Optical fiber channel service maintenance method, system and device and electronic equipment

Abstract

The invention relates to the technical field of optical fiber communication maintenance, in particular to an optical fiber channel service maintenance method, system and device and electronic equipment, an initial risk score is constructed by obtaining historical maintenance data, meanwhile, a real-time vibration signal is collected to extract characteristic parameters and recognize the biological erosion probability, and the initial risk score and the biological erosion probability are dynamically fused through a space-time weighting algorithm. And calculating a comprehensive risk score and generating a final risk level, and further outputting a dynamic maintenance priority list containing high-risk channel positioning, service priorities and maintenance strategies. The method solves the problems of incomplete assessment and unreasonable resource allocation caused by dependence on historical data or a single risk factor in a traditional method, achieves comprehensive and accurate assessment of historical and biological erosion risks, can recognize high-risk segments in advance, adopts protective measures, reduces the fiber breaking rate, improves the stability of high-priority services, optimizes resource scheduling, and improves the resource utilization rate. And the operation and maintenance intelligence level is enhanced.

Description

Technical Field

[0001] This invention relates to the field of optical fiber communication technology, specifically to a method, system, device, and electronic equipment for maintaining optical fiber channel services. Background Technology

[0002] As a crucial foundation for modern information transmission, optical fiber communication plays a key role in optical fiber broadband operation services, the construction of next-generation mobile communication core networks and access networks, mobile telecommunications services such as networking, as well as other telecommunications services such as mobile voice services and mobile data communication services. It is widely used in scenarios such as multi-operator optical fiber co-location in densely populated urban areas, concealed wiring in old buildings, and bare fiber transmission in rural areas, carrying various important services such as government and enterprise dedicated lines, home broadband, and 5G fronthaul.

[0003] Existing fiber optic channel service maintenance methods mainly rely on constructing breakpoint heatmaps based on historical fiber breakage data to determine maintenance priorities, or on biological erosion early warning alone to prevent fiber risks. However, the former only focuses on historical fault situations and cannot effectively predict the risk of fiber breakage caused by new factors such as biological erosion in the future. The latter, although it can monitor biological damage behavior, lacks integration with historical fault data and cannot comprehensively assess the overall risk of fiber optics. This leads to unreasonable allocation of maintenance resources, inability to accurately locate high-risk fiber segments, difficulty in taking effective preventive maintenance measures in advance, and easy interruption of high-priority services, affecting the quality of communication services. Summary of the Invention

[0004] This application provides a method, system, device, and electronic device for maintaining fiber channel services. This solution solves the problems of existing technologies that rely solely on historical fiber breakage data to assess risks, which makes it impossible to predict new risks such as biological erosion, and the problems of unreasonable allocation of maintenance resources and difficulty in accurately locating high-risk fiber segments and preventing fiber breaks in advance due to the lack of integration of historical and real-time risk factors.

[0005] To achieve the above objectives, the embodiments of this application disclose the following technical solutions:

[0006] Firstly, this solution discloses a method for maintaining fiber channel services, including the following steps:

[0007] Step S1: Obtain historical maintenance data for the fiber channel. The historical maintenance data includes historical fiber breakage event records, service priority data, and geographic information data. The historical fiber breakage event records include the breakage time, breakage location, and breakage cause. The service priority data includes the service types carried by different fiber channels and their corresponding quality of service levels. The geographic information data includes the routing distribution and co-channel situation of the fiber channel. Simultaneously, obtain real-time monitoring data for the fiber channel. The real-time monitoring data includes vibration signals collected by vibration sensors deployed at key locations in the fiber channel. These vibration signals contain periodic high-frequency vibration characteristics caused by biological erosion behavior.

