A monitoring method and system based on a MEMS fiber switching matrix

Abstract

The present application relates to the technical field of optical fiber communication and optical fiber link performance monitoring, and specifically provides a monitoring method and system based on a MEMS optical fiber switching matrix, which comprises the following steps: a control management unit completes initial path mapping of a multi-fiber channel and configures an initial scanning frequency; performance parameters are polled and collected, and a real-time dynamic baseline model is constructed based on a sliding window algorithm; real-time parameters are collected at the initial frequency and compared with the baseline, and an abnormal signal is triggered when the deviation exceeds a threshold value; a redundant verification path is switched to for comparative analysis to distinguish between environmental interference and link self-failure; the scanning frequency is adjusted according to the fault type, the frequency of the link fault channel is increased, and the initial frequency of the environmental interference channel is maintained; and a state evaluation report is generated and a warning is issued. The method reduces false positives and false negatives through a dynamic baseline, improves fault positioning accuracy through a redundant path, optimizes resource allocation through dynamic adjustment of the scanning frequency, and comprehensively improves monitoring accuracy, efficiency and operation pertinence.

Description

Technical Field

[0001] This invention relates to the field of optical fiber communication and optical fiber link performance monitoring technology, specifically to a monitoring method and system based on a MEMS optical fiber switching matrix. Background Technology

[0002] Microelectromechanical systems (MEMS) fiber optic switching matrices achieve optical signal path switching through precise control of microstructures (such as micromirror arrays). They offer advantages such as small size, high switching speed (microseconds), and high reliability, making them core devices for dynamic scheduling of multiple fiber optic channels in high-speed optical communication networks. During the operation of optical communication networks, the performance stability of fiber optic links directly affects communication quality. The fiber optic channels connected to MEMS fiber optic switching matrices are susceptible to various factors: external interference such as ambient temperature fluctuations and vibrations can cause temporary fluctuations in parameters such as optical power and signal-to-noise ratio; fiber aging, loose interfaces, and fatigue of MEMS microstructures can lead to link failures, causing signal attenuation or interruption. Therefore, real-time monitoring of fiber optic channel performance parameters and rapid identification of fault types are crucial for ensuring stable network operation. Existing monitoring technologies have the following shortcomings:

[0003] Poor baseline adaptability: It often uses fixed thresholds as the criteria for anomaly detection, which cannot adapt to the normal fluctuations in optical fiber links caused by slow environmental changes (such as gradual changes in optical power caused by day-night temperature differences), and is prone to false alarms or missed alarms.

[0004] The distinction between fault types is unclear: after detecting abnormal parameters, it is difficult to effectively distinguish whether it is external environmental interference or a fault in the link itself, resulting in insufficient targeted operation and maintenance response (such as misjudging temporary interference as a fault and performing ineffective repairs).

[0005] Unreasonable allocation of monitoring resources: Using the same scanning frequency for all fiber optic channels either consumes too many resources due to high-frequency scanning or results in insufficient detailed fault data due to low-frequency scanning, making it impossible to balance monitoring efficiency and accuracy.

[0006] Insufficient integration with MEMS matrix characteristics: The high-speed switching capability of the MEMS matrix is ​​not fully utilized to achieve fault location and dynamic monitoring, resulting in delayed abnormal response.

[0007] Therefore, there is an urgent need for a monitoring method that can combine the characteristics of MEMS fiber optic switching matrix to achieve dynamic baseline updates, accurate fault classification, and adaptive resource scheduling, so as to improve the accuracy and efficiency of fiber optic link monitoring. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a monitoring method and system based on a MEMS fiber optic switching matrix to solve the problems in existing technologies.

[0009] This invention provides a monitoring method based on a MEMS fiber optic switching matrix, comprising the following steps:

[0010] The control and management unit sends control commands to the MEMS fiber optic switching matrix to complete the initial path mapping of multiple fiber optic channels and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel.

[0011] The MEMS fiber optic switching matrix polls each fiber optic channel to collect performance parameters within a preset time period, and constructs a real-time dynamic baseline model for each fiber optic channel based on a sliding window algorithm. The performance parameters include at least optical power, signal-to-noise ratio, and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters.

[0012] The MEMS fiber optic switching matrix collects real-time performance parameters of each fiber optic channel at the initial scanning frequency, compares the real-time collected performance parameters with the dynamic baseline model, and triggers an abnormal signal when the parameter deviation exceeds the preset threshold.

[0013] In response to an abnormal signal, the control and management unit controls the MEMS fiber optic switching matrix to switch to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it distinguishes between abnormal environmental interference and link failure.

[0014] Adjust the scanning frequency of the corresponding fiber channel according to the fault type. Increase the scanning frequency of the fiber channel with the link itself fault to obtain high-frequency scanning data, and maintain the initial scanning frequency of the fiber channel with abnormal environmental interference.

[0015] Based on the fault type and high-frequency scanning data, an optical fiber link status assessment report is generated and corresponding early warning information is issued.

[0016] In one embodiment, the step of the control management unit sending control commands to the MEMS fiber optic switching matrix to complete the initial path mapping of multiple fiber optic channels and configuring the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel includes the following steps:

[0017] The control and management unit sends control commands to the MEMS fiber optic switching matrix, which include the input port number, output port number, and mapping priority of each fiber optic channel.

[0018] According to the control command, the MEMS fiber optic switching matrix drives the internal micromirror array or waveguide switch to reset to the preset position, establishes the corresponding optical link connection between the input port and the output port, and completes the initial path mapping of multiple fiber optic channels. The mapping priority is used to determine the path establishment order when there is a channel resource conflict.

[0019] By adopting the above scheme, the core information of input / output port numbers and mapping priorities is clearly defined. Combined with the micromirror array or waveguide switch driving method of the MEMS fiber optic switching matrix, standardized operation of path mapping is achieved. For example, when high-priority channels (such as those carrying real-time monitoring data) and low-priority channels (such as those carrying non-real-time backup data) compete for port resources, the mapping priority can directly determine the path establishment order, avoiding link blocking or delays caused by resource conflicts. This solves the configuration chaos problem caused by ambiguous steps and missing priorities in traditional path mapping, improves the efficiency and reliability of initial mapping of multiple fiber optic channels, and lays the foundation for the stable operation of subsequent monitoring processes.

[0020] In one embodiment, the step of the control management unit sending a control command containing the input port number, output port number, and mapping priority of each fiber channel to the MEMS fiber switching matrix includes the following steps for determining the mapping priority:

[0021] Obtain the preset attribute parameters of each fiber channel, including the real-time requirements of the channel-carrying services, the data transmission rate threshold, the historical failure frequency, and the hierarchical weight of the link where the channel is located;

[0022] The priority score for each channel is calculated by weighting the preset attribute parameters according to their respective weights; among them, the weights for real-time requirements are ≥30%, data transmission rate thresholds are ≥25%, historical fault occurrence frequency is ≤20%, and link level is ≤25%.

[0023] Based on the priority score, the mapping priority is divided into three levels: a score greater than or equal to the first preset score is high priority, a score within the preset range of the second preset score is medium priority, and a score less than or equal to the third preset score is low priority. Among them, the high priority channel will be established first when there is a resource conflict.

[0024] By adopting the above scheme, based on preset attribute parameters such as the real-time requirements and transmission rate thresholds of fiber optic channels, a standardized process of weighted calculation and hierarchical classification is used to determine mapping priorities, achieving quantification and objectification of priority determination. For example, when prioritizing financial transaction channels (high real-time requirements, weight ≥ 30%) and general office channels (low real-time requirements), clear weighting rules can distinguish priority levels, ensuring that core business channels are given priority in establishing paths when resource conflicts occur. This solves the problem of judgment bias caused by the reliance on subjective experience in traditional priority setting, making the scheduling logic of mapping priorities more aligned with actual business needs, improving the scientificity and accuracy of path allocation when resource conflicts occur, and providing a reliable decision-making basis for the orderly mapping of multiple fiber optic channels.

[0025] In one embodiment, determining the dynamic reference range includes the following steps:

[0026] Based on the preset duration, a dual-window hierarchy is divided, including a historical feature sliding window and a real-time fluctuation sliding window. The window duration of the historical feature sliding window is 2 / 3 of the preset duration, and the window duration of the real-time fluctuation sliding window is 1 / 3 of the preset duration.

[0027] Within the historical feature sliding window, the MEMS fiber optic switching matrix is ​​used to switch to the redundant verification path to collect the performance parameters of the fiber optic channel under interference-free conditions, and calculate the long-term trend curve of its performance parameters and the inherent fluctuation threshold.

[0028] Within the real-time fluctuation sliding window, performance parameter data is extracted according to a preset sliding step size, and the environmental interference correction coefficient is calculated in combination with the current environmental monitoring data. The sliding step size is 1 / 5 of the window duration of the real-time fluctuation sliding window.

[0029] The long-term trend curve of the historical feature sliding window is used as the baseline reference, and the inherent fluctuation threshold of the historical feature sliding window and the environmental interference correction coefficient of the real-time fluctuation sliding window are superimposed to form a dynamic reference range. Based on the dynamic reference range, the real-time dynamic baseline model of the optical fiber channel is obtained.

[0030] By adopting the above scheme, the long-term inherent characteristics and short-term environmental interference effects of the optical fiber channel can be extracted separately based on the dual sliding window hierarchy. A dynamic baseline range is constructed by combining interference-free parameters collected from redundant verification paths with environmental correction coefficients, achieving accurate adaptation to link performance fluctuations. For example, when a sudden increase in ambient temperature causes short-term fluctuations in optical power, the correction coefficient of the real-time fluctuation sliding window will increase accordingly, widening the dynamic baseline range and avoiding misjudging normal environmental interference as link failure. Conversely, when the link experiences slow optical power attenuation due to aging, the long-term trend curve of the historical characteristic sliding window will synchronously update the baseline, ensuring timely capture of actual performance degradation. This solves the problem that traditional fixed baselines or single sliding windows cannot adequately consider both long-term characteristics and real-time interference, significantly improving the adaptability and accuracy of the dynamic baseline model and providing reliable baseline support for accurate judgment of subsequent anomaly detection.

[0031] In one embodiment, the step of the MEMS fiber optic switching matrix acquiring real-time performance parameters of each fiber optic channel at an initial scanning frequency, comparing the real-time acquired performance parameters with a dynamic baseline model, and triggering an abnormal signal when the parameter deviation exceeds a preset threshold, further includes the following verification step before triggering the abnormal signal:

[0032] If the fiber channel has hardware functional association with other channels, then the verification for common hardware interference is performed first. The hardware functional association means that the fiber channel shares the same micromirror driving module or waveguide coupling region.

[0033] If the fiber channel is spatially associated with other channels, then verification against environmental cluster interference is performed first. The spatial association refers to the fiber channel sharing the same micromirror unit or adjacent waveguide paths.

[0034] When the above verification determines that it is a non-interference factor, the abnormal signal is then triggered.

[0035] By adopting the above scheme, a dual-path verification step based on hardware function correlation and spatial location correlation is added before the abnormal signal is triggered, enabling targeted filtering of two typical interference scenarios in the MEMS fiber optic switching matrix. For example, when the performance parameters of a fiber optic channel fluctuate due to a shared drive module, hardware function correlation verification can quickly identify common interference; when adjacent waveguides experience synchronous fluctuations due to local temperature effects, spatial location correlation verification can accurately determine environmental cluster interference. This solves the problem of misjudgment of interference caused by traditional anomaly detection relying solely on a single threshold, making interference screening before the abnormal signal is triggered more targeted, providing a layered verification basis for subsequent accurate alarms, and improving the overall reliability of monitoring.

[0036] In one embodiment, the step of controlling the MEMS fiber optic switching matrix to switch to the corresponding redundant verification path in response to an abnormal signal, and distinguishing between environmental interference anomalies and link faults by comparing and analyzing the performance parameters of the original path and the redundant verification path, includes the following steps:

[0037] Based on the abnormal signal, locate the target micromirror unit group corresponding to the original path, and call the redundant micromirror unit group that is physically isolated from the original micromirror unit group to construct a redundant verification path;

[0038] Simultaneously collect the optical power attenuation rate and micromirror deflection error of the original path and the redundant verification path;

[0039] The path difference corresponding to the attenuation rate of the collected optical power and the micromirror deflection error is calculated separately, specifically as follows:

[0040] Optical power attenuation rate difference = |original path attenuation rate - redundant path attenuation rate| / original path attenuation rate;

[0041] Micromirror deflection error difference = |Original path error - Redundant path error| / Original path error;

[0042] If the difference in optical power attenuation rate is less than or equal to the preset attenuation threshold, and the difference in micromirror deflection error is greater than the preset error threshold, it is determined to be a link fault; if the difference in optical power attenuation rate is greater than the preset attenuation threshold, and the difference in micromirror deflection error is less than or equal to the preset error threshold, it is determined to be an abnormal environmental interference.

