Communication transmission line fault monitoring method, device, equipment, medium and product

Abstract

The invention relates to the technical field of communication, and provides a communication transmission line fault monitoring method, device and equipment, a medium and a product, and the method comprises the steps: obtaining monitoring data from a plurality of monitoring devices of a communication transmission line in real time; inputting the monitoring data into a first fuzzy infectious disease dynamic model to obtain acquisition parameters output by the first fuzzy infectious disease dynamic model; wherein the first fuzzy infectious disease dynamic model is used for dynamically adjusting acquisition parameters according to a first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used for controlling the operation state and performance characteristics of the monitoring equipment; and performing fault detection according to the monitoring data to obtain a fault detection result. By means of the mode, intelligent upgrading of the communication line fault positioning monitoring system is achieved, the function of self-adaptive detection parameter adjustment is achieved through the first fuzzy infectious disease kinetic model, the system can adapt to the complex and changeable network environment, and therefore the fault detection sensitivity and positioning precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method, device, equipment, medium, and product for monitoring faults in communication transmission lines. Background Art

[0002] With the rapid development of communication technologies, the scale and complexity of communication transmission lines have been continuously increasing. As a key infrastructure for information transmission, the stability and reliability of communication transmission lines are crucial for the normal operation of the entire communication system. However, during operation, communication transmission lines may be affected by various factors, such as the natural environment (lightning strikes, heavy rains, ice and snow, etc.), human factors (construction damage, theft, etc.), and equipment aging, which may lead to line faults. These faults not only affect the normal conduct of communication services but may also cause problems such as data loss and service interruption, bringing great inconvenience and economic losses to users.

[0003] Traditional methods for monitoring faults in communication transmission lines mainly rely on manual inspections and simple threshold detection techniques. Manual inspections require a large amount of manpower, material resources, and time, and it is difficult to detect faults in a timely manner, especially in areas with extensive line distributions and complex terrains. Simple threshold detection techniques have certain limitations. They can only judge faults based on preset fixed thresholds and cannot dynamically adjust detection parameters according to changes in the network environment. Therefore, in a complex and changing network environment, the sensitivity and positioning accuracy of fault detection often cannot meet actual requirements. For example, in the case of large fluctuations in network traffic or different degrees of line aging, fixed thresholds may not be able to accurately distinguish between normal and faulty states, resulting in false alarms or missed alarms. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium, and product for monitoring faults in communication transmission lines to solve the problems in the prior art, such as low detection sensitivity, poor positioning accuracy, and insufficient adaptability, in the face of a complex and changing network environment, and it is difficult to meet the high requirements for fault monitoring in modern communication systems.

[0005] The present invention provides a method for monitoring faults in communication transmission lines, including: obtaining monitoring data in real time from multiple monitoring devices of the communication transmission line; inputting the monitoring data into a first fuzzy infectious disease dynamics model to obtain collection parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the collection parameters according to the first fuzzy logic algorithm and the monitoring data, and the collection parameters are used to control the operating state and performance characteristics of the monitoring devices; performing fault detection based on the monitoring data to obtain a fault detection result.

[0006] A communication transmission line fault monitoring method provided by the present invention includes, after performing fault detection based on monitoring data and obtaining a fault detection result: inputting the fault detection result into a second fuzzy infectious disease dynamics model to obtain a fault response strategy output by the second fuzzy infectious disease dynamics model; wherein, the second fuzzy infectious disease dynamics model is used to evaluate the severity of the fault according to the second fuzzy logic algorithm and the fault detection result, and match and output different fault response strategies according to different severities of the fault.

[0007] A communication transmission line fault monitoring method provided by the present invention, the first fuzzy infectious disease dynamics model is the SEIR model; the SEIR model determines the communication line state of the communication transmission line according to the monitoring data; the communication line state includes a susceptible state, a latent state, a fault state, and a recovery state; the SEIR model matches and outputs different acquisition parameters according to different communication line states; wherein the acquisition parameters include at least one of a detection threshold and a sampling frequency.

[0008] A communication transmission line fault monitoring method provided by the present invention, performing fault detection based on monitoring data to obtain a fault detection result, including: inputting the monitoring data into a fault prediction model to obtain a fault detection result output by the fault prediction model; wherein, the fault prediction model is trained according to historical data and real-time operation data; the fault prediction model optimizes the fault location strategy by introducing a reinforcement learning algorithm, and the fault location strategy can dynamically adjust the search path according to the monitoring data to perform fault location.

[0009] A communication transmission line fault monitoring method provided by the present invention, the second fuzzy infectious disease dynamics model is the MSIR model, and the severity of the fault includes a low-severity fault, a medium-severity fault, and a high-severity fault; when the MSIR model determines that it is a low-severity fault, the fault response strategy is to optimize the network load by triggering resource reallocation and adjust the acquisition parameters to improve the monitoring sensitivity; when the MSIR model determines that it is a medium-severity fault, the fault response strategy is to isolate the faulty line and switch to the standby line, and at the same time start a local repair process to reduce the spread of the fault; when the MSIR model determines that it is a high-severity fault, the fault response strategy is to trigger a global fault isolation mechanism to prevent the spread of the fault and maximize resource allocation to give priority to repairing critical lines.

[0010] A communication transmission line fault monitoring method provided by the present invention further includes: interacting with the user through a visualization chart and an operation interface to obtain an operation instruction or present the fault detection result.