[0008] Step S2: Construct an initial risk score for the fiber optic channel based on the historical maintenance data. Specifically, a spatial clustering algorithm is used to analyze the geographical distribution of historical fiber breakage events, and risk weights are calculated for fiber optic channels in different regions in combination with service priority data to generate an initial risk score. At the same time, feature parameters are extracted based on the real-time monitoring data to identify the probability of biological erosion. The feature parameters are obtained by preprocessing the vibration signal, including filtering and noise reduction and time-frequency analysis, to extract the vibration frequency, amplitude, and periodic pattern. Then, the feature parameters are input into a pre-trained biological erosion behavior recognition model to obtain the probability of biological erosion.

[0009] Step S3: Integrate the initial risk score and the probability of biological erosion, and dynamically adjust the initial risk score and the probability of biological erosion using a spatiotemporal weighted algorithm to calculate the comprehensive risk score of the fiber optic channel and generate the final risk level;

[0010] Step S4: Based on the comprehensive risk level, generate a dynamic maintenance priority list for the fiber optic channels. The dynamic maintenance priority list includes the location information, service priority, and recommended maintenance strategies for high-risk fiber optic channels. The recommended maintenance strategies include preventative protection measures and emergency maintenance scheduling plans.

[0011] Secondly, this solution discloses a fiber optic channel service maintenance system, characterized in that it includes:

[0012] The historical data acquisition module is used to acquire historical maintenance data of the fiber channel;

[0013] The real-time monitoring module is used to acquire real-time monitoring data of the fiber optic channel.

[0014] The initial risk calculation module is used to construct an initial risk score based on the historical maintenance data;

[0015] The biological erosion analysis module is used to extract feature parameters and identify the probability of biological erosion based on the real-time monitoring data;

[0016] The comprehensive risk calculation module is used to calculate a comprehensive risk score by integrating the initial risk score and the probability of biological erosion.

[0017] The maintenance priority generation module is used to generate a dynamic maintenance priority list based on the comprehensive risk score.

[0018] Thirdly, this solution discloses a fiber channel service maintenance device, characterized in that it includes at least one processor and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the fiber channel service maintenance method described in the first aspect.

[0019] Fourthly, this solution discloses an electronic device, including the aforementioned fiber channel service maintenance device.

[0020] This invention relates to a method, system, device, and electronic equipment for maintaining fiber optic channel services. It constructs an initial risk score by integrating historical fiber breakage event records, service priority data, and geographic information data. It then extracts bio-erosion characteristic parameters from real-time monitored vibration signals and inputs them into a pre-trained model to identify the probability of bio-erosion. Finally, it dynamically fuses the initial risk score and the bio-erosion probability using a spatiotemporal weighted algorithm to calculate a comprehensive risk score. This generates a dynamic maintenance priority list that includes the location information of high-risk fiber optic channels, service priorities, preventative protection measures, and emergency maintenance scheduling plans. This effectively solves the problems of incomplete risk assessment and unreasonable allocation of maintenance resources caused by traditional maintenance methods that rely solely on historical data or single risk factors. It achieves a comprehensive and accurate assessment of historical risks and potential future bio-erosion risks in fiber optic channels, enabling early identification of high-risk fiber segments and targeted protective measures. This significantly reduces the incidence of fiber breakage, improves the communication stability of high-priority services, optimizes the precise scheduling of maintenance resources, reduces unnecessary maintenance costs, and enhances the intelligence level and overall reliability of fiber optic channel operation and maintenance. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of step s1 in Embodiment 1 of the present invention;

[0022] Figure 3 This is a flowchart of step s2 in Embodiment 1 of the present invention;

[0023] Figure 4 This is a flowchart of step s3 in Embodiment 1 of the present invention;

[0024] Figure 5 This is a flowchart of step s4 in Embodiment 1 of the present invention;

[0025] Figure 6 This is an interaction diagram of the system in Embodiment 2 of the present invention. Detailed Implementation

[0026] Specific embodiments of the invention will now be described in detail. Although the invention is described in conjunction with these specific embodiments, it should be understood that the invention is not intended to be limited to these specific embodiments. Rather, these embodiments are intended to cover alternative, modified, or equivalent embodiments that may be included within the spirit and scope of the invention as defined by the claims. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. The invention may be practiced without some or all of these specific details. In other instances, well-known processes have not been described in detail so as not to unnecessarily obscure the invention.