[0043] By employing the above scheme, a verification path is constructed using physically isolated redundant micromirror units. Combined with quantitative analysis of the difference between optical power attenuation rate and micromirror deflection error, environmental interference anomalies and link faults can be accurately distinguished. Utilizing the hardware characteristic of "physically partitioned and isolated micromirror units" in MEMS fiber optic switching matrices, this avoids common interference between redundant and original paths, resolving the problem of misjudgment of fault types due to path correlation in traditional redundancy comparisons. This improves the accuracy of anomaly root cause localization and provides a reliable basis for subsequent targeted treatment (such as link repair or environmental control).

[0044] In one embodiment, the step of adjusting the scanning frequency of the corresponding fiber optic channel according to the fault type, increasing the scanning frequency of the fiber optic channel with a fault in the link itself to obtain high-frequency scanning data, and maintaining the initial scanning frequency of the fiber optic channel with abnormal environmental interference includes the following steps:

[0045] For fiber optic channels with inherent link failures, the cumulative operating time and historical deflection count of the corresponding micromirror units are obtained to calculate the hardware fatigue coefficient.

[0046] The frequency boost level is determined based on the hardware fatigue coefficient, specifically as follows:

[0047] If the hardware fatigue coefficient is less than or equal to the first coefficient threshold, the scanning frequency will be increased to the first multiple of the initial scanning frequency.

[0048] If the hardware fatigue coefficient is greater than the first coefficient threshold but less than or equal to the second coefficient threshold, the scanning frequency will be increased to a second multiple of the initial scanning frequency.

[0049] If the hardware fatigue coefficient is greater than the second coefficient threshold, the scanning frequency will be increased to the third multiple of the initial scanning frequency, and the duration of a single high-frequency scan will not exceed the preset safe duration.

[0050] For fiber optic channels with abnormal environmental interference, monitor the fluctuation range of environmental parameters in the associated area of ​​the fiber optic channel. If the fluctuation range is less than or equal to the environmental stability threshold, maintain the initial scanning frequency; if the fluctuation range is greater than the environmental stability threshold, perform a short-term high-frequency scan at a preset interval based on the initial scanning frequency.

[0051] The system calculates the density of abnormal features in high-frequency scan data in real time. When the density remains below a preset threshold for an extended period, it automatically reverts to the initial scan frequency.

[0052] By adopting the above scheme, the frequency enhancement level of the faulty channel of the link is dynamically divided by the hardware fatigue coefficient. Combined with the physical characteristics of the micromirror unit to limit the high-frequency scanning intensity, the problem of high-frequency monitoring accelerating hardware aging is avoided, balancing the fault monitoring accuracy and the hardware lifespan of the MEMS fiber optic switching matrix. For abnormal channels with environmental interference, the "initial frequency + short-interval high-frequency scanning" mode is adopted, which can accurately capture the instantaneous characteristics of environmental fluctuations while reducing the consumption of ineffective resources. The abnormal feature density is introduced as the basis for frequency callback, realizing adaptive dynamic adjustment of the scanning frequency, avoiding the waste of system resources caused by maintaining a high-frequency state for a long time, improving the flexibility and economy of the scanning strategy, and providing an efficient frequency control scheme for targeted monitoring of different types of faults.

[0053] This application also relates to a monitoring system based on a MEMS fiber optic switching matrix, comprising:

[0054] The path mapping module is used to control the management unit to send control commands to the MEMS fiber optic switching matrix, complete the initial path mapping of multiple fiber optic channels, and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel.

[0055] The baseline model construction module is used to poll each optical fiber channel through the MEMS optical fiber switching matrix, collect performance parameters within a preset time period, and construct a real-time dynamic baseline model for each optical fiber channel based on the sliding window algorithm; wherein, the performance parameters include at least optical power, signal-to-noise ratio and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters.

[0056] The parameter acquisition and comparison module is used to acquire real-time performance parameters of each fiber channel of the MEMS fiber switching matrix at the initial scanning frequency, compare the real-time acquired performance parameters with the dynamic baseline model, and trigger an abnormal signal when the parameter deviation exceeds the preset threshold.

[0057] The fault differentiation module is used to respond to abnormal signals and control the management unit to switch the MEMS fiber optic switching matrix to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it can distinguish between environmental interference abnormalities and link faults.

[0058] The scanning frequency adjustment module is used to adjust the scanning frequency of the corresponding fiber optic channel according to the fault type. It increases the scanning frequency of fiber optic channels with link faults to obtain high-frequency scanning data, and maintains the initial scanning frequency of fiber optic channels with abnormal environmental interference.

[0059] The assessment and early warning module is used to generate fiber optic link status assessment reports and issue corresponding early warning information based on fault type and high-frequency scanning data.

[0060] This application also relates to a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned monitoring method based on a MEMS fiber optic switching matrix.

[0061] This application also relates to a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned monitoring method based on a MEMS fiber optic switching matrix.

[0062] The monitoring method based on a MEMS fiber optic switching matrix provided in the above embodiments has the following beneficial effects:

[0063] It can leverage the high-speed switching capabilities of MEMS fiber optic switching matrices to achieve adaptive monitoring of fiber optic channel performance parameters through a dynamic baseline model, resolving false alarms and missed alarms caused by environmental fluctuations under fixed thresholds. Through comparative analysis of redundant verification paths, it accurately distinguishes between environmental interference and link-specific faults, improving the accuracy of fault location. By dynamically adjusting the scanning frequency based on channel importance and fault type, it achieves on-demand allocation of monitoring resources, reducing resource consumption while ensuring the monitoring accuracy of core links. Finally, through the fusion analysis of fault types and high-frequency data, it generates accurate status assessment reports and early warning information, comprehensively improving the accuracy, efficiency, and targeted operation and maintenance response of fiber optic communication network monitoring. Attached Figure Description

[0064] Figure 1 A flowchart illustrating a monitoring method based on a MEMS fiber optic switching matrix provided in an embodiment of the present invention;

[0065] Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings.

[0067] Reference Figure 1 One embodiment of the present invention provides a monitoring method based on a MEMS fiber optic switching matrix, comprising the following steps:

[0068] S10. The control and management unit sends control commands to the MEMS fiber optic switching matrix to complete the initial path mapping of multiple fiber optic channels and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel.

[0069] In this embodiment, the initial path mapping is led by the control management unit, which establishes the correspondence between the physical paths and logical links of multiple fiber optic channels by sending control commands to the MEMS fiber optic switching matrix. The control commands may include core information such as the initial deflection angle parameters of the micromirror units and the correspondence between the input / output ports of the fiber optic channels. After receiving the commands, the MEMS fiber optic switching matrix completes the conduction of the physical optical paths through the mechanical adjustment of the micromirror units, for example, realizing initial mapping relationships such as "logical channel 1 → physical port A" and "logical channel 2 → physical port B". This process utilizes the micromirror unit control characteristics of the MEMS fiber optic switching matrix to determine the initial optical signal transmission path of each fiber optic channel, providing basic physical link support for subsequent performance parameter acquisition and monitoring. For the initial scan frequency configuration, after the path mapping is completed, a corresponding initial scan frequency is assigned to each channel based on its "preset importance level" (such as core service channels, ordinary service channels, etc.). For example, the preset importance level can be divided into three levels: the initial scan frequency for core service channels (such as channels carrying real-time data transmission) is configured as 10Hz, for ordinary service channels as 5Hz, and for redundant spare channels as 2Hz. The classification of importance levels can be based on actual circumstances and scenarios, taking into account factors such as business priority and data type of transmission. This classification is not limited here, as it is clear to those skilled in the art and will not be elaborated further. Different levels correspond to different initial scanning frequencies to achieve preliminary differentiated allocation of monitoring resources.

[0070] S20. Poll each fiber channel through the MEMS fiber switching matrix to collect performance parameters within a preset time period, and construct a real-time dynamic baseline model for each fiber channel based on the sliding window algorithm; wherein, the performance parameters include at least optical power, signal-to-noise ratio and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters.

[0071] In this embodiment, for polling performance parameters, the MEMS fiber optic switching matrix scans each fiber optic channel sequentially in a preset order, collecting performance parameters in real time within a preset duration (e.g., 5 minutes, 10 minutes, etc., the specific duration can be set according to actual monitoring needs). Performance parameters include at least optical power, signal-to-noise ratio, and optical delay. For example, the polling process can proceed sequentially according to channel number, continuously collecting 100 sets of parameter data for each channel before switching to the next channel, ensuring comprehensive acquisition of the initial performance characteristics of each channel. For constructing a real-time dynamic baseline model, the collected historical performance parameters are processed based on a sliding window algorithm. The size of the sliding window can be set according to the parameter fluctuation characteristics (e.g., a window size of 30 sets of data, with the window sliding forward once for every 5 additional sets of data; the specific value can be adjusted). By analyzing the historical fluctuation characteristics of the parameters within the window (e.g., mean, standard deviation, fluctuation trend, etc.), the dynamic benchmark range of the performance parameters corresponding to each fiber optic channel is determined, i.e., the dynamic baseline model. For example, the dynamic reference range of optical power can be set to "mean within the window ± 2 standard deviations", and the reference range of signal-to-noise ratio can be set based on 80%-120% of the historical fluctuation peak. The model can be updated in real time as the parameters change, providing a dynamic reference standard for subsequent anomaly judgment.

[0072] The S30 and MEMS fiber optic switching matrix collects real-time performance parameters of each fiber optic channel at the initial scanning frequency, compares the real-time collected performance parameters with the dynamic baseline model, and triggers an abnormal signal when the parameter deviation exceeds the preset threshold.

[0073] In this embodiment, the MEMS fiber optic switching matrix collects real-time performance parameters for each fiber optic channel according to the initial scanning frequency configured in step S10, ensuring that channels of different importance levels acquire monitoring data at differentiated frequencies. For example, the core service channel collects optical power, signal-to-noise ratio, and optical delay parameters 10 times per second at a frequency of 10Hz, while ordinary channels collect them at a frequency of 5Hz. After collection, the real-time performance parameters of the corresponding fiber optic channel are compared with the dynamic baseline model corresponding to that fiber optic channel constructed in step S20 to determine whether the performance parameters are within the normal fluctuation range. The comparison logic can be calculated using the "difference between the parameter deviation and the boundary of the reference range," such as the difference between the real-time optical power value and the upper / lower limit of the dynamic baseline model, or the deviation ratio of the real-time signal-to-noise ratio value from the baseline mean. When the parameter deviation exceeds a preset threshold, an abnormal signal is triggered. The preset threshold can be set according to the parameter type, such as optical power deviation exceeding the baseline range by ±3dB, signal-to-noise ratio deviation from the mean by 15%, or optical delay fluctuation exceeding 50ns. The abnormal signal will trigger the subsequent fault differentiation process, providing triggering conditions for accurately locating the root cause of the abnormality.

[0074] S40. In response to an abnormal signal, the control and management unit controls the MEMS fiber optic switching matrix to switch to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it distinguishes between environmental interference abnormalities and link faults.

[0075] In this embodiment, when an abnormal signal is triggered in step S30, the control management unit immediately responds and sends a path switching command to the MEMS fiber optic switching matrix, controlling it to switch to the redundant verification path corresponding to the original faulty channel. The redundant verification path is a pre-configured backup link, which maintains an independent physical route from the original path (e.g., using different micromirror unit groups, independent fiber optic links, etc.) to ensure that it is not directly affected by potential faults in the original path. For example, when the original path uses micromirror unit group A to achieve signal transmission, the redundant verification path can use physically isolated micromirror unit group B to build a backup link. Then, by synchronously collecting the performance parameters of the original path and the performance parameters of the redundant verification path (such as optical power attenuation rate, signal stability, etc.), a comparative analysis is performed to distinguish the abnormality type. The comparison logic can be based on the consistency of parameter fluctuations of the two types of paths: if the parameters of the original path are abnormal while the parameters of the redundant path are normal, it indicates that the abnormality originates from the hardware or link problem of the original path itself (i.e., the link itself is faulty); if the parameters of the two paths show similar abnormal characteristics, it is more likely that environmental interference abnormalities are caused by external environmental factors (such as temperature fluctuations, vibration interference, etc.). For example, if the optical power of the original path drops sharply by 3dB while the optical power of the redundant path remains stable, it is determined to be a link failure. If the optical power of both paths drops by 1.5dB simultaneously, it is more likely due to abnormal environmental interference (the above judgment logic is only an example, and the specific analysis dimensions can be adjusted). Through this comparative analysis, the root cause of the anomaly can be accurately distinguished, providing a basis for subsequent targeted processing.

