Big data-based real-time monitoring method and system for optical fiber line quality

Abstract

The application provides a fiber line quality real-time monitoring method and system based on big data, relates to the technical field of fiber line monitoring, and comprises the following steps: combining a current effective OTDR curve and a historical OTDR curve after verification into comparable monitoring data; based on the historical normal data state, a segmented reference structure is established based on a historical normal OTDR curve under the same condition, position mapping is carried out, the current effective OTDR curve is compared with a normal change range position by position, an effective deviation proportion is calculated according to continuous out-of-range positions and fixed event blind areas, and suspected quality difference information is combined; candidate verification schemes are screened and discrimination application values are calculated, a verification scheme is determined based on the discrimination application values, and verification test tasks are combined; an effective verification OTDR curve is determined, a line quality state is determined according to difference performance types and verification comparison results, and corresponding monitoring disposal instructions and line quality monitoring results are generated according to the quality state.

Description

Technical Field

[0001] This invention relates to the field of optical fiber line monitoring technology, and more particularly to a method and system for real-time monitoring of optical fiber line quality based on big data. Background Technology

[0002] With the continuous expansion of communication networks such as backbone transmission networks, access networks, and enterprise leased lines, the operational quality of fiber optic lines directly affects the continuity of communication services and the efficiency of line maintenance. Current fiber optic line quality monitoring typically employs OTDR equipment to inject test light pulses into the target fiber, collect backscattering data along the line, and generate OTDR curves reflecting line attenuation, reflection, and fixed event locations. These curves are then compared with pre-saved baseline curves or preset thresholds to identify line conditions such as splice degradation, localized bending, abnormal attenuation, and fiber breaks.

[0003] In practical applications, this type of scheme typically uses the line number as the basis for historical data retrieval. However, after route relocation, joint additions or removals, or fiber core reconnection, the number of fixed events, their arrangement, and the structure of line segments may have changed for the same line. Furthermore, different test directions, test wavelengths, pulse widths, measurement ranges, averaging times, and sampling configurations will alter the curve's spatial resolution, noise level, event coverage, and effective remote measurement capabilities. Therefore, historical curves with the same line number are not necessarily directly comparable. Mixing curves from different line versions or different test schemes to form a benchmark can easily lead to misinterpreting changes in engineering structure and test conditions as changes in line quality.

[0004] Even with the same test scheme, the location of fixed events may still have slight shifts at different measurement times. If the entire curve is uniformly shifted or directly compared point by point, the positional error at the beginning may accumulate along the line, causing misalignment in the far-end section and subsequent events. There are also blind zones near the fixed events of the OTDR, which are related to pulse width and equipment configuration. Local fluctuations within these blind zones may expand into continuous out-of-range sections. In long-distance lines, adjacent consecutive events may merge into wide events due to larger pulse widths, and far-end sections may frequently cross the normal range due to insufficient effective signals. Existing systems, upon detecting these suspected changes, typically generate alarms directly or continue to repeat measurements according to the original test parameters. It is difficult to determine whether to increase the average number of measurements, shorten the pulse width, or enhance the far-end measurement capability based on the actual performance of the differences. Some schemes, even after changing the test parameters, still use the baseline curve under the initial measurement conditions, resulting in a lack of consistency between the verification curve and the reference curve.

[0005] At the same time, the large number of historical OTDR curves accumulated by the monitoring system over a long period of time are mostly used for storage, querying and trend display. The line version, test plan, fixed event structure and normal fluctuation information in the historical data have failed to form a continuous basis for anomaly judgment and verification.

[0006] Therefore, existing technologies still need a method that can first screen historical normal curves according to the line engineering status and actual test conditions, then complete the curve correspondence and normal range construction for line sections limited by fixed events, and select a verification test scheme with corresponding historical normal references based on the specific manifestations of suspected differences, and finally re-form normal references under verification conditions to complete line quality confirmation and monitoring and handling, so as to improve the reliability of judgment of slight amplitude changes, overlapping adjacent events and remote anomalies. Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a method and system for real-time monitoring of fiber optic line quality based on big data.