[0027] When used in conjunction with the terms "comprising," "method comprising," or similar language in this specification and appended claims, the singular forms "a," "some," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0028] Application Overview: In existing technologies, fiber channel service maintenance primarily relies on historical fiber breakage data to construct heatmaps for risk assessment, or on monitoring biological erosion behavior separately, without effectively integrating the two. For example, relying solely on historical data cannot predict future risks such as biological erosion, while separate biological monitoring lacks historical fault correlation analysis. If these problems are not addressed, it ultimately leads to unreasonable allocation of maintenance resources, missed detection of high-risk fiber segments, and increased risk of service interruption.

[0029] To address the aforementioned challenges, this application first considers the multi-source nature of fiber optic channel risks. It attempts to integrate historical maintenance data with real-time biological monitoring data to comprehensively assess both historical and future biological erosion risks, thereby achieving precise risk grading and dynamic maintenance scheduling. The specific design involves first acquiring historical fiber breakage events, service priorities, and geographic information data to construct an initial risk score. Simultaneously, real-time vibration signals are collected using vibration sensors, and biological erosion characteristic parameters are extracted and input into a pre-trained model to obtain the probability of biological erosion. Then, a spatiotemporal weighted algorithm dynamically fuses the two types of risks to generate a comprehensive risk score. Finally, a dynamic priority list containing location information, service priorities, and preventative and emergency maintenance strategies is output. This solves the technical problems of traditional methods, such as incomplete risk prediction, delayed maintenance, and resource waste, significantly improving the intelligence level of fiber optic channel operation and maintenance and the ability to ensure business continuity.

[0030] Example 1

[0031] A method for maintaining fiber channel services includes the following steps:

[0032] Step S1: Obtain historical maintenance data for the fiber channel. The historical maintenance data includes historical fiber breakage event records, service priority data, and geographic information data. The historical fiber breakage event records include the breakage time, breakage location, and breakage cause. The service priority data includes the service types carried by different fiber channels and their corresponding quality of service levels. The geographic information data includes the routing distribution and co-channel situation of the fiber channel. Simultaneously, obtain real-time monitoring data for the fiber channel. The real-time monitoring data includes vibration signals collected by vibration sensors deployed at key locations in the fiber channel. These vibration signals contain periodic high-frequency vibration characteristics caused by biological erosion behavior.

[0033] In this embodiment, historical maintenance data is acquired through the historical database of the fiber optic network operation and maintenance management system. This database stores detailed records of all past fiber breakage events, including the specific time of each breakage, the location of the breakage in latitude and longitude, and the cause of the breakage, such as construction damage or natural aging. Service priority data is extracted from the service management platform to clarify the service types carried by different fiber optic channels. For example, the service quality level of enterprise dedicated lines is higher than that of ordinary home broadband. Geographic information data is obtained through integration with a geographic information system, clearly showing the routing of the fiber optic channel and the co-location of cables with other operators' fibers. Real-time monitoring data is acquired by deploying vibration sensors in areas along the fiber optic channel prone to biological erosion, such as densely vegetated areas and areas where rodents are active. These sensors can capture vibration signals generated by biological gnawing, climbing, and other behaviors. The periodic high-frequency vibration characteristics unique to biological erosion can be distinguished from vibrations caused by natural factors such as wind and rain.

[0034] Step S2: Construct an initial risk score for the fiber optic channel based on the historical maintenance data. Specifically, a spatial clustering algorithm is used to analyze the geographical distribution of historical fiber breakage events, and risk weights are calculated for fiber optic channels in different regions in combination with service priority data to generate an initial risk score. At the same time, feature parameters are extracted based on the real-time monitoring data to identify the probability of biological erosion. The feature parameters are obtained by preprocessing the vibration signal, including filtering and noise reduction and time-frequency analysis, to extract the vibration frequency, amplitude, and periodic pattern. Then, the feature parameters are input into a pre-trained biological erosion behavior recognition model to obtain the probability of biological erosion.