[0076] S50. Adjust the scanning frequency of the corresponding fiber optic channel according to the fault type. Increase the scanning frequency of the fiber optic channel with a fault in the link itself to obtain high-frequency scanning data, and maintain the initial scanning frequency of the fiber optic channel with abnormal environmental interference.

[0077] In this embodiment, the scanning frequency of the corresponding fiber optic channel is adjusted differently based on the fault type identified in step S40. For fiber optic channels determined to be faults in the link itself, the scanning frequency is increased to obtain denser high-frequency scanning data, thereby accurately capturing the fault development trend or instantaneous characteristics. For example, a channel with an initial scanning frequency of 5Hz can be increased to 15Hz (i.e., 15 times per second to collect performance parameters). High-frequency data can more meticulously reflect the dynamic changes of fault parameters such as micromirror unit deflection error and optical power attenuation. The specific increase in frequency value can be set according to actual needs. For fiber optic channels determined to be affected by abnormal environmental interference, since interference usually has phased or intermittent characteristics, maintaining the initial scanning frequency configured in step S10 is sufficient to meet monitoring requirements, avoiding the waste of resources caused by high-frequency scanning. For example, a normal channel with an initial frequency of 3Hz continues to collect data at this frequency, and the scanning density can be temporarily increased only when environmental parameters (such as temperature and vibration) fluctuate drastically. Through this frequency adjustment strategy based on fault type, monitoring resources are allocated on demand, ensuring the monitoring accuracy of faulty channels while avoiding unnecessary resource consumption.

[0078] S60. Based on the fault type and high-frequency scanning data, generate an optical fiber link status assessment report and issue corresponding early warning information.

[0079] In this embodiment, based on the fault type (link fault or abnormal environmental interference) determined in step S40, and combined with the high-frequency scanning data (for the faulty link channel) or initial frequency acquisition data (for the abnormal environmental interference channel) obtained in step S50, a comprehensive analysis is performed to generate an optical fiber link status assessment report. The assessment report may include: the specific identifier of the faulty channel, the detailed basis for determining the fault type (such as the specific values ​​of abnormal performance parameters and fluctuation trends), the duration of the fault, and the potential impact on service transmission. For example, for a link fault, the report may specify that "Channel 3 has excessive micromirror deflection error, and the optical power attenuation rate has increased from 2% to 8% in the past 10 minutes"; for an abnormal environmental interference, it may describe that "Channels 1-5 in area A are affected by temperature fluctuations, and the signal-to-noise ratio decreases synchronously but does not exceed the safety threshold."

[0080] Simultaneously, corresponding early warning information is issued based on the assessment results, with warning levels categorized according to the severity of the fault (e.g., minor, moderate, and emergency warnings). For example, an emergency warning is triggered when the link itself fails and its parameters continue to deteriorate, indicating the need for immediate repair; a minor warning is triggered when environmental interference is abnormal but does not affect services, suggesting attention to environmental control. By generating assessment reports and tiered warnings, clear fault handling guidelines are provided to operations and maintenance personnel, improving the operational and maintenance response efficiency of fiber optic networks.

[0081] In one embodiment, step S10, completing the initial path mapping of the multiple fiber channels, includes the following steps:

[0082] S110, The control management unit sends a control command to the MEMS fiber optic switching matrix, which includes the input port number, output port number and mapping priority of each fiber optic channel.

[0083] S120 and MEMS fiber optic switching matrix, according to the control command, drive the internal micromirror array or waveguide switch to reset to the preset position, establish corresponding optical link connections between the input port and the output port, and complete the initial path mapping of multiple fiber optic channels. The mapping priority is used to determine the path establishment order when there is a channel resource conflict.

[0084] In this embodiment, step S110 involves generating and sending control commands. The control management unit sends control commands containing key information to the MEMS fiber optic switching matrix. The key information includes at least the input port number (such as IN1, IN2, etc., physical interface identifiers), output port number (such as OUT1, OUT2, etc., physical interface identifiers), and mapping priority (which can be set to 1-5 levels, with higher values ​​indicating higher priority). For example, a priority of 5 can be assigned to a channel carrying emergency services, and a priority of 3 can be assigned to a channel carrying ordinary services.

[0085] In step S120, after receiving the control command sent in step S110, the MEMS fiber optic switching matrix parses the input port number, output port number, and mapping priority information contained in the command, and then drives the internal core components (micromirror array or waveguide switch) to perform a reset operation: For the micromirror array, a precision motor controls each micromirror unit to rotate to a preset initial angle (such as a reference position with a 0° deflection, or a specific angle preset according to the port correspondence, for example, for the correspondence between input port IN3 and output port OUT7, the micromirror unit needs to be reset to a 30° deflection position to achieve optical path alignment); for the waveguide switch, an electronic control signal controls the switch core to switch to a preset path state (such as connecting the optical path between the waveguide input end and the specified output end, for example, switching to channel 3 to physically connect IN5 and OUT2). The preset position refers to the hardware state parameters (such as micromirror angle, waveguide switch position) preset according to the design route of the fiber optic channel to ensure that the optical signal can be transmitted along the preset path. After reset, a physical optical link connection between the input and output ports is established by precisely adjusting the angle of the micromirrors (to ensure the reflected light is accurately directed to the target output port) or switching the waveguide switch (to ensure the optical signal is transmitted along a specified waveguide). For example, the input port IN1 is reflected by the micromirror M2 (reset to 25°) in the micromirror array, and the optical signal is accurately transmitted to the output port OUT4, completing the optical link mapping between IN1 and OUT4; or the optical signal of IN6 is switched to the waveguide path corresponding to OUT9 by the waveguide switch, realizing the physical connection between the two (the angles and port numbers mentioned above are just examples). When multiple fiber optic channels request to occupy the same hardware resource (i.e., resource conflict, such as two channels needing to share the same micromirror unit or the same waveguide segment), the mapping priority in the control command comes into play: the system allocates resources to the channels in descending order of priority, with high-priority channels completing the core component reset and link establishment first, while low-priority channels wait for the resources to be released before performing operations. For example, if both priority 5 channel A and priority 3 channel B require the use of micromirror unit M5, M5 is first driven to reset to the required 40° position for channel A, completing the IN2 and OUT5 connection for channel A. Then, M5 is adjusted to the required 15° position for channel B, establishing the IN4 and OUT8 connection for channel B (the above priorities and operation sequence are only examples). Through this operation, the MEMS fiber optic switching matrix completes the initial path mapping of multiple fiber optic channels, providing a stable physical link foundation for subsequent data transmission and monitoring. Simultaneously, through the resource scheduling logic of mapping priorities, it ensures that links for high-priority services are established first.

[0086] In one embodiment, step S110, determining the mapping priority includes the following steps:

[0087] S111. Obtain the preset attribute parameters of each optical fiber channel. The preset attribute parameters include the real-time requirements of the channel-carrying services, the data transmission rate threshold, the frequency of historical faults, and the hierarchical weight of the link where the channel is located.

[0088] S112. Calculate the priority score for each channel by weighting the preset attribute parameters according to their respective weights; where the weights are: real-time requirement ≥ 30%, data transmission rate threshold ≥ 25%, historical fault occurrence frequency ≤ 20%, and link level ≤ 25%.

[0089] S113. Based on the priority score, the mapping priority is divided into three levels: a score greater than or equal to the first preset score is high priority, a score within the preset range of the second preset score is medium priority, and a score less than or equal to the third preset score is low priority. Among them, the high priority channel will be established first when there is a resource conflict.

[0090] In this embodiment, step S111 is used to obtain preset attribute parameters for each fiber optic channel. These preset attribute parameters include: the real-time requirements of the services carried by the channel (e.g., whether it is a latency-sensitive service such as industrial control or telemedicine; higher real-time requirements result in a more significant weighting of this parameter in the score), the data transmission rate threshold (the maximum transmission rate supported by the channel design, reflecting its ability to carry high-bandwidth services, such as 10Gbps or 40Gbps channel design transmission rates), the historical fault occurrence frequency (the number of faults within a statistical period, reflecting channel stability; a lower fault frequency results in a better score, such as the number of faults in the past 30 days), and the layer weight of the link where the channel is located (e.g., the weight values ​​corresponding to network layers such as the core layer, aggregation layer, and access layer; for example, core layer links directly affect the entire network communication, and their layer weight is higher than that of access layer links). For example, a certain channel carries a real-time monitoring service (high real-time requirements), has a transmission rate threshold of 100Gbps, has experienced 2 faults in the past 30 days, and is located on an aggregation layer link.

[0091] Step S112 is the priority score calculation, where "weight percentage" refers to the weight of each preset attribute parameter in the total score. The priority score is obtained through weighted calculation: real-time requirement weight ≥ 30% (e.g., set to 35% to ensure that latency-sensitive services are prioritized), data transmission rate threshold weight ≥ 25% (e.g., set to 30% to reflect the importance of high-bandwidth channels), historical fault frequency weight ≤ 20% (e.g., set to 15% to avoid stability factors excessively affecting the priority of core service channels), and link layer weight ≤ 25% (e.g., set to 20% to balance resource allocation across different network layers). Each parameter is converted into a base score according to its performance (e.g., high real-time requirement scores 90 points, 40Gbps transmission rate threshold scores 80 points, etc.), and then multiplied by the corresponding weight and summed to obtain the total score.

[0092] For example, if the scores of the four parameters of a certain channel are 90, 80, 85, and 70 respectively, and the weights are 35%, 30%, 15%, and 20%, the score is calculated as follows: 90×35%+80×30%+85×15%+70×20%=31.5+24+12.75+14=82.25 points.

[0093] Step S113 involves priority grading. The "first / second / third preset scores" are critical values ​​used to divide priorities. Based on the priority scores, the mapped priorities are divided into three levels: scores ≥ the first preset score (e.g., 85 points, which can be adjusted according to total business volume) are high priority; scores within the second preset score range (e.g., 60-84 points) are medium priority; and scores ≤ the third preset score (e.g., 59 points) are low priority. For example, a channel with a score of 82.25 is determined to be medium priority, while a channel with a score of 90 is determined to be high priority. High-priority channels are prioritized for path establishment in the event of resource conflicts. By clearly defining the quantitative logic of "weight ratio" and "preset scores," the subjectivity of priority division is avoided, ensuring the matching of mapped priorities with business needs and channel performance.

[0094] In one embodiment, step S20, determining the dynamic reference range includes the following steps:

[0095] S201. Based on the preset duration, a dual-window hierarchy is divided, including a historical feature sliding window and a real-time fluctuation sliding window, wherein the window duration of the historical feature sliding window is 2 / 3 of the preset duration, and the window duration of the real-time fluctuation sliding window is 1 / 3 of the preset duration.

[0096] S202. Within the historical feature sliding window, switch to the redundant verification path through the MEMS fiber optic switching matrix, collect the performance parameters of the fiber optic channel under interference-free conditions, and calculate the long-term trend curve and inherent fluctuation threshold of its performance parameters.

[0097] S203. Within the real-time fluctuation sliding window, performance parameter data is extracted according to a preset sliding step size, and the environmental interference correction coefficient is calculated in combination with the current environmental monitoring data, wherein the sliding step size is 1 / 5 of the window duration of the real-time fluctuation sliding window.

[0098] S204. The long-term trend curve of the historical feature sliding window is used as the baseline reference, and the inherent fluctuation threshold of the historical feature sliding window and the environmental interference correction coefficient of the real-time fluctuation sliding window are superimposed to form a dynamic reference range. Based on the dynamic reference range, the real-time dynamic baseline model of the optical fiber channel is obtained.