[0011] The present invention also provides a communication transmission line fault monitoring device, including: a data acquisition and aggregation module for obtaining monitoring data in real time from multiple monitoring devices of the communication transmission line; an adaptive acquisition parameter adjustment module for inputting the monitoring data into a first fuzzy infectious disease dynamics model to obtain the acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to the first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices; an intelligent fault prediction and location module for performing fault detection according to the communication line state to obtain a fault detection result.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, it implements the communication transmission line fault monitoring method as described in any one of the above.

[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the communication transmission line fault monitoring method as described in any one of the above.

[0014] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the communication transmission line fault monitoring method as described in any one of the above.

[0015] The communication transmission line fault monitoring method, device, equipment, medium, and product provided by the present invention, the communication transmission line fault monitoring method includes: obtaining monitoring data in real time from multiple monitoring devices of the communication transmission line; inputting the monitoring data into a first fuzzy infectious disease dynamics model to obtain the acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to the first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices; performing fault detection according to the monitoring data to obtain a fault detection result. Through the above method, the real-time data acquisition method of the present invention can ensure that the monitoring system obtains the latest line operating state information. Through the collaborative work of multiple monitoring devices, the key nodes of the communication transmission line can be comprehensively covered, thereby providing a rich data basis for subsequent fault detection and location; in addition, the present invention dynamically adjusts the acquisition parameters through the first fuzzy infectious disease dynamics model, can optimize the operation strategy of the monitoring devices in real time, realizes the intelligent upgrade of the communication line fault location monitoring system, can adapt to complex and changeable network environments, and thus improves the sensitivity and location accuracy of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of the communication transmission line fault monitoring method provided by the embodiment of the present invention.

[0018] Figure 2 It is one of the structural schematic diagrams of the communication transmission line fault monitoring device provided by the embodiment of the present invention.

[0019] Figure 3 It is another structural schematic diagram of the communication transmission line fault monitoring device provided by the embodiment of the present invention.

[0020] Figure 4 It is a schematic diagram of the physical structure of the electronic device provided by the embodiment of the present invention. Specific implementation manners

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0022] In the prior art, there is a lack of a complete monitoring system and method for communication line fault location that is applicable to complex dynamic network environments. Due to the inability of related technologies to adaptively adjust detection parameters, the fault location accuracy and detection sensitivity are limited, thereby affecting the monitoring range and effect. Especially when facing complex and changeable network environments and multi-type hybrid faults, the real-time processing ability is insufficient, lacking the ability to deeply analyze real-time operation data and optimize the fault response strategy. At the same time, the prior art lacks an efficient intelligent prediction and response mechanism, making it difficult to accurately predict potential faults and perform dynamic optimization, resulting in insufficient performance of the monitoring system in terms of fault warning and recovery capabilities and being unable to provide timely and accurate information support in sudden fault events.

[0023] Therefore, it is necessary to deeply integrate dynamic network modeling, real-time data analysis, reinforcement learning, and automated configuration optimization technologies to build an efficient communication fault prediction system. At the same time, establish a unified fault location monitoring platform to realize real-time monitoring of the network status and visual display of the fault handling progress, thereby improving the accuracy of fault location and the efficiency of system recovery.

[0024] Based on this, the present invention provides a method for monitoring faults in a communication transmission line. Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the method for monitoring faults in a communication transmission line provided by an embodiment of the present invention. In this embodiment, the method for monitoring faults in a communication transmission line includes steps S110 to S140, and the specific steps are as follows: S110: Obtain monitoring data in real time from multiple monitoring devices of the communication transmission line.

[0025] S120: Input the monitoring data into the first fuzzy infectious disease dynamics model to obtain the acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to the first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices.

[0026] S130: Perform fault detection based on the monitoring data to obtain a fault detection result.

[0027] In this embodiment, monitoring data is obtained in real time from multiple monitoring devices of the communication transmission line. This real-time data acquisition method can ensure that the monitoring system obtains the latest line operating state information, avoiding the problem of untimely fault detection caused by data delay. Through the collaborative work of multiple monitoring devices, the key nodes of the communication transmission line can be comprehensively covered, thus providing a rich data basis for subsequent fault detection and location.

[0028] Optionally, in the process of data acquisition and aggregation, first, data is acquired in real time from multi-source devices such as communication modules, hardware self-checking devices, and network node sensors through independently deployed data acquisition services, ensuring the high availability and scalability of the system. Then, at each data source, the collected information is subjected to preliminary aggregation and filtering processing to effectively remove redundant information and noise data. To ensure the high efficiency of data flow and the system elasticity, a lightweight message queue is introduced as a bridge for inter-service communication, enabling seamless cooperation among components, enhancing the response speed and stability of the system. In addition, the quality of the acquired data is further optimized through intelligent analysis and preprocessing of the acquired data.

[0029] In addition, this embodiment also introduces the first fuzzy infectious disease dynamics model. The first fuzzy infectious disease dynamics model combines the fuzzy logic algorithm and the infectious disease dynamics algorithm, and can dynamically adjust the acquisition parameters according to the fuzzy logic algorithm and the monitoring data.

[0030] Specifically, the fuzzy logic algorithm is used to handle the uncertainty and ambiguity of monitoring data, especially suitable for complex and variable environments such as communication transmission lines. The relevant threshold detection methods can only judge faults based on fixed values, while fuzzy logic can make more flexible judgments according to the fuzzy characteristics of data (such as the weakening of signal strength, the slight change of bit error rate, etc.), thereby improving the sensitivity of fault detection.