[0008] To achieve the above objectives, this invention proposes a real-time monitoring method for fiber optic line quality based on big data, comprising: The system acquires the monitoring task of the optical fiber line, controls the OTDR device to execute the strategy and generate the current OTDR curve based on the monitoring task, filters the current OTDR curve to obtain the current valid OTDR curve, extracts the historical OTDR curve from the historical database, performs verification on the historical OTDR curve, and combines the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, which includes historical normal data status. Based on the historical normal data status, a segmented reference structure is established using the historical normal OTDR curves under the same conditions and position mapping is performed. A normal variation range is formed based on the mapped historical normal OTDR curves. The current effective OTDR curve is compared with the normal variation range position by position. The effective deviation ratio is calculated based on the continuous out-of-range positions and fixed event blind spots. The difference manifestation type is determined by combining the ratio, deviation direction and event comparison results. The effective deviation ratio and difference manifestation type are combined into suspected quality difference information. Candidate verification schemes are selected from the test scheme library based on the type of difference in the suspected quality difference information. The difference segment is mapped to the sampling sequence of the candidate verification scheme and the discrimination applicability value is calculated. The verification scheme is determined based on the discrimination applicability value. The number of verification executions is determined based on the type of difference and the effective deviation ratio. The verification judgment condition is formed by combining the type of difference. The task information containing the verification scheme, the number of executions and the judgment condition is combined into a verification test task. The OTDR is controlled to perform multiple verification measurements according to the verification test task to determine the effective verification OTDR curve. The historical normal OTDR curve is retrieved and compared with the effective verification OTDR curve. The line quality status is determined according to the difference performance type and the verification comparison result. The corresponding monitoring and handling instructions and line quality monitoring results are generated according to the quality status.

[0009] In some embodiments, performing verification on the historical OTDR curve specifically includes: By reading the device execution parameters saved in the history record, the device execution parameters are matched one by one with the test scheme configuration marked in the record, and the historical OTDR curves with the actual test direction, test wavelength, pulse width, measurement range and average number of times are retained to be consistent with the test scheme configuration, thus completing the actual test condition verification. Based on the equipment operating status, backscatter data continuity and line termination events, determine whether the historical OTDR curve completely covers the target line. Retain the historical OTDR curves that are collected normally, have continuous valid data and cover the line termination section to complete the curve integrity verification. By reading the OTDR event records corresponding to the historical OTDR curves and matching the order of occurrence of test terminal connection events, registered connection events, and line termination events with the fixed event log of the current line version, the fixed event structure verification is completed.

[0010] In some embodiments, before combining the currently valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, the method further includes: Count the number of valid records of historical normal OTDR curves under the same conditions; If the number of valid records reaches the preset minimum usage limit, the historical normal data status will be marked as available. If the minimum usage quantity is not reached, it is marked as pending accumulation, and normal monitoring records of the target line continue to be collected. The minimum usage quantity is pre-configured based on the line length, number of fixed events, or stability requirements of the test plan.

[0011] In some embodiments, the step of establishing a segmented reference structure based on historical normal OTDR curves under the same conditions and performing position mapping specifically includes: The monitoring server establishes a segmented reference structure based on the corresponding event records of historical OTDR curves under the same conditions; Test connection events, line termination events, and registered connection events that appear continuously in the same order in multiple historical normal OTDR curves are identified as segmented reference events, and a continuous comparison segment is formed between two adjacent segmented reference events.

[0012] In some embodiments, determining the type of difference based on the ratio, deviation direction, and event comparison results specifically includes: When the current valid OTDR curve continuously exceeds the normal range of change in the same direction within the same continuous comparison segment and reaches the segment confirmation condition, and when the continuous coverage range exceeds the historical normal coverage range and covers the adjacent fixed event location, the difference type is determined to be continuous amplitude deviation type. When the continuous coverage area of ​​a current fixed event exceeds the historical normal coverage area and covers the location of an adjacent fixed event, the difference type is determined to be event overlap type; When the current effective OTDR curve crosses the upper and lower boundaries of the normal range of change multiple times within the continuous comparison segment and the deviation direction changes continuously, and the continuous coverage range exceeds the historical normal coverage range and covers the location of adjacent fixed events, the difference type is determined to be insufficient remote effectiveness.