[0099] In this embodiment, step S201 divides the historical feature sliding window and the real-time fluctuation sliding window based on a preset duration (e.g., 30 minutes, which can be adjusted). The window duration of the historical feature sliding window is 2 / 3 of the preset duration (e.g., 20 minutes under a preset duration of 30 minutes), used to capture long-term performance characteristics; the window duration of the real-time fluctuation sliding window is 1 / 3 of the preset duration (e.g., 10 minutes), used to capture the instantaneous impact of recent environmental changes on parameters. The "dual-window hierarchy" distinguishes between long-term trends and short-term fluctuations, enabling the baseline model to include both historical patterns and respond to real-time changes.

[0100] In step S202, within the historical feature sliding window, the MEMS fiber optic switching matrix first switches to the redundant verification path—this path is a physically isolated backup link (not carrying actual service data and far from major interference sources, such as high-temperature equipment and vibration sources), ensuring a state without service load and external interference, providing an environmental basis for collecting interference-free performance parameters. The acquisition process continues at fixed time intervals (e.g., once every 10 seconds) to obtain raw data of parameters such as optical power, signal-to-noise ratio, and optical delay (e.g., 120 sets of optical power data are collected within 20 minutes). For the long-term trend curve, it is obtained by fitting all data within the window: for example, a linear regression algorithm is used for the optical power data to fit a trend line of "time-optical power" (e.g., y=-0.0017x+1.2, where y is the optical power value and x is the time in minutes), reflecting the slow change of parameters over time (e.g., optical power attenuation of 0.1dB per hour due to micromirror aging). The inherent fluctuation threshold is calculated based on the natural fluctuation characteristics of the parameters within the window and is used to define the normal fluctuation range under undisturbed conditions. For example, when calculating the standard deviation of optical power data (e.g., 0.5 dB), the threshold is set to "mean ± 2 standard deviations" (i.e., ± 1 dB), or the maximum fluctuation difference in the data is taken (e.g., if the difference between the maximum and minimum values ​​is 1.8 dB, the threshold is set to ± 0.9 dB). Through these two indicators, the long-term trend of parameter changes is clarified, and the boundary of normal fluctuation is defined, providing a stable historical benchmark for the dynamic baseline.

[0101] In step S203, the sliding step size is 1 / 5 of the window duration (e.g., a 10-minute window corresponds to a 2-minute step size). That is, every 2 minutes, the window slides forward once, capturing the performance parameter data of the previous 10 minutes each time (forming continuously overlapping sub-windows, such as minutes 0-10, minutes 2-12, minutes 4-14, etc.) to ensure continuous tracking of real-time fluctuations. Simultaneously, environmental monitoring data of the channel-related area is acquired, including temperature, vibration amplitude, humidity, etc. This data is collected in real time by sensors deployed near the fiber optic link (e.g., temperature sensors transmit data every 5 seconds). The calculation of the environmental interference correction coefficient is based on the correlation between parameter fluctuations and environmental changes. For example, by analyzing historical data, the correlation between "optical power decreasing by 0.2dB for every 1°C increase in temperature" and "signal-to-noise ratio decreasing by 0.5dB for every 5μm increase in vibration amplitude" is determined. This is then combined with the difference between the current environmental parameters and historical averages (e.g., current temperature 3°C higher than average, vibration amplitude 10μm higher), to calculate a comprehensive correction coefficient (e.g., optical power correction coefficient -0.6dB, signal-to-noise ratio correction coefficient -1.0dB). The role of the environmental interference correction coefficient is to dynamically adjust the baseline range to offset the impact of environmental changes on parameters. For example, when a sudden increase in ambient temperature leads to a decrease in optical power, the baseline range is adjusted downwards synchronously using the correction coefficient to avoid misjudging link failures due to environmental fluctuations. The above step size, environmental parameters, and coefficient calculation methods are only examples and can be adjusted according to link characteristics to ensure that the real-time fluctuation window accurately reflects the immediate impact of the environment on the parameters.

[0102] In step S204, the long-term trend curve of the historical feature sliding window is used as the baseline. The inherent fluctuation threshold of the historical feature sliding window (reflecting the hardware's own characteristics) and the environmental interference correction coefficient of the real-time fluctuation sliding window (reflecting external influences) are then superimposed to form a dynamic baseline range. For example, if the baseline is a -0.1dB / h attenuation curve, after superimposing ±0.8dB inherent fluctuation and ±1dB environmental correction, the dynamic baseline range is "(-0.1dB / h±0.8dB)±1dB". Based on this dynamic baseline range, a real-time dynamic baseline model is obtained, enabling it to adapt to slow changes in hardware performance while also mitigating the instantaneous impact of environmental interference, thus improving the accuracy of subsequent anomaly detection.

[0103] In one embodiment, step S30 further includes the following verification step before triggering the abnormal signal:

[0104] S310. If the fiber channel has hardware functional association with other channels, then first perform verification for common hardware interference. The hardware functional association means that the fiber channel shares the same micromirror driving module or waveguide coupling region.

[0105] S320. If the fiber optic channel is spatially associated with other channels, then perform verification against environmental cluster interference first. The spatial association is that the fiber optic channels share the same micromirror unit or adjacent waveguide paths.

[0106] S330. When the above verification determines that it is a non-interference factor, then trigger the abnormal signal.

[0107] In this embodiment, step S310 is a hardware common interference verification step, applicable to fiber optic channels with hardware functional associations. "Hardware functional association" refers to the physical connection relationship where multiple fiber optic channels share the same key hardware module, such as sharing the same micromirror driver module (shared drive signal) or the same waveguide coupling region (intersecting optical signal transmission paths). When a parameter of a certain fiber optic channel is abnormal, it is necessary to first verify whether the common interference is caused by a shared hardware failure: for example, if channel 1 and channel 2 share micromirror driver module A, when the optical power of channel 1 is abnormal, the parameters of channel 2 are simultaneously detected. If both exhibit similar abnormal characteristics, it may be hardware common interference caused by a failure of driver module A.

[0108] Step S320 is the environmental clustering interference verification step, applicable to fiber optic channels with spatial location association. "Spatial location association" refers to the physical proximity of multiple fiber optic channels, such as sharing the same micromirror unit (physical location overlap) or adjacent waveguide paths (spacing less than a preset safety distance). Such fiber optic channels are susceptible to the same local environment and need to be verified to determine if it is environmental clustering interference: for example, if the waveguide paths of channels 3 and 4 are adjacent and both pass through high-temperature regions, when the signal-to-noise ratio of channel 3 is abnormal, the parameters of channel 4 are checked. If both fluctuate synchronously, it may be environmental clustering interference caused by a sudden increase in local temperature.

[0109] Step S330 is the abnormal signal triggering and determination step. When the verification results of S310 and S320 both determine that the abnormality is a non-interference factor (i.e., the abnormality exists only in the current channel and has no hardware commonality or environmental cluster characteristics), the abnormal signal is then triggered. By first eliminating common interference and then determining individual abnormalities, the logic reduces false triggering caused by hardware correlation or spatial proximity, improves the reliability of abnormal signals, and provides more accurate preconditions for subsequent fault differentiation.

[0110] As needed, step S310 specifically includes the following steps:

[0111] S311: The MEMS fiber optic switching matrix collects the real-time performance parameters of the fiber optic channel at the initial scanning frequency and calculates the instantaneous deviation between the real-time performance parameters and the reference values ​​at the corresponding time in the dynamic baseline model.

[0112] S312. If the instantaneous deviation is less than or equal to the first preset threshold, it is determined to be a normal fluctuation and no abnormal signal is triggered; if the instantaneous deviation is greater than the first preset threshold and less than or equal to the second preset threshold, the correlation verification stage is entered.

[0113] S313. In the association verification stage, extract the same source hardware association channel of the optical fiber channel, collect the real-time performance parameters of the same source hardware association channel, and calculate the same source deviation consistency. The same source deviation consistency is the ratio of the deviation amount of the optical fiber channel and the same source hardware association channel.

[0114] S314. If the consistency of the same source deviation is greater than or equal to the preset ratio, it is determined to be hardware common interference and no abnormal signal is triggered; if the consistency of the same source deviation is less than the preset ratio and the instantaneous deviation is greater than the second preset threshold, an abnormal signal is triggered.

[0115] This embodiment achieves tiered verification of common hardware interference by setting a first preset threshold (normal fluctuation threshold) and a second preset threshold (abnormal warning threshold) for tiered judgment, combined with a preset ratio for quantitative evaluation of consistency of common source deviations. This solves the problem of excessively high or low sensitivity caused by a single threshold in traditional hardware interference detection, improving the accuracy of common hardware interference identification, reducing invalid alarms while ensuring that genuine anomalies are not missed. Specifically:

[0116] In step S311 of this embodiment, the MEMS fiber optic switching matrix collects real-time performance parameters of the target fiber optic channel according to the initial scanning frequency configured in step S10 (e.g., 10Hz for core service channels, 5Hz for ordinary channels, etc., with different channels operating at differentiated frequencies). For example, a 10Hz frequency means collecting optical power, signal-to-noise ratio, and optical delay data 10 times per second, with each collection interval of 100ms; a 5Hz frequency means collecting data once every 200ms, ensuring that the density of parameter collection matches the importance level of the channel.

[0117] The acquired real-time performance parameters are instantaneous values ​​(e.g., optical power of -11.5 dBm, signal-to-noise ratio of 28.3 dB, and optical delay of 12.7 μs at a certain moment). These data are transmitted in real time to the control and management unit for processing. Simultaneously, the dynamic baseline model constructed in step S20 is invoked, and the reference values ​​that completely correspond to the current acquisition time are extracted from this model—"corresponding time" refers to the reference parameters in the dynamic baseline model that are synchronized with the real-time acquisition time (because the dynamic baseline model updates dynamically over time, the baseline reference value at 9:00:05 AM may differ from that at 9:00:10 AM, requiring precise matching of the reference at the same moment). For example, if the real-time acquisition time is 9:00:08, the corresponding baseline model has an optical power reference value of -10.2 dBm, a signal-to-noise ratio reference value of 30.1 dB, and an optical delay reference value of 12.0 μs at that moment.

[0118] The calculation method for instantaneous deviation depends on the parameter type: for linear parameters such as optical power and optical delay, the difference is used (real-time value - baseline value), for example, the optical power deviation is -11.5dBm - (-10.2dBm) = -1.3dBm; for proportional parameters such as signal-to-noise ratio, the relative deviation can be used ((real-time value - baseline value) / baseline value × 100%), for example, the signal-to-noise ratio deviation is (28.3 - 30.1) / 30.1 × 100% ≈ -5.98%. This instantaneous comparison quantifies the degree of deviation between the real-time parameter and the baseline, providing accurate numerical basis for subsequent threshold determination.

[0119] In step S312 of this embodiment, based on the instantaneous deviation calculated in step S311, a layered judgment is made through preset two-level thresholds (a first preset threshold and a second preset threshold) to achieve refined screening of parameter fluctuations, avoid falsely triggering the verification process due to slight fluctuations, and ensure that suspicious fluctuations are promptly addressed. The first preset threshold is a critical value for normal fluctuations, used to define the "normal range without intervention." Its value is set according to the parameter type and link characteristics: for example, the first preset threshold for optical power can be set to ±1dB (due to natural attenuation fluctuations in optical signals during transmission), the first preset threshold for signal-to-noise ratio can be set to ±5% (reflecting normal deviations in signal stability), and the first preset threshold for optical delay can be set to ±20ns (considering micro-delay jitter in fiber optic transmission). When the instantaneous deviation is ≤ the first preset threshold, it is determined to be a normal fluctuation in the link (e.g., the real-time optical power value differs from the baseline value by -0.8dB, which is within ±1dB and is considered normal), and no abnormal signal or verification process is triggered. The second preset threshold is the critical value for "suspicious fluctuations requiring verification," and its value is higher than the first preset threshold. It is used to define the range of "potential anomalies that require further confirmation." For example, the second preset threshold for optical power is set to ±3dB, the signal-to-noise ratio is set to ±15%, and the optical delay is set to ±100ns. When the instantaneous deviation is greater than the first preset threshold and less than or equal to the second preset threshold, it indicates that the parameter fluctuation has exceeded the normal range but has not yet reached the level of a clear anomaly, and further investigation is required in the correlation verification stage of step S313. For example:

[0120] If the instantaneous deviation of the optical power of a certain channel is -1.5dB (> the first preset threshold ±1dB, and ≤ the second preset threshold ±3dB), then proceed to the correlation verification.