[0031] The infectious disease dynamics algorithm draws on the idea of the infectious disease transmission model and regards the propagation process of faults as a dynamic process similar to "infection". By simulating the propagation path and speed of faults in the communication line, the location where the fault occurs can be more accurately located. This model can capture the dynamic change characteristics of faults, especially in the initial stage of the fault, it can detect abnormalities in time and issue early warnings.

[0032] The acquisition parameters are used to control the operating state and performance characteristics of the monitoring device, such as the monitoring frequency, data sampling rate, alarm threshold, etc. The operating environment of the communication transmission line is complex and variable, such as the fluctuation of network traffic, the different degrees of line aging, etc. In this embodiment, by dynamically adjusting the acquisition parameters, it is possible to adapt to the complex network environment, optimize the operating strategy of the monitoring device in real time according to the current network state and fault characteristics, thereby improving the adaptability and accuracy of fault detection.

[0033] In some embodiments, dynamically adjusting the acquisition parameters can also optimize resource utilization. In the normal operating state, the monitoring frequency can be appropriately reduced to save resources; while when potential fault signs are detected, the monitoring frequency and accuracy can be automatically increased to ensure that faults can be detected and located in time. This adaptive adjustment mechanism can effectively balance the relationship between resource utilization and fault detection effect.

[0034] In this embodiment, fault detection is performed through monitoring data, and a fault detection result is obtained. Optionally, the fault detection result may include information such as the fault type, fault location, and fault occurrence time. This information provides basic data for the generation of subsequent fault response strategies.

[0035] Based on this, this embodiment realizes the intelligent upgrade of the communication transmission line fault monitoring system through key technologies such as real-time monitoring data acquisition, the introduction of the first fuzzy infectious disease dynamics model, dynamic acquisition parameter adjustment, and fault detection and location. This solution can effectively cope with complex and variable network environments, improve the sensitivity of fault detection and the positioning accuracy, and provide strong technical support for the stable operation of communication transmission lines.

[0036] In some embodiments, after obtaining the fault detection result by performing fault detection according to the monitoring data, the steps may further include: Input the fault detection result into the second fuzzy infectious disease dynamics model to obtain the fault response strategy output by the second fuzzy infectious disease dynamics model. Among them, the second fuzzy infectious disease dynamics model is used to evaluate the severity of the fault according to the second fuzzy logic algorithm and the fault detection result, and match and output different fault response strategies according to different fault severities.

[0037] In this embodiment, on the basis of the original fault detection, a second fuzzy infectious disease dynamics model is further introduced to generate a fault response strategy according to the fault detection result. The second fuzzy infectious disease dynamics model also combines the fuzzy logic algorithm and the infectious disease dynamics algorithm.

[0038] It is often difficult to measure the severity of a communication transmission line fault with accurate numerical values. For example, signal attenuation may have different degrees of impact on communication quality in different scenarios. Therefore, the fuzzy logic algorithm can handle this uncertainty by defining fuzzy sets (such as "minor fault", "medium fault", "severe fault") and fuzzy rules to evaluate the severity of the fault.

[0039] In addition, the fuzzy logic algorithm can also dynamically evaluate the severity of the fault according to various factors in the fault detection result (such as bit error rate, signal strength, fault duration, etc.). This dynamic evaluation method can more accurately reflect the impact of the fault on the communication system and avoid unreasonable response strategies caused by misjudgment of a single index.

[0040] Drawing on the idea of the infectious disease transmission model, the second fuzzy infectious disease dynamics model of this embodiment incorporates the fault propagation path and influence range into the evaluation system. By simulating the propagation process of the fault in the communication network, not only the severity of the current fault can be evaluated, but also the possible chain reactions that the fault may further trigger can be predicted, so as to formulate countermeasures in advance.

[0041] According to the evaluated fault severity, the second fuzzy infectious disease dynamics model can match and output different fault response strategies. For example, for a minor fault, it may only be necessary to record the log and issue a simple alarm; for a medium fault, it may be necessary to start a backup line or perform local repair; for a severe fault, it may be necessary to immediately interrupt the service and perform a full repair.

[0042] The second fuzzy infectious disease dynamics model outputs corresponding fault response strategies according to the fault severity. These strategies may include but are not limited to the following: 1) Alarm and notification: Send fault alarm information to the operation and maintenance personnel, including detailed information such as fault type, location, and severity.

[0043] 2) Automatic repair measures: For some faults that can be automatically repaired, the repair program can be automatically started, such as reconfiguring network parameters, switching to standby devices, etc.

[0044] 3) Resource allocation: According to the severity of the fault, reasonably allocate operation and maintenance resources, and give priority to handling serious faults.

[0045] 4) User notification and service degradation: When necessary, send notifications to affected users and take service degradation measures according to the fault situation to reduce user inconvenience.

[0046] In summary, in this embodiment, by introducing the second fuzzy infectious disease dynamics model, the intelligent generation of fault response strategies is realized. The second fuzzy infectious disease dynamics model can dynamically evaluate the severity of faults and match reasonable response strategies according to different fault situations, which not only improves the efficiency and accuracy of fault handling, but also enhances the robustness of the system, optimizes resource utilization, and improves the user experience, providing strong support for the efficient operation and maintenance of communication transmission lines.

[0047] In some embodiments, the communication transmission line fault monitoring method further includes: Interact with the user through visual charts and operation interfaces to obtain operation instructions or present fault detection results.

[0048] In this embodiment, by introducing visual charts and operation interfaces, efficient interaction with users is realized. The visual charts and operation interfaces provide an intuitive and convenient interaction method for users, enabling users to more effectively manage and maintain communication transmission lines.