[0013] In some embodiments, the step of filtering candidate verification schemes from the test scheme library based on the type of difference in the suspected quality difference information specifically includes: For continuous magnitude deviations, select the option with a higher average number of deviations than the current test option and other main conditions being the same; For events with overlapping differences, select a scheme with a pulse width shorter than the current test scheme and a range that covers the target segment; For discrepancies due to insufficient remote effectiveness, when remote events are identifiable but scattering data fluctuates greatly, the option to increase the average number of occurrences should be selected. When both remote events and scattering data weaken, the option with a larger pulse width and coverage of line termination events should be selected.

[0014] In some embodiments, determining the line quality status based on the difference performance type and verification comparison results specifically includes: For discrepancies of continuous magnitude deviation or insufficient remote validity, when the verification consistency ratio reaches the verification judgment condition and the stable deviation segment continuously overlaps with the first suspected discrepancy segment, it is confirmed as abnormal. For events with overlapping differences, the original wide event is considered normal when it is stably decomposed into multiple events and all decomposed events are within the normal range; it is considered abnormal when any decomposed event continuously exceeds the corresponding normal range; and it is considered concerning when multiple decomposition results are inconsistent.

[0015] In some embodiments, generating corresponding monitoring and handling instructions and line quality monitoring results based on the quality status specifically includes: When the quality status is normal, an instruction is generated to maintain the current routine monitoring cycle; When the quality status is "concerned", an instruction to switch to the key monitoring cycle is generated, and the target section is written into the key monitoring task; When the quality status is confirmed as abnormal, a line quality abnormality alarm is generated, along with a line maintenance task containing the abnormal section and the type of difference in performance.

[0016] In some embodiments, the verification consistency ratio is calculated and generated by the effective verification OTDR curve, the number of effective verification OTDR curves, and the number of common valid mapping positions. The effective verification OTDR curve is the cumulative number of common valid mapping positions that exceed the normal range of verification change along the direction recorded in the first comparison within the verification target segment.

[0017] To achieve the above objectives, another aspect of the present invention proposes a real-time monitoring system for fiber optic line quality based on big data, comprising: The monitoring data construction module acquires the monitoring task of the optical fiber line, controls the OTDR device to execute the strategy and generate the current OTDR curve based on the monitoring task, filters the current OTDR curve to obtain the current valid OTDR curve, extracts the historical OTDR curve from the historical database, performs verification on the historical OTDR curve, and combines the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, which includes historical normal data status. The difference information construction module is used to establish a segmented reference structure and perform position mapping based on the historical normal data status and the same conditions of the historical normal OTDR curve. It forms a normal variation range according to the mapped historical normal OTDR curve, compares the current effective OTDR curve with the normal variation range position by position, calculates the effective deviation ratio based on the continuous out-of-range positions and fixed event blind spots, determines the difference manifestation type by combining the ratio, deviation direction and event comparison results, and combines the effective deviation ratio and difference manifestation type into suspected quality difference information. The test task construction module is used to filter candidate verification schemes from the test scheme library according to the difference manifestation type in the suspected quality difference information, map the difference segment to the sampling sequence of the candidate verification scheme and calculate the discrimination applicability value, determine the verification scheme based on the discrimination applicability value, determine the number of verification executions according to the difference manifestation type and the effective deviation ratio, and form verification judgment conditions in combination with the difference manifestation type, and combine the task information containing the verification scheme, the number of executions and the judgment conditions into a verification test task; The monitoring execution module controls the OTDR to perform multiple verification measurements according to the verification test task to determine the effective verification OTDR curve, retrieves historical normal OTDR curves and compares them with the effective verification OTDR curves, determines the line quality status based on the difference type and verification comparison results, and generates corresponding monitoring and handling instructions and line quality monitoring results based on the quality status.