[0121] If the instantaneous deviation of the signal-to-noise ratio is 8% (> the first preset threshold ±5%, and ≤ the second preset threshold ±15%), it will also proceed to the correlation verification.

[0122] If the instantaneous deviation of the optical delay is 15ns (≤ first preset threshold ± 20ns), it is determined to be a normal fluctuation and no subsequent operation is triggered.

[0123] The two-tiered threshold settings must match the parameter characteristics (e.g., optical power is more sensitive to link failures, so the threshold can be set more strictly; optical delay fluctuations are relatively slow, so the threshold can be appropriately relaxed), and both can be adjusted according to business needs (e.g., the second preset threshold for core business channels can be set lower to trigger verification earlier). Through this hierarchical judgment logic, invalid fluctuations can be filtered out, resource consumption can be reduced, and potential anomalies can be captured in a timely manner, providing accurate preconditions for subsequent interference verification.

[0124] In step S313 of this embodiment, which is the core step of the correlation verification stage, the "homogeneous hardware-related channels" are extracted and their parameter deviations are analyzed to determine whether the anomaly of the current channel originates from common hardware interference, providing a quantitative basis for subsequent interference determination. Homogeneous hardware-related channels refer to associated channels that share the same core hardware module as the current fiber channel. These hardware modules include, but are not limited to: sharing the same micromirror drive module (drive signal synchronization control), sharing the same waveguide coupler (optical signal convergence / splitting), sharing the same optical amplifier (signal gain adjustment), etc. For example, if the current channel uses drive module D5 to control the micromirror unit, then all other channels also driven by D5 (such as channel 2 and channel 7) are homogeneous hardware-related channels; if the current channel shares waveguide coupler C2 with channel 3 and channel 5, then these two channels also belong to the homogeneous association category (the above hardware modules and channel numbers are only examples).

[0125] After extracting the associated channels of the same source hardware, the MEMS fiber optic switching matrix synchronously collects the real-time performance parameters (optical power, signal-to-noise ratio, optical delay) of these associated channels at the same initial scanning frequency (e.g., 10Hz) as the current channel, and repeats the calculation logic of step S311 to obtain the instantaneous deviation of each associated channel (e.g., the optical power deviation of associated channel 2 is -2.2dB, and the deviation of associated channel 7 is -1.9dB).

[0126] Consistency of deviation from the same source is a quantitative indicator that measures the similarity of deviation characteristics between the current channel and related channels. It is obtained by calculating the ratio (absolute value) of the instantaneous deviation of the current channel to the instantaneous deviation of each related channel from the same source. For example:

[0127] The optical power deviation of the current channel is -2.0dB, and the deviation of the associated channel 2 is -1.8dB. The ratio of the two is |-2.0| / |-1.8|≈1.11;

[0128] The deviation of associated channel 7 is -2.1dB, and the ratio is |-2.0| / |-2.1|≈0.95;

[0129] The average ratio of multiple associated channels (e.g., (1.11+0.95) / 2≈1.03) is taken as the final homology deviation consistency.

[0130] The closer the ratio is to 1, the more similar the magnitude and direction of the deviation between the current channel and the associated channel (such as both being negative deviations and having similar values), and the higher the possibility of common hardware interference (such as a fault in the D5 driver module causing synchronous attenuation of optical power in all associated channels). If the ratio deviates significantly from 1 (such as a deviation of -2.0dB for the current channel and +0.5dB for the associated channel, resulting in a ratio of 4.0), it indicates that the deviation characteristics are unrelated and are more likely to be an independent anomaly of the current channel.

[0131] By extracting the associated channels of the same source hardware and calculating the deviation consistency, the abstract concept of common hardware interference is transformed into a quantifiable ratio index, providing an objective basis for the interference judgment in step S314 and avoiding the one-sidedness of relying solely on data from a single channel.

[0132] In step S314 of this embodiment, based on the consistency of the same source deviation calculated in step S313 and the threshold range in step S312, the abnormal signal is accurately triggered through dual condition judgment, avoiding misjudgment caused by hardware correlation.

[0133] The preset ratio is a threshold value for determining the consistency of deviations from the same source. It is used to define whether there is common hardware interference. Its value is set according to the tightness of the hardware correlation: for example, for channels sharing the same micromirror driver module (extremely high correlation), the preset ratio can be set to 0.8 (i.e., when the deviation consistency is ≥0.8, it is determined to be common interference); for channels sharing a waveguide coupler (medium correlation), the preset ratio can be set to 0.7. The closer this ratio is to 1, the stricter the requirements for common characteristics.

[0134] When the consistency of the source deviation is greater than or equal to a preset ratio, it is determined to be a common hardware interference—that is, the parameter abnormality of the current channel is caused by a failure of a shared hardware module (such as the aging of the driver module causing synchronous attenuation of optical power in all associated channels). In this case, the abnormality is not unique to the current channel, but a common problem of multiple associated channels. It is unnecessary to trigger an abnormal signal for a single channel (to avoid duplicate alarms), but can be resolved by subsequently checking the shared hardware module as a whole. For example:

[0135] The optical power homogeneity deviation of a certain channel is 0.9 (≥ preset ratio 0.8), and all related channels show a similar attenuation trend. It is determined to be a common interference caused by a driver module failure, and no abnormal signal is triggered.

[0136] When the consistency of the source deviation is less than the preset ratio, it indicates that the anomaly of the current channel has no obvious commonality with the associated channels (i.e., no common hardware interference). If the instantaneous deviation is greater than the second preset threshold (i.e., the parameter fluctuation has exceeded the suspicious range that needs to be verified and has reached the level of a clear anomaly), it is determined to be an individual anomaly of the current channel, and an anomaly signal must be triggered immediately. This dual condition ensures that a signal is triggered only when the anomaly is unique to the channel and of a significant degree, avoiding false triggering caused by isolated minor fluctuations.

[0137] For example: if the instantaneous deviation of optical power in a certain channel is -4dB (> the second preset threshold ±3dB) and the consistency of the same source deviation is 0.6 (< the preset ratio 0.8), it indicates that the abnormality exists only in this channel and is significant, triggering an abnormality signal.

[0138] The values ​​of the aforementioned preset ratio and the second preset threshold can be adjusted according to business needs (e.g., the preset ratio for the core business channel can be increased to 0.85 to more strictly filter common interference). By first eliminating common interference and then determining individual anomalies, this logic reduces the triggering of invalid signals and ensures timely alarms for real individual faults, providing clear guidance for subsequent fault location.

[0139] As needed, step S320 specifically includes the following steps:

[0140] S321. Compare the real-time performance parameters of the fiber optic channel with the corresponding dynamic reference range. If the performance parameters exceed the dynamic reference range, mark the fiber optic channel as a suspected abnormal channel.

[0141] S322. Extract the associated channel group of the suspected abnormal channel, wherein the associated channel group is an optical fiber channel that is spatially associated with the suspected abnormal channel;

[0142] S323. Calculate the synchronization fluctuation coefficient of the performance parameters in the associated channel group, wherein the synchronization fluctuation coefficient is the cosine similarity of the performance parameter changes of the suspected abnormal channel and each optical fiber channel in the associated channel group.

[0143] S324. If the synchronization fluctuation coefficient is greater than or equal to the third preset threshold, it is determined to be cluster fluctuation caused by environmental interference, and the mark of the suspected abnormal channel is removed; if the synchronization fluctuation coefficient is less than the third preset threshold, it is determined to be an independent abnormality of the suspected abnormal channel, and an abnormal signal is triggered.

[0144] This embodiment marks suspected anomalies by comparing real-time performance parameters with a dynamic benchmark range and introduces a third preset threshold to quantify the synchronization fluctuation coefficient (cosine similarity), thereby achieving accurate identification of environmental cluster interference. This solves the problem that traditional environmental interference detection struggles to distinguish between cluster fluctuations and independent faults, improving the accuracy of anomaly detection in environmentally correlated scenarios and providing more reliable preliminary judgments for subsequent fault location.

[0145] Specifically:

[0146] In step S321 of this embodiment, by comparing the real-time performance parameters with the dynamic benchmark range, abnormal channels that may be affected by the environment are initially identified, providing objects for subsequent correlation verification.

[0147] First, the core object of comparison is the real-time performance parameters of the optical fiber channel and the corresponding dynamic reference range: the real-time performance parameters are the instantaneous values ​​collected by the MEMS optical fiber switching matrix at the initial scanning frequency (such as optical power -11dBm, signal-to-noise ratio 29dB, and optical delay 18ns at a certain moment), covering key indicators such as optical power, signal-to-noise ratio, and optical delay.

[0148] The dynamic reference range is the core content of the real-time dynamic baseline model constructed in step S20 (such as the range of optical power "long-term trend curve ± inherent fluctuation threshold + environmental interference correction coefficient", the range of signal-to-noise ratio "historical fluctuation range + real-time environmental correction", etc.), and its value is dynamically updated with time and environment (for example, at a certain moment, the dynamic reference range of optical power is -9dBm to -12dBm, the signal-to-noise ratio is 30dB to 35dB, and the optical delay is 15ns to 25ns).

[0149] The comparison logic is as follows: each real-time parameter is checked against the dynamic benchmark range of the corresponding indicator. If the real-time value is between the upper and lower limits of the benchmark range (e.g., optical power -10dBm is in the range of -9dBm to -12dBm), the parameter is judged to be normal. If the real-time value exceeds the upper limit or is lower than the lower limit (i.e., "out of bounds"), the channel is marked as a suspected abnormal channel.

[0150] Specific examples:

[0151] The real-time optical power value is -8.5dBm, and the upper limit of the dynamic reference range is -9dBm. Since 8.5dBm > -9dBm (exceeding the upper limit), it is marked as a suspected anomaly.

[0152] The real-time signal-to-noise ratio is 28dB, and the lower limit of the dynamic reference range is 30dB. Since 28dB < 30dB (below the lower limit), it is marked as a suspected anomaly.

[0153] The real-time optical delay value is 20ns, and the dynamic reference range is 15ns to 25ns (within the range), which is considered normal and will not be marked.

[0154] Marking a channel as potentially abnormal only indicates that the channel parameters are currently outside the normal fluctuation range. Further verification through steps S322-S324 is needed to confirm whether it is due to environmental cluster interference, rather than directly classifying it as a fault. This design avoids triggering abnormal signals directly due to a single parameter exceeding the limit, reducing misjudgments caused by environmental factors. The dynamic characteristics of the dynamic reference range (such as synchronously adjusting the upper limit of optical power as temperature rises) also ensure the accuracy of the comparison, making the determination of out-of-range conditions more consistent with the actual link status.

[0155] In step S322 of this embodiment, the core of step S322 is to filter out the associated channels that may be affected by the same environmental factors as the suspected abnormal channels through spatial location correlation, so as to provide a comparison object for subsequent judgment on whether it is environmental cluster interference. The key is to clarify the specific connotation of spatial location correlation and extraction logic.

[0156] Spatial location correlation refers to the proximity of fiber optic channels in their physical layout or the sharing of physical components, making them susceptible to interference from the same local environment (such as temperature, vibration, electromagnetic radiation, etc.). Specifically, this includes two scenarios:

[0157] Sharing the same micromirror unit or micromirror array area: In a MEMS fiber optic switching matrix, multiple fiber optic channels may achieve optical path redirection through the same micromirror unit, or multiple micromirror units may be densely distributed in the same area (e.g., 1). Within the range of micromirror units, the optical transmission paths of these channels highly overlap, exhibiting strong consistency in response to local environmental influences. For example, channels 4, 5, and 6 all achieve optical path switching through micromirror unit group M3 (physically located in region A of the matrix), and the three are spatially related.

[0158] Adjacent waveguide paths with a spacing less than a preset safety distance: The waveguide path is the physical channel for optical signal transmission. If the spacing between two waveguides is less than the preset safety distance (e.g., 5mm, which can be adjusted according to anti-interference requirements), they may be affected by shared environment factors such as heat conduction and vibration conduction. For example, the waveguide path of channel 9 is parallel to the waveguide path of channel 10 and the spacing is 3mm (<5mm safety distance), and the two are spatially related.