[0049] Exemplarily, a unified platform that supports multi-terminal access is provided. The platform provides user interfaces with multiple perspectives to meet the needs of different roles, ensuring that all users can monitor the network status and fault handling progress in real time. Integrating real-time data processing functions enables the system to instantly update the network operation situation and any fault information that occurs, and display key performance indicators and alarm information through intuitive visual charts, facilitating users to quickly understand the current situation. Design an interactive operation interface that allows users to deeply explore specific problems through simple click or drag operations, view detailed fault analysis reports and historical data comparisons, so as to better support the decision-making process.

[0050] In some embodiments, the first fuzzy infectious disease dynamics model is the SEIR model; the SEIR model determines the communication line status of the communication transmission line according to the monitoring data; the communication line status includes susceptible state, latent state, fault state and recovery state; the SEIR model matches and outputs different acquisition parameters according to different communication line states; where the acquisition parameters include at least one of a detection threshold and a sampling frequency.

[0051] In this embodiment, the SEIR (Susceptible-Exposed-Infectious-Recovered) model is used as the first fuzzy infectious disease dynamics model.

[0052] In the urban area, multi-source devices such as communication modules, hardware self-checking devices, and network node sensors are arranged, and an edge computing and Internet of Things technology is adopted to establish a unified communication line fault location monitoring system. Considering that this architecture includes efficient connection and real-time data processing among multi-source devices, this embodiment provides a system establishment method for a complex dynamic network environment, introducing an adaptive detection parameter adjustment algorithm based on the fuzzy infectious disease dynamics SEIR, enabling the monitoring system to more efficiently and flexibly adapt to the rapid detection and accurate location requirements of various faults in the communication network, and significantly improving the adaptability and reliability of the entire system. The specific steps are as follows: (1) Model initialization: The operating state of the communication line is analogized to the four states in the SEIR model as follows: Susceptible (susceptible state): It is defined as the normal operation of the communication line, but there is a potential fault risk and routine monitoring is required.

[0053] Exposed (latent state): It is defined as the performance degradation or potential fault of the communication line, but it has not been detected yet, and the monitoring sensitivity needs to be improved.

[0054] Infectious (fault state): It is defined as the communication line has failed, affecting the network performance, and the fault location and response mechanism need to be triggered immediately.

[0055] Recovered (recovered state): It is defined as the fault has been repaired, the communication line has returned to normal operation, and the network configuration needs to be optimized to maintain stability.

[0056] (2) State evaluation and parameter adjustment: Considering the complexity and dynamics of communication line fault detection, in order to improve the accuracy of fault prediction and the adaptive ability of the system, this embodiment adopts a state evaluation and parameter adjustment method based on the fuzzy infectious disease dynamics SEIR model.

[0057] Specifically, the operating data of the communication line are collected in real time, including key indicators such as signal strength, delay, and packet loss rate. The collected data are evaluated using fuzzy logic, and the state of the communication line is classified into Susceptible (susceptible state), Exposed (latent state), Infectious (fault state), and Recovered (recovered state). According to the state classification results, the detection parameters are dynamically adjusted to optimize the system performance.

[0058] (3) Model parameter update: Dynamically update the SEIR model parameters (such as state transition probabilities, detection thresholds, etc.) based on real-time operation data and fault handling results. Combine historical data to optimize the fuzzy logic evaluation rules and improve the accuracy of state classification.

[0059] (4) Fault warning and response: When the line state changes from Susceptible to Exposed, trigger a potential fault warning and notify the operation and maintenance personnel for inspection; when the line state changes from Exposed to Infectious, immediately start the fault location and response mechanism to isolate the fault source and implement repair measures; when the line state changes from Infectious to Recovered, generate a fault report and optimize the network configuration to prevent similar faults from occurring again.

[0060] (5) Visualization and feedback: Real-time display the state changes of the communication line and the adjustment of detection parameters through the user interface, and provide detailed reports on fault warnings, handling progress, and recovery status to support the decision-making and operation of the operation and maintenance personnel.

[0061] It should be added that the state evaluation and parameter adjustment method based on the fuzzy infectious disease dynamics SEIR model mentioned in (2) above has the following specific steps: 1) Describe the state transition of the communication line through the following differential equations: ; ; ; ; Among them, represents the proportion of communication lines in the susceptible state (Susceptible), that is, the lines are operating normally but there are potential fault risks; represents the proportion of communication lines in the latent state (Exposed), that is, the lines have performance degradation or potential faults but have not been detected; represents the proportion of communication lines in the fault state (Infectious), that is, the lines have failed and affect the network performance; represents the proportion of communication lines in the recovered state (Recovered), that is, the faults have been repaired and the lines have returned to normal operation.

[0062] represents the fault propagation rate, that is, the probability that a susceptible line changes to the latent state due to the influence of a faulty line; represents the transition rate from the latent state to the fault state, that is, the probability that a latent line changes to the fault state due to fault deterioration; Denotes the fault repair rate, i.e., the probability that a faulty line is repaired and transitions to the recovered state; Denotes the rate of re - entering the susceptible state after recovery, i.e., the probability that a recovered line re - enters the susceptible state due to environmental changes or external factors.

[0063] This system of differential equations describes the dynamic transition process of communication lines among four states. By solving these equations, the proportional changes of line states can be calculated in real - time, providing a basis for subsequent state evaluation and parameter adjustment. When the fault propagation rate is high, susceptible lines will transition to the latent state faster, thus requiring an increase in detection sensitivity to capture potential faults; when the fault repair rate is high, faulty lines will recover faster, thus requiring optimization of the network configuration to maintain system stability. This modeling method can not only reflect the dynamic changes of communication line states but also provide theoretical support for fault prediction and response strategies.