[0018] The beneficial effects of this invention are as follows: This invention addresses the problem of unreliable monitoring result confirmation caused by the interplay of changes in OTDR testing conditions, line engineering structure, and actual line quality. It establishes a monitoring process that seamlessly integrates historical normal data screening, the formation of suspected quality discrepancies, the generation of targeted verification tasks, and post-verification line handling. The system first extracts historical OTDR curves from historical normal monitoring data that are consistent with the current measurement in terms of management object, engineering structure, and actual testing conditions, based on the target line, target fiber core, test direction, current line version, and current test plan. The system then confirms the correspondence between the curves and the current optical fiber physical structure through a fixed event log and historical event records.

[0019] Based on this, the system defines continuous comparison segments by defining adjacent fixed events, segments the current curve and historical normal curves to correspond to each other, forming continuous scattering segments and normal variation ranges for fixed events. Combining the occupancy of the fixed event blind zone in the candidate difference segments, the system identifies suspected quality differences with clear physical locations and performance characteristics. Based on differences such as continuous amplitude deviation, event overlap, or insufficient remote effectiveness, the system selects verification schemes from pre-configured test schemes on the OTDR device that have historical normal data for the current line version. It also considers the historical reference coverage of the target segment, the occupancy of the fixed event blind zone, and changes in accompanying test conditions to determine the verification test task that can be measured against the current identified obstacle. After the verification measurement is completed, the system reconstructs the normal variation range under the verification conditions using the historical normal curve corresponding to the verification test scheme, and maps the verification results to the same physical line segment in the initial measurement by the relative positions of preceding and following fixed events and within the segment.

[0020] For continuous amplitude deviations and insufficient remote validity, the line status is confirmed based on the persistence of deviations in the same direction in multiple verification curves. For overlapping events, the line status is confirmed based on the consistent results of event decomposition in multiple verification curves and the changes of decomposed events relative to the normal range. Finally, the system maintains the regular monitoring cycle, switches to the key monitoring cycle, generates abnormal alarms, or forms line maintenance tasks based on the confirmation results, so that historical monitoring data, real-time OTDR measurements, verification test plans, and actual line handling form a continuous technical process. Attached Figure Description

[0021] Figure 1 This is a flowchart of a real-time monitoring method for fiber optic line quality based on big data in a specific embodiment of the present invention; Figure 2 This is a block diagram of a real-time fiber optic line quality monitoring system based on big data, as described in a specific embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] refer to Figure 1 As shown, one embodiment of the present invention proposes a real-time monitoring method for fiber optic line quality based on big data, including: S1: Obtain the monitoring task of the optical fiber line, control the OTDR device to execute the strategy based on the monitoring task and generate the current OTDR curve, filter the current OTDR curve to obtain the current valid OTDR curve, extract historical OTDR curves from the historical database, perform verification on the historical OTDR curves, and combine the current valid OTDR curves and the verified historical OTDR curves into comparable monitoring data. The comparable monitoring data includes historical normal data status, specifically including:

[0024] The monitoring server receives monitoring tasks for the current fiber optic line. These tasks include the target line number, target fiber core number, test terminal identifier, and standard test scheme number. The target line number and target fiber core number are provided by the line resource management system and are used to determine the actual fiber optic channel to be measured. The test terminal identifier determines the test direction for the OTDR device to access the target fiber. The standard test scheme number corresponds to a pre-configured set of test schemes in the OTDR control system. These configurations include test direction, test wavelength, pulse width, measurement range, averaging count, sampling configuration, fixed event blind zone configuration, and segment confirmation conditions. The test scheme library also associates and stores the estimated measurement duration for each test scheme and historical normal data identifiers for the corresponding line version. Based on the target line number, target fiber core number, and test terminal identifier, the monitoring server sends a channel switching command to the optical switch control device. After switching the OTDR test port to the target fiber core, the optical switch control device returns channel switching completion information. The monitoring server then uses this information to call the test conditions corresponding to the standard test scheme number to control the OTDR device to perform the measurement. After completing the measurement, the OTDR device sends the current OTDR curve, the actual test conditions, the test time, and the device's operating status to the monitoring server. The current OTDR curve is backscatter data arranged in order of fiber optic measurement distance. The monitoring server checks the actual test direction, test wavelength, pulse width, measurement range, and average number of measurements against the preset conditions corresponding to the conventional test scheme number. If all conditions are consistent and the device is operating normally, the conventional test scheme number is designated as the current test scheme number for this measurement, and the current OTDR curve is determined as valid current measurement data. If parameter loading discrepancies occur, the monitoring server re-invokes the conventional test scheme to complete the measurement. For example, if the conventional test scheme specified by the monitoring task corresponds to the first pulse width, but the actual execution record returned by the device shows the use of the second pulse width, the monitoring server reloads the conventional test scheme and measures again until the actual execution conditions match the preset conditions corresponding to the current test scheme number.

