analysis results of this equipment with diagnostic opinions from related equipment. A weighted voting mechanism is used to reach a final diagnostic conclusion. Weight allocation is based on the equipment's historical reliability record and the degree of correlation; equipment with higher reliability receives a higher voting weight. Equipment reliability history record R device Represented by a value between 0 and 1, with an initial value of 0.5. After each collaborative diagnosis, the device's diagnostic conclusion is compared with the finally confirmed root cause of the fault. If the diagnosis is correct, then R... device =R device +δ×(1-R device If the diagnosis is incorrect, then R device =R device ×(1-δ). Where δ is the learning rate (e.g., 0.1).

[0046] When implementing collaborative diagnostic decisions, the voting weight VW of each associated device is... j Its correlation weight W j With reliability record R devic The product of VW j =W j ×R devic The final decision is determined by the weighted average of all voting devices, thus ensuring that devices with a reliable historical track record have a greater say in the diagnostic process.

[0047] Implement diagnostic result feedback optimization, compare the final diagnostic results with the preliminary diagnoses of each device, calculate the diagnostic accuracy rate, and dynamically optimize the association weights and diagnostic rules based on the accuracy rate to form a closed-loop diagnostic mechanism for continuous improvement.

[0048] A fault analysis system for distribution network communication equipment, comprising the following modules: The topology management module automatically collects device configuration information, establishes a dynamically updated device relationship topology map, defines a comprehensive weight that considers device criticality level and time decay factor, and implements a device status synchronization mechanism based on network status self-adaptation, providing a complete device relationship foundation for fault analysis.

[0049] The fault analysis module constructs a fault propagation model based on topological relationships, determines the probability of each device as a fault root cause through multi-factor weighted probability calculation, introduces an uncertainty quantification mechanism to evaluate the reliability of the analysis results, and finally determines the most likely fault propagation path through simulation verification.

[0050] The diagnostic decision module establishes a multi-dimensional diagnostic decision matrix, dynamically adjusts diagnostic thresholds and rules based on the status of associated equipment, and adopts a weighted voting mechanism that considers equipment reliability to synthesize the diagnostic opinions of various equipment and form the final fault diagnosis conclusion.

[0051] The feedback optimization module dynamically updates equipment reliability records and associated weights by comparing diagnostic results with actual conditions, calculates diagnostic accuracy, and optimizes diagnostic rules, forming a self-improving closed-loop optimization mechanism to ensure continuous improvement of the system's diagnostic capabilities.

[0052] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0053] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0054] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0058] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for fault analysis of distribution network communication equipment, characterized in that, The steps include the following: S1. Topology Modeling and State Synchronization: By collecting device configuration information, a device association topology diagram is constructed, dynamic association weights considering device criticality and time decay are defined, and an adaptive state synchronization mechanism is established to ensure that each device can obtain the operating status of associated devices in a timely manner. S2. Fault Propagation and Root Cause Location: When an anomaly is detected by the equipment, a fault propagation model is constructed based on the associated topology. The probability of each associated equipment being the root cause of the fault is calculated by weighted scoring. Uncertainty is introduced to quantify the reliability of the probability. Finally, the most likely fault propagation path is determined through simulation verification. S3. Dynamic Diagnosis and Collaborative Decision-Making: Establish a multi-dimensional diagnostic decision matrix, dynamically adjust the diagnostic threshold and rules of this device according to the status of related devices, adopt a weighted voting mechanism to integrate the diagnostic opinions of this device and related devices to form a final conclusion, and continuously improve the diagnostic accuracy through feedback optimization to form a closed-loop diagnostic optimization mechanism.

2. The method for fault analysis of distribution network communication equipment according to claim 1, characterized in that, S1 includes: Collect device configuration information to construct a device association topology diagram; Define the association weights between devices, which are dynamically calculated based on the association type, device criticality level, and historical fault data; Establish a device status synchronization mechanism and dynamically adjust the status synchronization cycle according to network load.

3. The method for fault analysis of distribution network communication equipment according to claim 1, characterized in that, S2 includes: When the device detects abnormal parameters, a fault impact propagation model is constructed based on the associated topology. Calculate the probability value of each associated device as the root cause of the fault. The probability calculation takes into account factors such as fault indicators, time sequence and parameter correlation. Verify the fault propagation path and generate a fault impact analysis report.

4. The method for fault analysis of distribution network communication equipment according to claim 1, characterized in that, S3 includes: Establish a multi-dimensional diagnostic decision matrix and dynamically adjust diagnostic thresholds based on the status of associated devices; A weighted voting mechanism was used to synthesize the diagnostic opinions of this equipment and related equipment. The system parameters are optimized based on the diagnostic results to form a closed-loop diagnostic mechanism.

5. The method for fault analysis of distribution network communication equipment according to claim 3, characterized in that, The probability calculation also introduces an uncertainty quantification mechanism: It provides a reliability assessment of the probability calculation results and optimizes the failure propagation path verification strategy based on the uncertainty assessment results.

6. The method for fault analysis of distribution network communication equipment according to claim 2, characterized in that, The state synchronization mechanism includes: Monitor the quality indicators of communication links between devices; The frequency and amount of data for status synchronization are dynamically adjusted based on communication quality. Initiate event-triggered synchronization when an anomaly is detected.

7. A system applied to the fault analysis method for distribution network communication equipment as described in claims 1-6, characterized in that, include: The topology management module is used to build device association topologies and manage the state synchronization between devices; The fault analysis module is used to perform fault propagation analysis and locate the root cause of the fault based on the associated topology. The diagnostic decision module is used to perform dynamic diagnostics and execute collaborative decisions based on the status of associated devices. The feedback optimization module is used to optimize system parameters based on diagnostic results.

8. A fault analysis system for distribution network communication equipment according to claim 7, characterized in that, The topology management module includes: Topology building unit, used to collect device configuration information and build a dynamically updated associated topology graph; The weight calculation unit is used to calculate the dynamic correlation weights that take into account the criticality level of the equipment and historical fault data. The synchronization control unit is used to dynamically adjust the state synchronization strategy according to the network load.

9. A fault analysis system for distribution network communication equipment according to claim 7, characterized in that, The fault analysis module includes: The propagation analysis unit is used to construct a fault impact propagation model and calculate the probability of fault root causes. The verification execution unit is used to verify the fault propagation path through simulation testing. The report generation unit is used to generate an analysis report that includes the root cause of the fault and its propagation path.

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