[0035] In this embodiment, the initial risk score is constructed by first processing historical fiber breakage event records using a spatial clustering algorithm, grouping geographically similar fiber breakage events into one category to identify high-incidence areas. Combined with service priority data, higher risk weights are assigned to fiber channel areas containing high-priority services, thereby calculating the initial risk score for each fiber channel location. For identifying the probability of bio-erosion, the vibration signals collected by vibration sensors are first filtered and denoised to remove low-frequency interference signals from the environment. Then, time-frequency analysis is used to extract characteristic parameters such as vibration frequency, amplitude, and periodicity. These characteristic parameters are input into a pre-trained bio-erosion behavior recognition model, which can determine whether the current vibration is caused by bio-erosion behavior based on the characteristic parameters and output the corresponding probability value.

[0036] Step S3: Integrate the initial risk score and the probability of biological erosion, and dynamically adjust the initial risk score and the probability of biological erosion using a spatiotemporal weighted algorithm to calculate the comprehensive risk score of the fiber optic channel and generate the final risk level;

[0037] In this embodiment, when integrating the initial risk score and the probability of biological erosion, the spatiotemporal weighted algorithm dynamically adjusts based on the geographical characteristics of the fiber optic channel and different time periods. For example, during seasons with frequent biological activity, the weight of the probability of biological erosion is increased; in areas with a history of high fiber breakage, the weight of the initial risk score is increased. Through such dynamic adjustments, the comprehensive risk score of each fiber optic channel location at different times is calculated, and the risk level is divided into high, medium, and low levels based on the score.

[0038] Step S4: Based on the comprehensive risk level, generate a dynamic maintenance priority list for the fiber optic channel. The dynamic maintenance priority list includes the location information, service priority, and recommended maintenance strategy for high-risk fiber optic channels. The recommended maintenance strategy includes preventive protection measures and emergency maintenance scheduling schemes.

[0039] In this embodiment, a dynamic maintenance priority list is generated based on the overall risk level. For high-risk fiber optic channels, the list will clearly indicate their specific location information, such as the specific road segment or tower number, and also indicate the priority of the services carried by the channel. Recommended maintenance strategies may include preventative protective measures such as installing rodent-proof netting and applying bird repellent; the emergency maintenance scheduling plan specifies the response time for maintenance personnel, the equipment they need to carry, and the fault handling procedures in the event of a fiber breakage.

[0040] This solution further proposes that, in step S2, the initial risk score is calculated using the following formula:

[0041] ;in, Indicates the first Initial risk score for each fiber optic channel location The number of historical fiber optic cable breakages in the vicinity of this location. For the first The risk weights for each historical fiber breakage event are determined based on business priority data and geographic information data. For the first The spatial correlation between a historical fiber breakage event and the location of the fiber channel is calculated using a spatial clustering algorithm.

[0042] In the embodiment, during the application of the initial risk score calculation formula, the first step is to determine the... Then, the number of historical fiber breakage events occurring within a certain radius of each fiber channel location is counted. For each historical fiber breakage event Risk weights are determined based on their corresponding business priorities and geographical information. The higher the business priority, the greater the corresponding risk weight; if the location of a historical fiber breakage event is related to the first... If the geographical environments of the fiber optic channels are similar, such as being located in areas with frequent construction activity, their risk weights will increase accordingly. Spatial correlation. The calculation is performed using a spatial clustering algorithm, which combines historical fiber breakage events with the first... The algorithm inputs the geographic coordinates of each fiber optic channel location into the algorithm, which then calculates the spatial correlation based on factors such as distance and geographic feature similarity. Finally, it processes each... and Multiply them, then add all the products together to get the first product. Initial risk score for each fiber optic channel location ;