[0064] 2) Real - time collect the operation data of communication lines, including key indicators such as signal strength, delay, and packet loss rate. Use fuzzy logic to evaluate the collected data and classify the state of communication lines into Susceptible (susceptible state), Exposed (latent state), Infectious (fault state), and Recovered (recovered state).

[0065] Represent the operation data of communication lines as a matrix , and each element in the matrix represents the operation state of the rd line at the th time point. The operation state is determined by multiple indicators (such as signal strength, delay, packet loss rate, etc.). For each line and time point , its state membership degree can be expressed as: ; where represents the operation data of the th line at the th time point. Specifically, the state classification rules are as follows.

[0066] Susceptible state: The signal strength is greater than or equal to the first threshold , the delay is less than or equal to the second threshold , and the packet loss rate is less than or equal to the third threshold . The calculation rule for its membership degree is: .

[0067] Exposed state: The signal strength is greater than or equal to the fourth threshold and less than the first threshold , the latency is greater than the second threshold and less than or equal to the fifth threshold , the packet loss rate is greater than the third threshold and less than or equal to the sixth threshold , and its membership degree calculation rule is: .

[0068] Infectious state: The signal strength is less than the fourth threshold , the latency is greater than the fifth threshold , the packet loss rate is greater than the sixth threshold , and its membership degree calculation rule is: .

[0069] Recovered state: After the line is repaired from the fault state and meets the conditions of the Susceptible state, its membership degree calculation rule is: .

[0070] By iteratively applying these rules to the running data matrix , the status of each line can be evaluated in real time, providing a basis for subsequent dynamic parameter adjustment and fault handling. The specific thresholds need to be determined according to the characteristics of the communication line and the desired monitoring behavior.

[0071] 3) According to the status classification results, dynamically adjust the detection parameters to optimize the system performance. In the Susceptible state, the communication line is running normally but there is a potential fault risk. The system reduces resource consumption by lowering the detection frequency, and at the same time sets a lower detection threshold to ensure the stability of routine monitoring. The detection frequency is dynamically adjusted according to the line status ratio, expressed as: .

[0072] where is the initial detection frequency, respectively represent the ratios of the susceptible, latent, faulty, and recovered states. This adjustment strategy can optimize resource utilization and improve system efficiency while ensuring the monitoring effect.

[0073] In the Exposed state, the performance of the communication line degrades or there are potential faults but have not been detected yet. The system captures potential fault signals by increasing the detection sensitivity and the data sampling frequency. At the same time, the detection threshold According to the latent state ratio Dynamically adjust, expressed as: , where Is the initial threshold, Is the adjustment coefficient.

[0074] This adjustment strategy can effectively identify potential faults, ensure timely early warning before the occurrence of faults, and improve the accuracy and response speed of fault detection.

[0075] In the Infectious state, the communication line has failed and affected network performance. The system immediately triggers the fault location and response mechanism and starts the fault handling process. At the same time, the detection frequency Is adjusted to the maximum value , to ensure the accurate identification and rapid response of the fault scope. This strategy can maximize the detection sensitivity and frequency, help the system quickly locate the fault source and implement repair measures, thereby minimizing the impact of faults on network performance and ensuring the stability and reliability of the communication network.

[0076] In the Recovered state, the fault of the communication line has been repaired and the normal operation has been restored. The system optimizes the network configuration to restore the stable operation state, and gradually reduces the detection frequency to return to the normal monitoring mode. Specifically, the detection frequency Dynamically adjust according to the recovery state ratio , expressed as: ; Where Is the initial detection frequency, Respectively represent the proportions of the susceptible, latent, faulty, and recovered states. This adjustment strategy can gradually reduce resource consumption while ensuring the stability of the system, and achieve efficient fault recovery and network optimization.

[0077] In some embodiments, the steps of performing fault detection based on the monitoring data to obtain the fault detection result may include: Input the monitoring data into the fault prediction model to obtain the fault detection result output by the fault prediction model; wherein, the fault prediction model is trained based on historical data and real-time operation data; the fault prediction model optimizes the fault location strategy by introducing a reinforcement learning algorithm, and the fault location strategy can dynamically adjust the search path according to the monitoring data to perform fault location.

[0078] In this embodiment, the fault prediction model can be used for intelligent fault prediction and location.

[0079] Specifically, first, deep learning technology is used to train a large amount of historical data and real-time operation data to construct a multi-level fault prediction model to accurately predict the probability and type of communication line faults. Then, by introducing a reinforcement learning algorithm to optimize the fault location strategy, which can dynamically adjust the search path according to the network state, thereby improving the recognition speed and accuracy of the fault source. At the same time, combined with the dynamic network topology model, the network structure information is updated in real time to ensure that the fault location process can adapt to network changes and enhance the system's response ability.

[0080] Optionally, this embodiment can also adopt a multi-node collaborative analysis mechanism, enabling each node to share fault information and work collaboratively to further improve the comprehensiveness and accuracy of fault detection.

[0081] In some embodiments, the second fuzzy infectious disease dynamics model is the MSIR model, and the fault severity includes low-severity faults, medium-severity faults, and high-severity faults; when the MSIR model determines that it is in a low-severity fault, the fault response strategy is to optimize the network load by triggering resource reallocation and adjust the acquisition parameters to improve the monitoring sensitivity; when the MSIR model determines that it is in a medium-severity fault, the fault response strategy is to isolate the faulty line and switch to the standby line, and at the same time start a local repair process to reduce fault spread; when the MSIR model determines that it is in a high-severity fault, the fault response strategy is to trigger a global fault isolation mechanism to prevent fault spread and maximize resource allocation to prioritize the repair of critical lines.