[0025] After obtaining the currently valid OTDR curve, the monitoring server reads the current line version number and fixed event log corresponding to the target line through the line resource management interface. The line version number indicates the current engineering structure status of the target line. When the line undergoes route relocation, joint addition, joint removal, fiber core reconnection, or other engineering operations that cause changes in the number, sequence, or location of fixed events, the line resource management system establishes a new line version number. The fixed event log is formed by line commissioning and acceptance records, connection records, and line resource records, and at least records the chronological relationship of test end connection events, registered connection events, and line termination events. The monitoring server uses the target line number, target fiber core number, test end identifier, current line version number, and current test plan number as common query conditions to extract candidate historical OTDR curves from the historical normal monitoring database. Each record in the historical normal monitoring database contains at least the historical OTDR curve, the actual test conditions, equipment operating status, corresponding event record, acquisition time, and normal status marker. The normal status marker comes from the confirmation measurement after line commissioning and acceptance, the recovery confirmation measurement after maintenance, or the normal records formed during the stable operation of the line. The candidate historical OTDR curves extracted using the aforementioned common query conditions correspond to the same line, fiber core, test direction, line engineering status, and test plan at the management record level, thus forming the data range for subsequent physical structure verification. Taking a fiber optic line that has completed relocation as an example, the line number and fiber core number remain consistent before and after the relocation, but the newly added splicing event changes the fixed event structure. Based on this, the line resource management system uses a new line version number. When filtering historical records, the monitoring server only extracts curves formed under the new line version, ensuring that subsequent reference data corresponds to the actual engineering structure of the current line.

[0026] The monitoring server sequentially performs actual test condition verification, curve integrity verification, and fixed event structure verification on candidate historical records. Actual test condition verification reads the device execution parameters saved in the historical records and matches them item by item with the test plan configuration marked in the record, retaining historical curves where the actual test direction, test wavelength, pulse width, measurement range, and average number of tests are consistent with the test plan configuration. Curve integrity verification determines whether the historical curve completely covers the target line based on the device operating status, backscatter data continuity, and line termination events, retaining historical curves where the acquisition process is normal, the valid data is continuous, and the line termination section is covered. Fixed event structure verification reads the OTDR event records corresponding to the historical curves and matches the order of occurrence of test end connection events, registered connection events, and line termination events with the fixed event log of the current line version; the specific location of fixed events is allowed to have normal measurement offsets, and their sequence and line coverage should be consistent with the current line structure. For example, the current line version's fixed event log sequentially records test terminal connection events, first connection events, second connection events, and line termination events. If a candidate historical curve only contains test terminal connection events and closely spaced termination events, the monitoring server, combining the curve's coverage area and equipment operation records, identifies the curve as a record indicating anomalies in the access channel, range configuration, or acquisition process. If another candidate historical curve contains a complete event sequence, and all events are within the segment corresponding to the current line structure, it is retained as a normal historical OTDR curve under the same conditions. Through continuous verification of management identifiers, actual test conditions, and fixed event structures, the screening results simultaneously satisfy data consistency, measurement condition consistency, and physical structure consistency.