[0043] This solution further proposes that, in step S3, the comprehensive risk score is calculated using the following formula:

[0044] ;in, Indicates the first The location of each fiber optic channel in time Comprehensive risk score, Assign an initial risk score to this location. For this location in time The probability of biological erosion, and The spatiotemporal weighting coefficients for the initial risk score and the probability of bioerosion, respectively, are determined by the following formula: ;

[0045] ;in and For adjustment coefficients, Using historical and biological risks as the time reference point, this formula achieves real-time and accurate assessment of the overall risk of fiber optic channels by dynamically adjusting the weights of historical and biological risks.

[0046] When applying the comprehensive risk score calculation formula, for the first... The location of each fiber optic channel in time Comprehensive risk score An initial risk score for the location needs to be obtained first. and in time Biological erosion probability Spatiotemporal weighting coefficients and The determination is based on the time reference point and adjustment coefficient and Time reference point It could be a specific time of day or a particular season of the year; for example, setting the start time of a season with high biological activity as... Adjustment coefficient and Based on historical data and practical experience, this setting is used for adjustment. and The rate of change over time. As time moves away... hour, It may increase. The risk may decrease, meaning that more emphasis is placed on the initial risk score; when the time is close to or during a period of frequent biological activity, It may increase. The risk may be reduced, with greater emphasis placed on the probability of biological erosion. Through such dynamic adjustments, a real-time and accurate assessment of the overall risk of fiber optic channels can be achieved.

[0047] This scheme further proposes that, in step S2, the pre-trained biological erosion behavior recognition model is trained using historical vibration signal samples to distinguish the vibration characteristics of biological erosion behavior from those of non-biological erosion behavior.

[0048] The training process of the pre-trained bio-erosion behavior recognition model is as follows: First, a large number of historical vibration signal samples are collected. These samples include vibration signals generated by bio-erosion behaviors, such as vibrations from rodents gnawing on optical fibers and vibrations from birds moving on optical fiber towers, as well as vibration signals generated by non-bio-erosion behaviors, such as vibrations from wind blowing on optical fibers and ground-transmitted vibrations from passing vehicles. These samples are preprocessed to extract feature parameters such as vibration frequency, amplitude, and periodicity patterns. Then, the samples are divided into training and testing sets. The model is trained using the training set, and by continuously adjusting the model parameters, the model can accurately distinguish the vibration characteristics of bio-erosion behaviors from non-bio-erosion behaviors. After training, the model is validated using the testing set to ensure that the model's recognition accuracy meets the requirements of practical applications. In practical applications, the real-time extracted vibration signal feature parameters are input into the model, and the model can then output the corresponding bio-erosion probability.

[0049] This solution further proposes that, in step S3, the spatiotemporal weighted algorithm dynamically adjusts the weight ratio of the initial risk score and the probability of biological erosion based on the geographical region and time period of the optical fiber channel.

[0050] In this embodiment, the spatiotemporal weighted algorithm dynamically adjusts the weight ratio of the initial risk score and the probability of biological erosion, taking into full account the geographical region and time period factors of the fiber optic channel. Geographically, in densely populated urban areas, due to high population density, frequent construction, and a higher historical fiber breakage rate, the initial risk score receives a relatively higher weight. In rural areas, where biological activity is more frequent, the probability of biological erosion receives a correspondingly higher weight. Time-wise, in spring and autumn, biological activity is more active, increasing the weight of the probability of biological erosion. In summer and winter, with more extreme weather events and a higher historical risk of fiber breakage, the initial risk score receives a higher weight. This dynamic adjustment based on geographical region and time period makes the comprehensive risk score more consistent with reality.

[0051] This solution further proposes that, in step S4, the dynamic maintenance priority list includes the location information, service priority, and recommended maintenance strategy of high-risk fiber optic channels, and the recommended maintenance strategy includes preventive protection measures and emergency maintenance scheduling schemes.