[0159] When extracting associated channel groups, the system queries the physical topology database of fiber optic channels (which pre-stores information such as micromirror unit affiliation, waveguide path coordinates, and spacing for each channel) to automatically match all channels that meet the above spatial location association conditions, forming associated channel groups. For example:

[0160] If the suspected abnormal channel is channel 4 (belonging to micromirror unit group M3), then the associated channel group includes channels 5 and 6, which also belong to M3.

[0161] If the suspected abnormal channel is channel 9 (waveguide spacing 3mm, adjacent to channel 10), then the associated channel group includes channel 10 and other channels 11 with spacing <5mm.

[0162] The purpose of extracting associated channel groups is to determine whether the anomaly is a cluster effect caused by local environmental factors (such as a sudden temperature rise in the same area causing synchronous anomalies in all associated channels) by comparing the parameter fluctuation characteristics of suspected abnormal channels with those of spatially associated channels. This step provides a comparative sample for the calculation of the synchronization fluctuation coefficient in step S323 and is a key prerequisite for distinguishing between environmental cluster interference and individual link failures.

[0163] In step S323 of this embodiment, the consistency of parameter changes between suspected abnormal channels and associated channel groups is quantified by calculating the synchronization fluctuation coefficient, providing a core basis for determining whether there is environmental cluster interference. The core is to use the mathematical index of cosine similarity to measure the synchronicity of the changing trend.

[0164] First, determine the performance parameter changes: Select the same time period (e.g., the most recent 10 seconds, or one scan cycle) corresponding to the time when the suspected anomaly was marked in step S321, and calculate the performance parameter changes of each channel in the suspected anomaly channel and related channel group—that is, the difference between the endpoint and starting values ​​of the parameters within that time period. For example:

[0165] The optical power of the suspected abnormal channel A dropped from -10dBm to -13dBm within 10 seconds, a change of -3dB.

[0166] The variation in channel B within the associated channel group was -2.8 dB, the variation in channel C was -3.2 dB, and the variation in channel D was -0.5 dB (due to the greater distance, it was less affected by the environment).

[0167] Then, these changes are converted into vector form: the changes of suspected abnormal channels constitute a vector (e.g., [-3dB], which is a multi-dimensional vector if signal-to-noise ratio and optical delay are considered at the same time), and the changes of each associated channel constitute a corresponding comparison vector (e.g., [-2.8dB] for channel B and [-3.2dB] for channel C).

[0168] Cosine similarity is used to calculate the cosine of the angle between two vectors, with a numerical range of [-1, 1]. In this step, since the direction of parameter change (such as synchronous decay or synchronous increase) is more meaningful, the [0, 1] interval is actually of interest: if the cosine similarity is close to 1, it indicates that the two vectors have almost the same direction (synchronous change trend); if it is close to 0, it indicates that the change trends are unrelated. The specific calculation formula is as follows:

[0169] Cosine similarity = (vector A • vector B) / (|vector A| × |vector B|);

[0170] Where “・” represents the vector dot product, and |vector A| and |vector B| represent the magnitude of the vectors (i.e., the absolute value of the change).

[0171] For example, regarding changes in optical power:

[0172] The vector of the suspected abnormal channel A is [-3], and the vector of the associated channel B is [-2.8]; the dot product = (-3) × (-2.8) = 8.4;

[0173] |Vector A|=3, |Vector B|=2.8; Cosine similarity=8.4 / (3×2.8)=8.4 / 8.4=1 (completely synchronized); If the vector of associated channel D is [-0.5];

[0174] Cosine similarity = [(-3)×(-0.5)] / (3×0.5) = 1.5 / 1.5 = 1 (They may be in the same direction, but with large differences in amplitude, they may still be synchronized).

[0175] When an associated channel group contains multiple channels, the cosine similarity between the suspected abnormal channel and each associated channel is calculated, and the average value is taken as the final synchronization fluctuation coefficient. For example, if the cosine similarity between channel A and B, C, and D are 1, 0.98, and 0.8 respectively, then the synchronization fluctuation coefficient is (1+0.98+0.8) / 3≈0.93, indicating that the overall synchronization is relatively high.

[0176] The closer the coefficient is to 1, the more consistent the direction and magnitude of parameter changes between the suspected abnormal channel and the associated channel group, and the higher the probability that they are affected by the same environmental factor (such as a sudden increase in local temperature). If the coefficient is close to 0 (e.g., the change in the suspected abnormal channel is -3dB, the change in the associated channel is +1dB, and the cosine similarity is -0.7), it indicates that the change trends are unrelated, and it is more likely to be an individual fault. Through this quantitative calculation, the qualitative judgment of environmental cluster interference is transformed into a measurable numerical indicator, improving the objectivity and accuracy of the judgment.

[0177] In step S324 of this embodiment, the synchronization fluctuation coefficient calculated in step S323 is compared with the third preset threshold. The determination of whether to retain the suspected abnormality mark or trigger the abnormal signal is made through the explicit threshold conditions. The core is to distinguish between common environmental interference and channel-specific problems through quantitative indicators.

[0178] The third preset threshold is the critical value for determining the synchronization fluctuation coefficient, used to define whether environmental cluster interference exists. Its value is set according to the spatial correlation of the associated channel groups: for example, for channel groups sharing the same micromirror unit (extremely close spatial correlation), the third preset threshold can be set to 0.85 (i.e., when the synchronization fluctuation coefficient ≥ 0.85, it is determined to be cluster interference); for channel groups with only adjacent waveguide paths (moderate correlation), the threshold can be set to 0.75, allowing for a certain degree of fluctuation difference. The higher the threshold, the more stringent the requirement for synchronization, and the more accurately it can filter out anomalies caused by non-environmental factors.

[0179] When the synchronization fluctuation coefficient is greater than or equal to the third preset threshold, it is determined to be cluster fluctuation caused by environmental interference—that is, the parameter out-of-bounds error of the suspected abnormal channel is caused by local environmental factors (such as sudden temperature rise, enhanced vibration, electromagnetic interference, etc.), and this environmental factor also affects other channels in the associated channel group (manifested as synchronized parameter changes). In this case, the anomaly is not an individual channel fault, but a common manifestation of environmental interference. Therefore, the suspected anomaly flag for this channel is removed, and there is no need to trigger an anomaly signal (to avoid repeated alarms for environmental interference). For example:

[0180] The synchronization fluctuation coefficient of suspected abnormal channel A is 0.9 (≥ third preset threshold 0.85), and most channels in the associated channel group show a similar parameter decreasing trend. It is determined to be environmental cluster interference (such as local temperature rise causing synchronous attenuation of optical power), and the suspected label of A is removed.

[0181] When the synchronization fluctuation coefficient is less than the third preset threshold, it indicates that the parameter changes of the suspected abnormal channel are not significantly synchronized with the associated channel group (i.e., not due to environmental cluster interference). In this case, it is determined to be an independent anomaly of that channel—the anomaly originates from the channel's own hardware or link problems (such as micromirror deflection errors, fiber optic connector losses, etc.), and an anomaly signal must be triggered immediately. For example:

[0182] The synchronization fluctuation coefficient of the suspected abnormal channel B is 0.6 (< the third preset threshold of 0.75), and the parameters of the associated channel group are all within the normal range, indicating that the abnormality of B is an individual problem, triggering an abnormal signal to start subsequent troubleshooting.

[0183] The aforementioned third preset threshold can be adjusted according to the business scenario (e.g., the threshold for core area channels can be increased to 0.9 to more strictly identify environmental interference). This logic, which categorizes synchronous compliance as environmental interference and non-compliance as individual anomalies, avoids false alarms caused by environmental factors while ensuring timely response to real individual faults, providing a clear direction for subsequent fault handling.

[0184] In one embodiment, step S40 includes the following steps:

[0185] S410. Locate the target micromirror unit group corresponding to the original path based on the abnormal signal, and call the redundant micromirror unit group that is physically isolated from the original micromirror unit group to construct a redundant verification path.

[0186] S420, synchronously acquire the optical power attenuation rate and micromirror deflection error of the original path and redundant verification path;

[0187] S430. Calculate the path difference between the acquired optical power attenuation rate and the micromirror deflection error, respectively:

[0188] Optical power attenuation rate difference = |original path attenuation rate - redundant path attenuation rate| / original path attenuation rate;

[0189] Micromirror deflection error difference = |Original path error - Redundant path error| / Original path error;

[0190] S440. If the difference in optical power attenuation rate is less than or equal to the preset attenuation threshold, and the difference in micromirror deflection error is greater than the preset error threshold, then the link itself is determined to be faulty; if the difference in optical power attenuation rate is greater than the preset attenuation threshold, and the difference in micromirror deflection error is less than or equal to the preset error threshold, then the environmental interference is determined to be abnormal.

[0191] In step S410 of this embodiment, the independence of the two paths is ensured by accurately locating the hardware of the original path and calling the physically isolated redundant resources, thus providing a reliable benchmark for subsequent parameter comparison.

[0192] The abnormal signal contains identification information of the original path (such as channel number and route ID). The control and management unit uses this information to locate the corresponding target micromirror unit group—that is, a group of micromirror units in the original path responsible for optical path redirection (such as an array composed of M1-M5, responsible for optical path conduction from input port IN3 to output port OUT7). The location process is achieved by querying a pre-stored "channel-micromirror unit mapping table" to ensure accurate association.

[0193] Physical isolation is a core feature of redundant micromirror unit groups, specifically referring to:

[0194] Independent drive circuits: The redundant unit group uses a drive circuit that is completely separate from the target unit group (e.g., the target group uses drive module D1, and the redundant group uses D2) to avoid common errors caused by circuit failures.

[0195] Optical path isolation: The optical path transmission path of the redundant unit group does not overlap with the original path (e.g., the original path is in region A of the matrix, and the redundant path is in region B), reducing the synchronization impact of environmental interference.

[0196] Heat dissipation area separation: If the micromirror unit generates significant heat, the redundant group and the target group belong to different heat dissipation areas to avoid performance correlation caused by temperature conduction.

[0197] After calling the redundant unit group, its micromirror units are reset to a preset angle matching the original path (e.g., if the original path M1 deflects by 30°, the corresponding redundant unit M6 also deflects by 30°), thus constructing a redundant verification path with the same function as the original path. For example, if the original path is IN3→M1-M5→OUT7, the redundant path is IN3→M6-M10→OUT7, ensuring that the input / output ports of the two paths are the same, but the hardware carriers are completely independent (the above numbering and angles are only examples).

[0198] In step S420 of this embodiment, the core parameters of the two paths are collected synchronously to provide raw data for the difference calculation. The key lies in the synchronization and the accurate definition of the parameters.

[0199] Synchronous acquisition mechanism: The control and management unit sends a synchronization trigger command to the MEMS fiber optic switching matrix to ensure that the parameter acquisition of the original path and the redundant path starts at the same time (e.g., accurate to the microsecond level), avoiding the impact of environmental fluctuations caused by time differences (e.g., the asynchronous interference of instantaneous temperature changes on the two paths). The acquisition frequency is consistent with the scanning frequency of the original path (e.g., 10Hz), and the interval between each acquisition is 100ms.

[0200] Parameter definition and acquisition method:

[0201] Optical power attenuation rate: refers to the proportion of power loss of optical signal from input port to output port. The calculation formula is "(input optical power - output optical power) / input optical power × 100%", which is collected in real time by optical power sensor at the port (e.g., input power -10dBm, output power -12dBm, attenuation rate is 20%).

[0202] Micromirror deflection error: refers to the difference between the actual deflection angle of the micromirror and the theoretical command angle (e.g., if the command angle is 30° and the actual angle is 29.8°, the error is 0.2°). It is measured in real time by the angle feedback sensor (such as a Hall sensor) built into the micromirror, with an accuracy of up to 0.01°.

[0203] For example: the optical power attenuation rate of the original path at a certain moment is 5.2%, and the micromirror deflection error is 0.12°; the attenuation rate of the redundant path acquired synchronously is 5.0%, and the error is 0.03°. The two sets of data are perfectly aligned in timestamps (the above values ​​are just examples).

[0204] In step S430 of this embodiment, the degree of parameter deviation between the two paths is quantified by calculating the relative difference, which is more reflective of the relativity of the fault than the absolute difference (e.g., a small absolute difference with a small attenuation rate may represent a big problem).