[0082] This embodiment can deploy multi-source devices such as communication modules, hardware self-checking devices, and network node sensors within the urban area and establish a unified communication line fault location monitoring system. Considering that this architecture includes efficient connection and real-time data processing between multi-source devices, this embodiment introduces a fault response algorithm based on the fuzzy infectious disease dynamics MSIR (Maternally-Susceptible-Infectious-Recovered) model and designs an automated fault response module. This module can automatically trigger a series of response measures according to the fault location result, such as line switching, resource reallocation, and fault isolation, to ensure the timeliness and effectiveness of fault handling.

[0083] The MSIR model classifies the state of the communication line into four categories as follows: Maternally Protected (protected state): Defined as the line being protected and temporarily not affected by faults.

[0084] Susceptible (susceptible state): Defined as the line operating normally but with potential fault risks.

[0085] Infectious (infection status): Defined as a line failure that affects network performance.

[0086] Recovered (recovery status): Defined as the fault has been repaired and the line has returned to normal operation.

[0087] Considering the complexity and dynamics of communication line fault location prediction, in order to improve the accuracy of fault location prediction and the adaptive ability of the system, this embodiment proposes a state evaluation and fault response method based on the fuzzy infectious disease dynamics MSIR model. The specific steps are as follows: 1) Model the operating state of the communication line as the initial state of an infectious disease.

[0088] According to the rules and parameters of the MSIR model, simulate the dynamic transfer process of the state over time to generate the prediction results of state changes. Represent the state of the communication line as a matrix , matrix Each element in represents the state of the th line at the th time point. The MSIR model is defined as: ; ; ; ; Among them, represents the transition rate from the protected state to the susceptible state; represents the transition rate from the susceptible state to the protected state; represents the fault propagation rate; represents the fault repair rate; represents the rate of re-entering the susceptible state after recovery.

[0089] 2) Calculate the probability of the fault state.

[0090] The transition from the susceptible state to the fault state is determined by the fault propagation rate , and can be calculated through , where represents the probability of transitioning from the susceptible state to the fault state.

[0091] The transition from the fault state to the recovery state is determined by the fault repair rate , and can be calculated through . By iteratively applying these equations to the state matrix of the communication line , the dynamic transfer process of the simulation state over time can generate prediction results for the change in the communication line state. The specific transfer parameters need to be determined according to the characteristics of the communication line and the desired simulation behavior.

[0092] 3) Evaluate the severity of the fault through fuzzy logic.

[0093] Use fuzzy logic to evaluate the severity of the fault, classify it into three levels: low, medium, and high, and define the corresponding membership degrees. The state transition equation is the MSIR model in 1).

[0094] 4) Defuzzification.

[0095] Convert the fuzzy output into a specific severity level (low, medium, high) through the weighted average method or other defuzzification methods. The weighted average formula is: , where is the weight, is the membership degree in step 3).

[0096] 5) Automatic response.

[0097] According to the output result of 4), in the case of low-severity faults, the system optimizes the network load by triggering resource reallocation and adjusts the detection parameters to improve the monitoring sensitivity; in the case of medium-severity faults, the system isolates the faulty line and switches to the standby line, and at the same time starts a local repair process to reduce the spread of the fault; in the case of high-severity faults, the system triggers a global fault isolation mechanism to prevent the spread of the fault and maximizes resource allocation to prioritize the repair of critical lines.

[0098] Above, the embodiment of the present invention establishes a communication transmission line fault location and monitoring system based on a distributed microservice architecture, which can effectively improve the fault warning and recovery capabilities of the communication network. The adaptive algorithm based on the fuzzy infectious disease dynamics SEIR model can dynamically adjust the detection parameters to adapt to the complex and changeable network environment, significantly improving the sensitivity and location accuracy of fault detection. The MSIR model fault response algorithm based on fuzzy infectious disease dynamics can automatically trigger fault handling measures and, combined with the hybrid mode of manual intervention, further improve the reliability and adaptability of the system. At the same time, based on the design of the distributed microservice architecture, the efficient collaboration of data collection, processing, and analysis is realized, ensuring that the system can provide timely and accurate information support when dealing with sudden faults, and significantly improving the stability and operation and maintenance efficiency of the communication network.

[0099] Therefore, by constructing a communication transmission line fault location monitoring system based on a distributed microservices architecture and combining an adaptive algorithm based on the fuzzy infectious disease dynamics SEIR model and a fault response algorithm of the MSIR model, efficient and intelligent fault detection and location are achieved, providing strong support for the stable operation and rapid recovery of communication networks. This solution can significantly reduce operation and maintenance costs and improve network reliability, and is applicable to multiple fields such as telecommunications, transportation, and energy.

[0100] The present invention also provides a communication transmission line fault monitoring device. The communication transmission line fault monitoring device provided by the present invention will be described below. The communication transmission line fault monitoring device described below can be correspondingly referred to the communication transmission line fault monitoring method described above.

[0101] Please refer to Figure 2 , Figure 2 which is one of the schematic structural diagrams of the communication transmission line fault monitoring device provided by the embodiment of the present invention. In this embodiment, the communication transmission line fault monitoring device includes a data acquisition and aggregation module 210, an adaptive acquisition parameter adjustment module 220, and an intelligent fault prediction and location module 230.