[0052] In this embodiment, the dynamic maintenance priority list is generated based on a comprehensive risk score. The location information of high-risk fiber optic channels is determined by combining geographic information system (GIS) data with fiber optic channel routing data, accurate to specific pole segments or pipe sections. Service priorities are determined based on the type of service carried by the fiber optic channel; for example, fiber optic channels carrying 5G base station signal transmission have higher service priority than ordinary home broadband. Recommended preventative protection measures in the maintenance strategy include using high-hardness sheaths to wrap the optical fibers in areas susceptible to rodent infestation, and installing bird spikes in areas with frequent bird activity. The emergency maintenance scheduling plan clearly defines the departure time and arrival time limits for maintenance personnel, as well as the fault repair process, in the event of a high-risk fiber optic channel failure, ensuring that high-priority services can be restored as quickly as possible.

[0053] This solution further proposes that, in step S2, the characteristic parameters of the vibration signal are extracted through filtering and noise reduction and time-frequency analysis, and the time-frequency analysis includes vibration frequency, amplitude and periodic pattern; the vibration sensor is deployed in the bio-erosion-prone area of ​​the optical fiber channel, and the bio-erosion-prone area is determined based on the historical fiber breakage thermal map and geographical information data.

[0054] In the preprocessing of vibration signals, digital filtering technology is used for noise reduction to remove low-frequency and high-frequency noise while retaining effective signals related to bio-erosion behavior. Time-frequency analysis uses wavelet transform and other methods to convert the vibration signal from the time domain to the frequency domain, thereby extracting characteristic parameters such as vibration frequency, amplitude, and periodicity patterns. The deployment locations of vibration sensors are determined based on historical fiber breakage heat maps and geographic information data. In areas where bio-erosion has occurred multiple times in the past, as well as in areas with lush vegetation and frequent biological activity, the deployment density of sensors is increased to improve monitoring accuracy and sensitivity.

[0055] Example 2

[0056] A fiber channel service maintenance system, comprising:

[0057] The historical data acquisition module is used to acquire historical maintenance data of the fiber channel;

[0058] The real-time monitoring module is used to acquire real-time monitoring data of the fiber optic channel.

[0059] The initial risk calculation module is used to construct an initial risk score based on the historical maintenance data;

[0060] The biological erosion analysis module is used to extract feature parameters and identify the probability of biological erosion based on the real-time monitoring data;

[0061] The comprehensive risk calculation module is used to calculate a comprehensive risk score by integrating the initial risk score and the probability of biological erosion.

[0062] The maintenance priority generation module is used to generate a dynamic maintenance priority list based on the comprehensive risk score.

[0063] The historical data acquisition module connects to the historical database of the fiber optic network via an interface, extracts historical maintenance data from the database according to preset time intervals and data formats, and performs preliminary data processing and verification to ensure data integrity and accuracy. The real-time monitoring module establishes a communication connection with vibration sensors deployed on the fiber optic channel, receives vibration signals collected by the sensors in real time, and transmits and stores the signals in real time. The initial risk calculation module receives historical maintenance data provided by the historical data acquisition module, uses spatial clustering algorithms and risk weight calculation methods to generate an initial risk score. The bio-erosion analysis module receives vibration signals transmitted by the real-time monitoring module, preprocesses the signals and extracts feature parameters, and then calls a pre-trained bio-erosion behavior recognition model to obtain the bio-erosion probability. The comprehensive risk calculation module receives the initial risk score from the initial risk calculation module and the bio-erosion probability from the bio-erosion analysis module, and uses a spatiotemporal weighted algorithm to calculate a comprehensive risk score. The maintenance priority generation module generates a dynamic maintenance priority list based on the comprehensive risk score provided by the comprehensive risk calculation module and transmits the list to the relevant maintenance management system.