[0205] The formula for calculating the difference in optical power attenuation rate is "|original path attenuation rate - redundant path attenuation rate| / original path attenuation rate". Its physical meaning is: the ratio of the difference in attenuation rate between the redundant path and the original path to the attenuation rate of the original path, used to measure the relative consistency of power loss between the two paths. For example:

[0206] The original path has an attenuation rate of 5%, and the redundant path has an attenuation rate of 4.8%. Therefore, the difference is |5%-4.8%| / 5%=0.04 (i.e. 4%), indicating that the attenuation characteristics of the two paths are highly similar.

[0207] If the original path attenuation rate is 5% and the redundant path is 8%, the difference is |5%-8%| / 5%=0.6 (i.e. 60%), indicating that the attenuation characteristics are significantly different.

[0208] The formula for calculating the difference in micromirror deflection error is "|Original path error - Redundant path error| / Original path error", reflecting the difference in deflection accuracy between the two paths of hardware (micromirrors). For example:

[0209] The original path error is 0.1°, the redundant path error is 0.02°, and the difference is |0.1-0.02| / 0.1=0.8 (i.e. 80%), indicating that the micromirror accuracy of the original path is much lower than that of the redundant path.

[0210] If the original path error is 0.1° and the redundant path error is 0.09°, the difference is 0.1 (i.e., 10%), indicating that the accuracy of the two paths is close.

[0211] The above formula eliminates the influence of the magnitude of the parameters themselves by using relative values ​​rather than absolute values ​​(e.g., a 0.2° error is a big problem when the original path error is 0.1°, but a small problem when the original path error is 1°), making the differences more comparable.

[0212] In step S440 of this embodiment, the fault type is accurately distinguished by a combination of two differences and a preset threshold. The threshold setting needs to be combined with hardware characteristics and environmental interference patterns.

[0213] A preset attenuation threshold (e.g., 0.1, or 10%) is used to define the similarity of optical power attenuation characteristics: when the difference in attenuation rate is ≤0.1, it indicates that the power loss patterns of the two paths are similar (e.g., both are slightly affected by the environment); when it is >0.1, it indicates that the attenuation characteristics are significantly different (e.g., the original path has a sudden increase in attenuation rate due to excessive connector loss, while the redundant path is normal).

[0214] A preset error threshold (e.g., 0.5, i.e., 50%) is used to define the difference in micromirror deflection accuracy: when the difference in error is >0.5, it indicates that the accuracy of the original path micromirror is much lower than that of the redundant path (i.e., there is an anomaly in the hardware of the original path); when it is ≤0.5, it indicates that the accuracy of the two is close (there is no significant difference in hardware).

[0215] The specific scenarios for the two decision logics are as follows:

[0216] Link-specific fault: When the attenuation rate difference is less than or equal to the preset attenuation threshold (the attenuation characteristics of the two paths are similar, excluding attenuation differences caused by the environment) and the error difference is greater than the preset error threshold (the original path micromirror accuracy is abnormal), it is determined to be a hardware problem of the original path (such as micromirror aging, deflection mechanism jamming). For example:

[0217] The attenuation rate difference is 0.04 (≤0.1), and the error difference is 0.8 (>0.5), indicating that the original path micromirror error is significant and is determined to be a link failure.

[0218] Abnormal environmental interference: When the difference in attenuation rate is greater than the preset attenuation threshold (the attenuation characteristics of the two paths are very different, and the original path is affected by additional environment) and the difference in error is less than or equal to the preset error threshold (the micromirror precision is not significantly different), it is determined to be caused by environmental factors (such as a sudden increase in temperature or enhanced vibration near the original path).

[0219] For example, if the attenuation rate difference is 0.6 (>0.1) and the error difference is 0.1 (≤0.5), it indicates that the abnormal attenuation of the original path is caused by environmental interference, and it is determined to be an abnormal environmental interference.

[0220] The above thresholds can be adjusted according to the actual scenario (e.g., the preset attenuation threshold can be relaxed to 0.15 under high temperature environment). Through strict matching of dual conditions, the accuracy of fault type determination is ensured, providing a clear basis for subsequent frequency adjustment (step S50).

[0221] In one embodiment, step S50 includes the following steps:

[0222] S510. For fiber optic channels with inherent link failures, obtain the cumulative operating time and historical deflection count of the micromirror unit corresponding to the fiber optic channel to calculate the hardware fatigue coefficient.

[0223] S520, the frequency boost level is determined based on the hardware fatigue coefficient, specifically as follows:

[0224] If the hardware fatigue coefficient is less than or equal to the first coefficient threshold, the scanning frequency will be increased to the first multiple of the initial scanning frequency.

[0225] If the hardware fatigue coefficient is greater than the first coefficient threshold but less than or equal to the second coefficient threshold, the scanning frequency will be increased to a second multiple of the initial scanning frequency.

[0226] If the hardware fatigue coefficient is greater than the second coefficient threshold, the scanning frequency will be increased to the third multiple of the initial scanning frequency, and the duration of a single high-frequency scan will not exceed the preset safe duration.

[0227] S530. For fiber optic channels with abnormal environmental interference, monitor the fluctuation range of environmental parameters in the associated area of ​​the fiber optic channel. If the fluctuation range is less than or equal to the environmental stability threshold, maintain the initial scanning frequency. If the fluctuation range is greater than the environmental stability threshold, perform a short-term high-frequency scan at a preset interval based on the initial scanning frequency.

[0228] S540: Real-time statistical analysis of abnormal feature density in high-frequency scanning data. When the density is continuously below a stable threshold for a preset duration, it automatically reverts to the initial scanning frequency.

[0229] In step S510 of this embodiment, the hardware fatigue coefficient is calculated by quantifying the aging degree of the micromirror unit, providing a hardware status basis for subsequent frequency adjustment. The core is to construct a fatigue model by combining key loss parameters.

[0230] Parameter acquisition details:

[0231] Cumulative operating time: Recorded by the built-in timer of the MEMS fiber optic switching matrix, accurate to the hour (e.g., a micromirror unit has accumulated 8760 hours of operation since its commissioning). This parameter reflects the material aging caused by long-term operation (e.g., wear of the micromirror coating, aging of the drive circuit).

[0232] Historical deflection count: Statistically recorded through the action log of the drive circuit, including the count of each micromirror angle adjustment (e.g., cumulative deflection of 250,000 times). This parameter reflects mechanical fatigue (e.g., hinge wear, motor wear).

[0233] Formula for calculating hardware fatigue coefficient:

[0234] A weighted summation model is used, with weights assigned based on the degree of impact of parameters on hardware lifespan (cumulative operating time has a greater impact on chronic aging, hence its higher weight):

[0235] Hardware fatigue coefficient = 0.6 × (cumulative working time / design life) + 0.4 × (historical deflection count / design deflection count).

[0236] The design life is typically the rated operating time of the micromirror unit (e.g., 10,000 hours), and the design deflection number is the rated number of mechanical actions (e.g., 300,000 times).

[0237] For example: If a micromirror unit has accumulated 5000 hours of operation (5000 / 10000=0.5) and 180,000 historical deflections (18 / 30=0.6), then the fatigue coefficient = 0.6×0.5+0.4×0.6=0.3+0.24=0.54 (moderate fatigue state).

[0238] The coefficient ranges from [0,1]. The closer it is to 1, the more severe the hardware aging is, which directly affects the conservatism of subsequent frequency increases (the above design parameters are just examples and can be adjusted according to the hardware model).

[0239] In step S520 of this embodiment, a scanning frequency enhancement strategy is determined based on the hardware fatigue coefficient. The core is to balance the high-frequency monitoring accuracy with hardware life protection, and to avoid accelerated damage to high-fatigue hardware due to overuse.

[0240] Coefficient threshold and multiplier setting logic:

[0241] The first coefficient threshold (e.g., 0.3) corresponds to a low fatigue state (stable hardware performance, capable of withstanding high-frequency scanning). The first multiplier value is set to 3 times (e.g., initial frequency 5Hz → 15Hz). Because the mechanical structure and circuit redundancy of the low fatigue hardware are sufficient, high-frequency operation has little impact on lifespan.

[0242] The second coefficient threshold (e.g., 0.6) corresponds to a moderate fatigue state (slight aging of hardware, requiring a moderate reduction in frequency load). The second multiplier value is set to 2 times (5Hz→10Hz) to reduce the number of actions and slow down aging.

[0243] Hardware fatigue coefficient > second coefficient threshold: corresponds to high fatigue state (hardware nearing the end of its lifespan, with obvious mechanical wear), the third multiplier value is set to 1.5 times (5Hz→7.5Hz), and the duration of a single high-frequency scan is ≤ preset safe duration (e.g., 5 minutes) — this duration is determined based on the reliability test of high fatigue hardware (e.g., if continuous high-frequency operation exceeds 5 minutes, the probability of failure increases sharply), and the scan interval must be ≥10 minutes (to allow for cooling / recovery time).

[0244] For example: The initial frequency of the core business channel is 10Hz, corresponding to a micromirror unit fatigue coefficient of 0.2 (≤0.3), which is increased to 30Hz (3 times) without duration limit;

[0245] The initial frequency of the normal channel is 5Hz, with a coefficient of 0.7 (>0.6), which is then increased to 7.5Hz. Each frequency lasts for ≤5 minutes, with an interval of 15 minutes.

[0246] The threshold and multiplier can be adjusted according to the importance of the business (e.g., the first threshold of the core channel can be relaxed to 0.4, allowing a higher frequency to be maintained under higher fatigue conditions).

[0247] In step S530 of this embodiment, for channels with abnormal environmental interference, the scanning frequency is dynamically adjusted based on fluctuations in environmental parameters. The core is on-demand high-frequency monitoring—the frequency is only temporarily increased when the environment is unstable, thus avoiding resource waste.

[0248] Environmental parameter monitoring details:

[0249] Associated area: refers to the area where the physical routing of the fiber optic channel is located (such as the upper layer of cabinet A, the northeast corner of computer room B), where temperature sensors (accuracy ±0.5℃), vibration sensors (accuracy ±0.1μm), and humidity sensors (accuracy ±2%RH) are deployed.

[0250] Fluctuation amplitude calculation: Take the difference between the maximum and minimum values ​​of the parameter within 5 consecutive minutes (e.g., if the temperature rises from 25℃ to 29℃, the fluctuation amplitude = 4℃).

[0251] Threshold and frequency adjustment rules:

[0252] Environmental stability threshold: set based on the normal fluctuation range of historical environmental data (e.g., temperature ±2℃, vibration ±1μm) — this range corresponds to the safe range in which environmental interference does not affect the channel performance.

[0253] If the fluctuation range is less than or equal to the stability threshold (e.g., temperature fluctuation of 1.5℃): maintain the initial scanning frequency (e.g., 3Hz). Since the parameter fluctuation is small when the environment is stable, high-frequency monitoring is not required.

[0254] If the fluctuation amplitude is greater than the stability threshold (e.g., vibration amplitude 2μm): based on the initial frequency, perform a short high-frequency scan at a preset interval (e.g., 30 minutes) - the period and duration are set based on the persistence characteristics of environmental interference (e.g., most environmental fluctuations last within 30 minutes, and 1 minute of high frequency is sufficient to capture the anomaly).

[0255] For example: the temperature fluctuation in a certain channel-related area reaches ±3℃ (>±2℃). The initial scanning frequency is 4Hz, and a 1-minute 20Hz (5 times) scan is started every 30 minutes to collect the instantaneous fluctuations of optical power and signal-to-noise ratio. The monitoring frequency is still 4Hz at other times.

[0256] In step S540 of this embodiment, the determination of whether to return to the initial frequency is based on the abnormal feature density. The core is to automatically terminate excessive monitoring and ensure efficient use of resources.

[0257] Anomaly feature density statistical methods:

[0258] Definition: The number of times a performance parameter exceeds the dynamic reference range within a unit of time (e.g., per minute) (e.g., optical power exceeding the limit, signal-to-noise ratio abnormality), with a statistical period of 1 minute (density value updated every 60 seconds).

[0259] For example: During high-frequency scanning, the optical power exceeds the limit 3 times within 1 minute, the signal-to-noise ratio is abnormal 2 times, and the total abnormal feature density is 5 times / minute.

[0260] Callback determination condition:

[0261] Stability threshold: Based on the frequency of anomalies under normal conditions (e.g., 0.1 times / minute, i.e., ≤1 anomaly every 10 minutes), this value ensures that the fault / interference has been substantially reduced.

[0262] Preset duration: such as 1 hour - within 60 minutes, the density of abnormal features per minute must be ≤0.1 times / minute (to avoid false callbacks caused by brief periods of stability).