[0102] The data acquisition and aggregation module 210 is used to obtain monitoring data in real time from multiple monitoring devices of the communication transmission line; The adaptive acquisition parameter adjustment module 220 is used to input the monitoring data into the first fuzzy infectious disease dynamics model to obtain the acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to the first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices; The intelligent fault prediction and location module 230 is used to perform fault detection according to the communication line state to obtain a fault detection result.

[0103] In some embodiments, the communication transmission line fault monitoring device may further include an automated fault response module. The automated fault response module can be used for: Inputting the fault detection result into the second fuzzy infectious disease dynamics model to obtain a fault response strategy output by the second fuzzy infectious disease dynamics model; wherein, the second fuzzy infectious disease dynamics model is used to evaluate the severity of the fault according to the second fuzzy logic algorithm and the fault detection result, and match and output different fault response strategies according to different fault severities.

[0104] In some embodiments, the first fuzzy infectious disease dynamics model is the SEIR model; the SEIR model determines the communication line status of the communication transmission line according to the monitoring data; the communication line status includes a susceptible state, a latent state, a fault state, and a recovery state; the SEIR model matches and outputs different acquisition parameters according to different communication line states; wherein the acquisition parameters include at least one of a detection threshold and a sampling frequency.

[0105] In some embodiments, the intelligent fault prediction and location module 230 can specifically be used for: Input the monitoring data into the fault prediction model to obtain the fault detection result output by the fault prediction model; wherein, the fault prediction model is trained according to historical data and real-time operation data; the fault prediction model optimizes the fault location strategy by introducing a reinforcement learning algorithm, and the fault location strategy can dynamically adjust the search path according to the monitoring data to perform fault location.

[0106] In some embodiments, the second fuzzy infectious disease dynamics model is the MSIR model, and the fault severity includes low-severity faults, medium-severity faults, and high-severity faults; when the MSIR model determines that a low-severity fault occurs, the fault response strategy is to optimize the network load by triggering resource reallocation and adjust the acquisition parameters to improve the monitoring sensitivity; when the MSIR model determines that a medium-severity fault occurs, the fault response strategy is to isolate the faulty line and switch to the standby line, and at the same time start a local repair process to reduce the spread of the fault; when the MSIR model determines that a high-severity fault occurs, the fault response strategy is to trigger a global fault isolation mechanism to prevent the spread of the fault and maximize resource allocation to preferentially repair critical lines.

[0107] In some embodiments, the communication transmission line fault monitoring device may further include a user interaction and visualization module. The user interaction and visualization module can be used for: Interact with the user through a visualization chart and an operation interface to obtain operation instructions or present the fault detection result.

[0108] Please refer to Figure 3 , Figure 3 which is the second structural schematic diagram of the communication transmission line fault monitoring device provided by the embodiments of the present invention. In this embodiment, the communication transmission line fault monitoring device includes five main parts: a data acquisition and aggregation module 310, an adaptive acquisition parameter adjustment module 320, an intelligent fault prediction and location module 330, an automated fault response module 340, and a user interaction and visualization module 350, and the five modules are deployed through Docker container technology.

[0109] The application scenario of this embodiment is to build a unified communication line fault location monitoring system. The system realizes real-time data collection, analysis, and network transmission by connecting independent monitoring devices such as communication modules, hardware self-checking devices, and network node sensors. The system adopts an adaptive detection parameter adjustment algorithm based on the fuzzy infectious disease dynamics SEIR model to dynamically analyze the operating state of the communication line and adjust the detection parameters; at the same time, it uses a fault response algorithm based on the MSIR model to achieve automated fault response.

[0110] Specifically, the data collection and aggregation module 310 is designed with a microservices architecture. It collects data in real time from multiple sources such as communication modules, hardware self-checking devices, and network node sensors through independently deployed data collection services, ensuring the high availability and scalability of the module. Based on edge computing technology, each data collection service performs preliminary aggregation and filtering at the data source, removing redundant information and noise data, significantly reducing data transmission pressure and improving processing efficiency. In addition, the module uses lightweight message queues (such as Kafka, RabbitMQ) to achieve communication between services, ensuring the efficient flow of data and the overall elastic expansion ability of the system.

[0111] The adaptive acquisition parameter adjustment module 320 integrates an adaptive algorithm based on the fuzzy infectious disease dynamics SEIR model. It analogizes the operating state of the communication line to the four types of states in the SEIR model and dynamically evaluates the network health. Through fuzzy logic, it quantitatively analyzes the potential fault risks of the communication line and adjusts the detection parameters (such as detection thresholds, sampling frequencies, etc.) in real time to adapt to the complex and changeable network environment. This algorithm can automatically optimize the detection strategy according to the changes in network load and fault risks, ensuring that the system can still operate efficiently under high load or abnormal conditions. At the same time, by combining historical data and real-time operation data, it continuously updates the model parameters to improve the adaptive ability of the algorithm and the accuracy of fault detection.

[0112] The intelligent fault prediction and location module 330 combines deep learning and reinforcement learning technologies to build a multi-level fault prediction model. By analyzing historical data and real-time operation data, it accurately predicts the probability and type of communication line faults. This module uses reinforcement learning to optimize the fault location strategy, combines the dynamic network topology model and multi-node collaborative analysis to achieve the rapid location and accurate identification of the fault source.

[0113] The automated fault response module 340 integrates a fault response algorithm based on the fuzzy infectious disease dynamics MSIR model, which can automatically trigger a series of response measures according to the fault location results, such as line switching, resource reallocation, and fault isolation, to ensure the timeliness and effectiveness of fault handling. The system uses the fuzzy MSIR model to formulate a hybrid mode of manual intervention, allowing operation and maintenance personnel to make manual adjustments in complex or uncertain scenarios, realizing the flexible combination of automated decision-making based on the MSIR model and manual experience, and further improving the reliability and adaptability of the system.