[0064] Example 3

[0065] A Fibre Channel service maintenance apparatus includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, cause the at least one processor to perform a Fibre Channel service maintenance method as described in any one of Embodiment 1.

[0066] The processor in the fiber optic channel service maintenance device coordinates the work of various modules, controlling data acquisition, processing, calculation, and output according to a preset program and time sequence. The memory stores computer programs containing all the steps and algorithms for implementing the methods described in claims 1 to 7. When the processor executes these programs, it first calls the historical data acquisition module and the real-time monitoring module to obtain relevant data. Then, it sequentially starts the initial risk calculation module, the bio-erosion analysis module, the comprehensive risk calculation module, and the maintenance priority generation module to perform data processing and calculation. Finally, it generates and outputs a dynamic maintenance priority list. The processor and memory communicate via an internal bus to ensure rapid data transmission and processing.

[0067] Example 4

[0068] An electronic device includes a fiber channel service maintenance device as described in Embodiment 3.

[0069] The electronic device, containing a Fibre Channel (FCC) service maintenance unit, is responsible for implementing various calculation and processing functions for FCC service maintenance. It establishes a connection with the FCC network through a communication interface. The communication interface uses standard network communication protocols, enabling communication between the electronic device and various nodes in the FCC network, including receiving real-time status data and sending maintenance control commands. The electronic device can be deployed in the FCC network's operation and maintenance center. It acquires relevant FCC data in real time through the communication interface and sends the generated dynamic maintenance priority list and control commands to on-site maintenance equipment and personnel, enabling remote maintenance and management of FCC services. Simultaneously, the electronic device can also receive execution results from on-site maintenance equipment, allowing for the evaluation and optimization of maintenance effectiveness.

[0070] This fiber optic channel service maintenance method, system, device, and electronic equipment achieve comprehensive risk assessment and dynamic maintenance of fiber optic channels through multi-module collaborative operation. Compared with traditional methods, its advantage lies in comprehensively considering historical fiber breakage data and real-time biological erosion monitoring data, improving the accuracy and comprehensiveness of risk assessment. By dynamically adjusting maintenance priorities and strategies, maintenance resources can be rationally allocated, preventative measures can be taken in advance, reducing the incidence of fiber breakage failures and ensuring the stable operation of high-priority services. For example, in a fiber optic channel with a history of frequent fiber breaks and a high probability of current biological erosion, the system will classify it as a high-priority maintenance target and promptly take measures such as installing protective devices to avoid fiber breakage accidents. Compared with traditional reactive maintenance methods, this greatly improves the reliability and stability of fiber optic communication.

[0071] In practical applications, this system can be widely used in various environments, including urban fiber optic networks and rural fiber optic transmission lines. In urban areas with frequent construction and dense populations, the system can focus on historical fiber breakage risks; in rural areas, it can enhance the monitoring and prevention of biological erosion risks. Through this targeted maintenance strategy, the intelligence and efficiency of fiber optic channel operation and maintenance are effectively improved, reducing unnecessary maintenance costs.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation methods of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for maintaining fiber optic channel services, characterized in that, The process includes the following steps: Step S1: Obtain historical maintenance data for the fiber optic channel. The historical maintenance data includes historical fiber breakage event records, service priority data, and geographic information data. The historical fiber breakage event records include the breakage time, breakage location, and breakage cause. The service priority data includes the service types carried by different fiber optic channels and their corresponding quality of service levels. The geographic information data includes the routing distribution and co-channel situation of the fiber optic channel. Simultaneously, obtain real-time monitoring data for the fiber optic channel. The real-time monitoring data includes vibration signals collected by vibration sensors deployed at key locations in the fiber optic channel. These vibration signals contain periodic high-frequency vibration characteristics caused by biological erosion behavior. Step S2: Construct an initial risk score for the fiber optic channel based on the historical maintenance data. Specifically, a spatial clustering algorithm is used to analyze the geographical distribution of historical fiber breakage events, and risk weights are calculated for fiber optic channels in different regions in combination with service priority data to generate an initial risk score. At the same time, feature parameters are extracted based on the real-time monitoring data to identify the probability of biological erosion. The feature parameters are obtained by preprocessing the vibration signal, including filtering and noise reduction and time-frequency analysis, to extract the vibration frequency, amplitude, and periodic pattern. Then, the feature parameters are input into a pre-trained biological erosion behavior recognition model to obtain the probability of biological erosion. Step S3: Integrate the initial risk score and the probability of biological erosion, and dynamically adjust the initial risk score and the probability of biological erosion using a spatiotemporal weighted algorithm to calculate the comprehensive risk score of the fiber optic channel and generate the final risk level; Step S4: Based on the comprehensive risk level, generate a dynamic maintenance priority list for the fiber optic channel. The dynamic maintenance priority list includes the location information, service priority, and recommended maintenance strategy for high-risk fiber optic channels. The recommended maintenance strategy includes preventive protection measures and emergency maintenance scheduling schemes.

2. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S2, the initial risk score is calculated using the following formula: ;in, Indicates the first Initial risk score for each fiber optic channel location The number of historical fiber optic cable breakages in the vicinity of this location. For the first The risk weights for each historical fiber breakage event are determined based on business priority data and geographic information data. For the first The spatial correlation between a historical fiber breakage event and the location of the fiber channel is calculated using a spatial clustering algorithm.

3. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S3, the comprehensive risk score is calculated using the following formula: ;in, Indicates the first The location of each fiber optic channel in time Comprehensive risk score, Assign an initial risk score to this location. For this location in time The probability of biological erosion, and The spatiotemporal weighting coefficients for the initial risk score and the probability of bioerosion, respectively, are determined by the following formula: ; ;in and For adjustment coefficients, Using historical and biological risks as the time reference point, this formula achieves real-time and accurate assessment of the overall risk of fiber optic channels by dynamically adjusting the weights of historical and biological risks.

4. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S2, the pre-trained biological erosion behavior recognition model is trained using historical vibration signal samples to distinguish the vibration characteristics of biological erosion behavior from those of non-biological erosion behavior.

5. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S3, the spatiotemporal weighted algorithm dynamically adjusts the weight ratio of the initial risk score and the probability of biological erosion based on the geographical region and time period of the optical fiber channel.

6. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S4, the dynamic maintenance priority list includes the location information, service priority, and recommended maintenance strategy of high-risk fiber optic channels. The recommended maintenance strategy includes preventive protection measures and emergency maintenance scheduling schemes.

7. The fiber optic channel service maintenance method according to claim 1, characterized in that, In step S2, the characteristic parameters of the vibration signal are extracted through filtering and noise reduction and time-frequency analysis. The time-frequency analysis includes vibration frequency, amplitude, and periodic pattern. The vibration sensor is deployed in the bio-erosion-prone area of ​​the optical fiber channel. The bio-erosion-prone area is determined based on the historical fiber breakage thermal map and geographical information data.

8. A fiber optic channel service maintenance system, characterized in that, include: The historical data acquisition module is used to acquire historical maintenance data of the fiber channel; The real-time monitoring module is used to acquire real-time monitoring data of the fiber optic channel. The initial risk calculation module is used to construct an initial risk score based on the historical maintenance data; The biological erosion analysis module is used to extract feature parameters and identify the probability of biological erosion based on the real-time monitoring data; The comprehensive risk calculation module is used to calculate a comprehensive risk score by integrating the initial risk score and the probability of biological erosion. The maintenance priority generation module is used to generate a dynamic maintenance priority list based on the comprehensive risk score.

9. The fiber optic channel service maintenance device according to claim 8, characterized in that, The device includes at least one processor and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the Fibre Channel service maintenance method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, Includes the fiber channel service maintenance device as described in claim 9.

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