[0263] For example: When the link itself is faulty, the high-frequency scan (30Hz) reduces the anomaly density from 5 times / minute to 0.05 times / minute and maintains this level for 1 hour, triggering a callback to the initial scan frequency of 10Hz;

[0264] In the short-time high-frequency scan of the environmental interference channel, the abnormal density of each scan within one hour is ≤0.1 times / minute. After that, the short-time high frequency is stopped, and only the initial scan frequency of 4Hz is maintained.

[0265] This mechanism enables closed-loop control that achieves high-frequency monitoring when a fault is active and automatic frequency reduction after the fault subsides, balancing monitoring accuracy and resource efficiency.

[0266] In one embodiment, a monitoring system based on a MEMS fiber optic switching matrix is ​​provided, which corresponds to the monitoring method based on a MEMS fiber optic switching matrix described in the above embodiments. The monitoring system based on a MEMS fiber optic switching matrix includes:

[0267] The path mapping module is used to control the management unit to send control commands to the MEMS fiber optic switching matrix, complete the initial path mapping of multiple fiber optic channels, and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel.

[0268] The baseline model construction module is used to poll each optical fiber channel through the MEMS optical fiber switching matrix, collect performance parameters within a preset time period, and construct a real-time dynamic baseline model for each optical fiber channel based on the sliding window algorithm; wherein, the performance parameters include at least optical power, signal-to-noise ratio and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters.

[0269] The parameter acquisition and comparison module is used to acquire real-time performance parameters of each fiber channel of the MEMS fiber switching matrix at the initial scanning frequency, compare the real-time acquired performance parameters with the dynamic baseline model, and trigger an abnormal signal when the parameter deviation exceeds the preset threshold.

[0270] The fault differentiation module is used to respond to abnormal signals and control the management unit to switch the MEMS fiber optic switching matrix to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it can distinguish between environmental interference abnormalities and link faults.

[0271] The scanning frequency adjustment module is used to adjust the scanning frequency of the corresponding fiber optic channel according to the fault type. It increases the scanning frequency of fiber optic channels with link faults to obtain high-frequency scanning data, and maintains the initial scanning frequency of fiber optic channels with abnormal environmental interference.

[0272] The assessment and early warning module is used to generate fiber optic link status assessment reports and issue corresponding early warning information based on fault type and high-frequency scanning data.

[0273] For specific limitations regarding the monitoring system based on a MEMS fiber optic switching matrix, please refer to the limitations of the monitoring method based on a MEMS fiber optic switching matrix mentioned above, which will not be repeated here. Each module in the aforementioned monitoring system based on a MEMS fiber optic switching matrix can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0274] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database is used for data storage, data processing, and data analysis. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a monitoring method based on a MEMS fiber optic switching matrix.

[0275] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a monitoring method based on a MEMS fiber optic switching matrix.

[0276] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a monitoring method based on a MEMS fiber optic switching matrix.

[0277] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0278] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0279] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A monitoring method based on a MEMS fiber optic switching matrix, characterized in that, Includes the following steps: The control and management unit sends control commands to the MEMS fiber optic switching matrix to complete the initial path mapping of multiple fiber optic channels and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel. The MEMS fiber optic switching matrix polls each fiber optic channel to collect performance parameters within a preset time period, and constructs a real-time dynamic baseline model for each fiber optic channel based on a sliding window algorithm. The performance parameters include at least optical power, signal-to-noise ratio, and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters. The MEMS fiber optic switching matrix collects real-time performance parameters of each fiber optic channel at an initial scan frequency. These parameters are then compared with a dynamic baseline model. An anomaly signal is triggered when the parameter deviation exceeds a preset threshold. Prior to triggering the anomaly signal, the following verification steps are also included: If the fiber channel has hardware functional association with other channels, then the verification for common hardware interference is performed first. The hardware functional association means that the fiber channel shares the same micromirror driving module or waveguide coupling region. If the fiber channel is spatially associated with other channels, then verification against environmental cluster interference is performed first. The spatial association refers to the fiber channel sharing the same micromirror unit or adjacent waveguide paths. When the above verification determines that it is a non-interference factor, then the abnormal signal is triggered. In response to an abnormal signal, the control and management unit controls the MEMS fiber optic switching matrix to switch to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it distinguishes between abnormal environmental interference and link failure. Adjust the scanning frequency of the corresponding fiber channel according to the fault type. Increase the scanning frequency of the fiber channel with the link itself fault to obtain high-frequency scanning data, and maintain the initial scanning frequency of the fiber channel with abnormal environmental interference. Based on the fault type and high-frequency scanning data, an optical fiber link status assessment report is generated and corresponding early warning information is issued.

2. The monitoring method based on a MEMS fiber optic switching matrix as described in claim 1, characterized in that, In the step of the control management unit sending control commands to the MEMS fiber optic switching matrix to complete the initial path mapping of multiple fiber optic channels and configuring the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel, the initial path mapping of multiple fiber optic channels includes the following steps: The control and management unit sends control commands to the MEMS fiber optic switching matrix, which include the input port number, output port number, and mapping priority of each fiber optic channel. According to the control command, the MEMS fiber optic switching matrix drives the internal micromirror array or waveguide switch to reset to the preset position, establishes the corresponding optical link connection between the input port and the output port, and completes the initial path mapping of multiple fiber optic channels. The mapping priority is used to determine the path establishment order when there is a channel resource conflict.

3. The monitoring method based on a MEMS fiber optic switching matrix as described in claim 2, characterized in that, In the step of the control management unit sending control commands containing the input port number, output port number, and mapping priority of each fiber channel to the MEMS fiber optic switching matrix, the determination of the mapping priority includes the following steps: Obtain the preset attribute parameters of each fiber channel, including the real-time requirements of the channel-carrying services, the data transmission rate threshold, the historical failure frequency, and the hierarchical weight of the link where the channel is located; The priority score for each channel is calculated by weighting the preset attribute parameters according to their respective weights; among them, the weights for real-time requirements are ≥30%, data transmission rate thresholds are ≥25%, historical fault occurrence frequency is ≤20%, and link level is ≤25%. Based on the priority score, the mapping priority is divided into three levels: a score greater than or equal to the first preset score is high priority, a score within the preset range of the second preset score is medium priority, and a score less than or equal to the third preset score is low priority. Among them, the high priority channel will be established first when there is a resource conflict.

4. The monitoring method based on a MEMS fiber optic switching matrix as described in claim 1, characterized in that, Determining the dynamic reference range includes the following steps: Based on the preset duration, a dual-window hierarchy is divided, including a historical feature sliding window and a real-time fluctuation sliding window. The window duration of the historical feature sliding window is 2 / 3 of the preset duration, and the window duration of the real-time fluctuation sliding window is 1 / 3 of the preset duration. Within the historical feature sliding window, the MEMS fiber optic switching matrix is ​​used to switch to the redundant verification path to collect the performance parameters of the fiber optic channel under interference-free conditions, and calculate the long-term trend curve of its performance parameters and the inherent fluctuation threshold. Within the real-time fluctuation sliding window, performance parameter data is extracted according to a preset sliding step size, and the environmental interference correction coefficient is calculated in combination with the current environmental monitoring data. The sliding step size is 1 / 5 of the window duration of the real-time fluctuation sliding window. The long-term trend curve of the historical feature sliding window is used as the baseline reference, and the inherent fluctuation threshold of the historical feature sliding window and the environmental interference correction coefficient of the real-time fluctuation sliding window are superimposed to form a dynamic reference range. Based on the dynamic reference range, the real-time dynamic baseline model of the optical fiber channel is obtained.

5. The monitoring method based on a MEMS fiber optic switching matrix as described in claim 1, characterized in that, The step of responding to an abnormal signal by controlling the MEMS fiber optic switching matrix to switch to the corresponding redundant verification path, and distinguishing between environmental interference anomalies and link faults by comparing and analyzing the performance parameters of the original path and the redundant verification path, includes the following steps: Based on the abnormal signal, locate the target micromirror unit group corresponding to the original path, and call the redundant micromirror unit group that is physically isolated from the original micromirror unit group to construct a redundant verification path; Simultaneously collect the optical power attenuation rate and micromirror deflection error of the original path and the redundant verification path; The path difference corresponding to the attenuation rate of the collected optical power and the micromirror deflection error is calculated separately, specifically as follows: Optical power attenuation rate difference = |original path attenuation rate - redundant path attenuation rate| / original path attenuation rate; Micromirror deflection error difference = |Original path error - Redundant path error| / Original path error; If the difference in optical power attenuation rate is less than or equal to the preset attenuation threshold, and the difference in micromirror deflection error is greater than the preset error threshold, it is determined to be a link fault; if the difference in optical power attenuation rate is greater than the preset attenuation threshold, and the difference in micromirror deflection error is less than or equal to the preset error threshold, it is determined to be an abnormal environmental interference.

6. The monitoring method based on a MEMS fiber optic switching matrix as described in claim 1, characterized in that, The step of adjusting the scanning frequency of the corresponding fiber optic channel according to the fault type, increasing the scanning frequency of fiber optic channels with inherent link faults to obtain high-frequency scanning data, and maintaining the initial scanning frequency of fiber optic channels with abnormal environmental interference includes the following steps: For fiber optic channels with inherent link failures, the cumulative operating time and historical deflection count of the corresponding micromirror unit are obtained to calculate the hardware fatigue coefficient. The frequency boost level is determined based on the hardware fatigue coefficient, specifically as follows: If the hardware fatigue coefficient is less than or equal to the first coefficient threshold, the scanning frequency will be increased to the first multiple of the initial scanning frequency. If the hardware fatigue coefficient is greater than the first coefficient threshold but less than or equal to the second coefficient threshold, the scanning frequency will be increased to a second multiple of the initial scanning frequency. If the hardware fatigue coefficient is greater than the second coefficient threshold, the scanning frequency will be increased to the third multiple of the initial scanning frequency, and the duration of a single high-frequency scan will not exceed the preset safe duration. For fiber optic channels with abnormal environmental interference, monitor the fluctuation range of environmental parameters in the associated area of ​​the fiber optic channel. If the fluctuation range is less than or equal to the environmental stability threshold, maintain the initial scanning frequency; if the fluctuation range is greater than the environmental stability threshold, perform a short-term high-frequency scan at a preset interval based on the initial scanning frequency. The system calculates the density of abnormal features in high-frequency scan data in real time. When the density is continuously below a stable threshold for a preset duration, it automatically reverts to the initial scan frequency.

7. A monitoring system based on a MEMS fiber optic switching matrix, used to implement the steps of the monitoring method based on a MEMS fiber optic switching matrix as described in any one of claims 1-6, characterized in that, include: The path mapping module is used to control the management unit to send control commands to the MEMS fiber optic switching matrix, complete the initial path mapping of multiple fiber optic channels, and configure the corresponding initial scanning frequency according to the preset importance level of each fiber optic channel. The baseline model construction module is used to poll each optical fiber channel through the MEMS optical fiber switching matrix, collect performance parameters within a preset time period, and construct a real-time dynamic baseline model for each optical fiber channel based on the sliding window algorithm; wherein, the performance parameters include at least optical power, signal-to-noise ratio and optical delay, and the dynamic baseline model is a dynamic reference range determined based on the historical fluctuation characteristics of the corresponding performance parameters. The parameter acquisition and comparison module is used to acquire real-time performance parameters of each fiber channel of the MEMS fiber switching matrix at the initial scanning frequency, compare the real-time acquired performance parameters with the dynamic baseline model, and trigger an abnormal signal when the parameter deviation exceeds the preset threshold. The fault differentiation module is used to respond to abnormal signals and control the management unit to switch the MEMS fiber optic switching matrix to the corresponding redundant verification path. By comparing and analyzing the performance parameters of the original path and the redundant verification path, it can distinguish between environmental interference abnormalities and link faults. The scanning frequency adjustment module is used to adjust the scanning frequency of the corresponding fiber optic channel according to the fault type. It increases the scanning frequency of fiber optic channels with link faults to obtain high-frequency scanning data, and maintains the initial scanning frequency of fiber optic channels with abnormal environmental interference. The assessment and early warning module is used to generate fiber optic link status assessment reports and issue corresponding early warning information based on fault type and high-frequency scanning data.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the monitoring method based on a MEMS fiber optic switching matrix as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the monitoring method based on a MEMS fiber optic switching matrix as described in any one of claims 1-6.

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