[0027] After completing the above verification, the monitoring server identifies the retained historical curves as historical normal OTDR curves under the same conditions, and retains the original backscatter data and corresponding event records for each historical curve. The monitoring server counts the number of valid records for historical normal OTDR curves under the same conditions, and forms a historical normal data status based on the minimum usage number set during system deployment. The minimum usage number is configured according to line length level, fixed event count, test scheme stability requirements, or maintenance parameter table, and is saved along with the target line type and test scheme number. When the number of valid records reaches the minimum usage number, the historical normal data status is recorded as available; when the number of valid records has not yet reached the minimum usage number, the historical normal data status is recorded as pending accumulation, and normal monitoring records for the target line continue to be collected according to the same test scheme. This setting allows S2 to determine whether there is a basis for forming a normal variation range based on the historical normal data status. For example, when a newly put-in target optical fiber only forms a small number of normal records, the system continues to accumulate curves at different collection times according to the same conventional test scheme; when the number of valid records reaches the preset condition, S2 uses these historical normal OTDR curves under the same conditions to form the normal variation range under the current test conditions.

[0028] The monitoring server ultimately combines the current OTDR curve, target line number, target fiber core number, test terminal identifier, current line version number, current test scheme number, historical normal OTDR curves under the same conditions, corresponding event records, and historical normal data status into current comparable monitoring data, and sends this data to S2. S2 reads the current OTDR curve and the historical normal OTDR curves under the same conditions, completes the curve position correspondence based on the corresponding event records, and establishes a normal variation range. The target line number, target fiber core number, test terminal identifier, current line version number, and current test scheme number are used to ensure consistency between the monitoring object and test conditions of the current curve and the historical curves. The historical normal data status is used to determine whether the current data enters the automatic quality difference judgment. Thus, the current OTDR curve and historical normal data form a unified comparison basis at three levels: fiber object, engineering structure, and OTDR test conditions.

[0029] S2: Based on the historical normal data status, a segmented reference structure is established using historical normal OTDR curves under the same conditions, and position mapping is performed. A normal variation range is formed based on the mapped historical normal OTDR curves. The current effective OTDR curve is compared position by position with the normal variation range. The effective deviation ratio is calculated based on continuous out-of-range positions and fixed event blind spots. The difference manifestation type is determined by combining the ratio, deviation direction, and event comparison results. The effective deviation ratio and difference manifestation type are combined into suspected quality difference information, specifically including: S2 receives the current comparable monitoring data generated by S1. This data includes the current OTDR curve, target line number, target fiber core number, test terminal identifier, current line version number, current test scheme number, historical normal OTDR curves under the same conditions, corresponding event records, and historical normal data status. When the historical normal data status is "to be accumulated," the monitoring server generates a reference to be accumulated result, writes the target line number, target fiber core number, test terminal identifier, current line version number, and current test scheme number into the data accumulation plan under the same conditions, and continues to collect normal monitoring records of the target line according to the current test scheme. When the historical normal OTDR curves under the same conditions reach the minimum usage quantity, S1 is re-executed. When the historical normal data status is "available," the monitoring server uses the current OTDR curve as the judgment object, the historical normal OTDR curves under the same conditions as the normal reference under the current test conditions, determines the fixed event relationships in each curve using the corresponding event records, and performs a suspected quality difference judgment.

[0030] The monitoring server establishes a segmented reference structure based on the corresponding event records of historical normal OTDR curves under the same conditions. Test-end connection events, line termination events, and registered connection events that appear continuously in the same order across multiple historical normal OTDR curves are identified as segmented reference events. A continuous comparison segment is formed between two adjacent segmented reference events. The monitoring server determines the event arrangement direction according to the test-end identifier and searches for the current event corresponding to each segmented reference event in the current OTDR curve. Event mapping is first completed based on the order of events and the relationship between adjacent segments, and then confirmed based on whether the current event is within the normal position range of the historical fixed event. After completing the fixed event mapping, the segmented reference events at both ends of each continuous comparison segment are used as position boundaries to perform intra-segment position mapping between the current OTDR curve and the historical normal OTDR curves under the same conditions. For curves with the same number of sampling locations, direct mapping is performed according to the sampling order; for curves with different numbers of sampling locations, the segment start event and segment end event are mapped as unified boundaries, and then segmented linear interpolation is performed according to the relative position within the segment, so that the mapped current curve data corresponds to the same physical line segment as the historical normal curve data.