[0114] The user interaction and visualization module 350 provides a multi-terminal and multi-perspective user interface, supporting real-time network status monitoring, fault handling progress tracking, and data analysis report generation, helping users comprehensively understand the system operation. Through intuitive visualization charts and an interactive operation interface, users can quickly locate faults and view the handling progress.

[0115] On the other hand, an embodiment of the present invention further provides an electronic device. Please refer to Figure 4 , Figure 4 which is a schematic diagram of the physical structure of the electronic device provided by the embodiment of the present invention. As Figure 4 shown, the electronic device may include a memory 420, a processor 410, and a computer program stored on the memory 420 and executable on the processor 410. When the processor 410 executes the program, it can implement a communication transmission line fault monitoring method, which may include: Obtaining monitoring data in real time from multiple monitoring devices of the communication transmission line; inputting the monitoring data into a first fuzzy infectious disease dynamics model to obtain acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to the first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices; performing fault detection based on the monitoring data to obtain a fault detection result.

[0116] Optionally, the electronic device may further include a communication bus 430 and a communication interface 440. Among them, the processor 410, the communication interface 440, and the memory 420 complete mutual communication through the communication bus 430. The processor 410 can call the computer program in the memory 420 to execute the communication transmission line fault monitoring method provided by the above various methods.

[0117] In addition, when the logical instructions in the above-mentioned memory 420 can be implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0118] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the communication transmission line fault monitoring method provided by the above-mentioned various methods. The steps and principles have been introduced in detail in the above methods and will not be repeated here.

[0119] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the communication transmission line fault monitoring method provided by the above-mentioned various methods. The steps and principles have been introduced in detail in the above methods and will not be repeated here.

[0120] The non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROM, EPROM, EEPROM, non-volatile memories (NANDFLASH), solid-state drives (SSD)).

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0122] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A communication transmission line fault monitoring method, characterized in that, Including: Obtaining monitoring data in real time from multiple monitoring devices on a communication transmission line; Inputting the monitoring data into a first fuzzy infectious disease dynamics model to obtain acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to a first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring devices; Performing fault detection based on the monitoring data to obtain a fault detection result.

2. The communication transmission line fault monitoring method according to claim 1, wherein After performing fault detection based on the monitoring data to obtain a fault detection result, it further includes: Inputting the fault detection result into a second fuzzy infectious disease dynamics model to obtain a fault response strategy output by the second fuzzy infectious disease dynamics model; Wherein, the second fuzzy infectious disease dynamics model is used to evaluate the severity of a fault according to a second fuzzy logic algorithm and the fault detection result, and match and output different fault response strategies according to different severities of the fault.

3. The communication transmission line fault monitoring method according to claim 1, wherein The first fuzzy infectious disease dynamics model is an SEIR model; The SEIR model determines the communication line state of the communication transmission line according to the monitoring data; the communication line state includes a susceptible state, a latent state, a fault state, and a recovery state; The SEIR model matches and outputs different acquisition parameters according to different communication line states; wherein the acquisition parameters include at least one of a detection threshold and a sampling frequency.

4. The communication transmission line fault monitoring method according to claim 1, wherein Performing fault detection based on the monitoring data to obtain a fault detection result includes: Inputting the monitoring data into a fault prediction model to obtain a fault detection result output by the fault prediction model; Wherein, the fault prediction model is trained according to historical data and real-time operation data; the fault prediction model optimizes the fault location strategy by introducing a reinforcement learning algorithm, and the fault location strategy can dynamically adjust the search path according to the monitoring data to perform fault location.

5. The communication transmission line fault monitoring method according to claim 2, characterized in that, The second fuzzy infectious disease dynamics model is an MSIR model, and the severity of the fault includes a low-severity fault, a medium-severity fault, and a high-severity fault; When the MSIR model determines that it is a low-severity fault, the fault response strategy is to optimize the network load by triggering resource reallocation and adjust the acquisition parameters to improve monitoring sensitivity; When the MSIR model determines that it is a medium-severity fault, the fault response strategy is to isolate the faulty line and switch to a standby line, and at the same time start a local repair process to reduce fault spread; When the MSIR model determines that it is a high-severity fault, the fault response strategy is to trigger a global fault isolation mechanism to prevent fault spread and maximize resource allocation to preferentially repair critical lines.

6. The communication transmission line fault monitoring method according to any one of claims 1 to 5, characterized in that It further includes: Interacting with the user through a visualization chart and an operation interface to obtain an operation instruction or present the fault detection result.

7. A communication transmission line fault monitoring device, characterized in that, Including: A data collection and aggregation module for obtaining monitoring data in real time from multiple monitoring devices on a communication transmission line; An adaptive acquisition parameter adjustment module, configured to input the monitoring data into a first fuzzy infectious disease dynamics model to obtain acquisition parameters output by the first fuzzy infectious disease dynamics model; wherein, the first fuzzy infectious disease dynamics model is used to dynamically adjust the acquisition parameters according to a first fuzzy logic algorithm and the monitoring data, and the acquisition parameters are used to control the operating state and performance characteristics of the monitoring device. An intelligent fault prediction and location module, configured to perform fault detection according to the communication line state to obtain a fault detection result.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the communication transmission line fault monitoring method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the communication transmission line fault monitoring method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the communication transmission line fault monitoring method according to any one of claims 1 to 6.

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