[0031] The monitoring server forms a normal variation range based on the historical normal OTDR curves under the same conditions after location mapping. For the same mapped location in a continuous comparison segment, the historical normal data are arranged according to their numerical values, and the middle main data is selected to form the normal variation range for that location. For fixed events, normal ranges are formed for the event location, the curve drop before and after the event, the local reflection peak of the event, and the continuous coverage range of the event. As one implementation, when there are twenty historical normal records for the same mapped location, the records are sorted, the two lowest and two highest records are removed, and the minimum and maximum values ​​among the remaining records are used as the normal variation range. The monitoring server compares the current OTDR curve with the normal variation range position by position, merges the sampling locations that are within the same continuous comparison segment and continuously exceed the normal variation range in the same direction into candidate difference segments, and reads the fixed event blind zone configuration according to the current test scheme number to determine the sampling locations in the candidate difference segments that fall into the fixed event blind zone.

[0032] The effective continuous deviation ratio of candidate difference segments is: ; in, Indicates the proportion of valid continuous deviations in the candidate difference segment; This indicates the number of sampling locations in the current OTDR curve that continuously exceed the normal range of variation along the same direction within the candidate difference segment; This represents the number of sampling positions that fall into the fixed event blind zone among the continuous out-of-range sampling positions, and is not greater than [amount missing]. ; This represents the total number of sampling locations with valid historical normal references within the continuous comparison segment to which the candidate difference segment belongs, and is a positive integer in the calculation. , and All are the number of sampling locations, obtained from the formula. For dimensionless proportions; when If the value is 0 or the candidate difference segment does not have a valid historical normal reference, it is not calculated. The segment is then marked as insufficient reference. Taking a scenario with 600 valid comparison positions between two adjacent consecutive events as an example, if 180 consecutive positions in the current curve exceed the normal range of variation along the same direction, and 30 of these positions are located within the fixed event blind zone, then the effective continuous deviation ratio is 0.25. When the segment confirmation condition corresponding to the current test plan is 0.2, this candidate difference is retained as a suspected quality difference segment.

[0033] The monitoring server determines the type of difference by combining the effective continuous deviation ratio, the deviation direction of the current curve, and the comparison results of fixed events. When the current curve continuously exceeds the normal range in the same direction within the same continuous comparison segment and meets the segment confirmation criteria, it is recorded as a continuous amplitude deviation type difference. When the continuous coverage range of the current fixed event exceeds its historical normal coverage range and covers the location of adjacent fixed events, it is recorded as an event overlap type difference. When the continuous out-of-range locations are mainly concentrated in the fixed event blind zone, and the continuous coverage range of the current event exceeds the historical normal coverage range, it is recorded as an event boundary expansion scenario in the event overlap type difference. When the suspected difference is located at the rear of the line, and the current curve repeatedly crosses the upper and lower boundaries of the normal range within this segment with continuously changing deviation directions, it is recorded as a remote effectiveness insufficient type difference. The segment confirmation criteria are determined by the segment confirmation criteria in the test plan configuration and are saved along with the test plan number, sampling configuration, and fixed event blind zone configuration. For situations where multiple manifestations occur simultaneously in the same physical segment, the manifestation that directly determines the subsequent verification test parameters is taken as the primary difference type, and other manifestations are written as accompanying differences in the initial comparison results.

[0034] The monitoring server associates and saves suspected quality difference sections with their preceding and following segment reference events, and uses the target line number, target fiber core number, test terminal identifier, current line version number, preceding segment reference event, following segment reference event, and the relative position of the suspected quality difference section between the two segment reference events to describe the difference location. The current test scheme number, historical normal data identifier under the same conditions, effective continuous deviation ratio, difference manifestation type, accompanying difference, and the deviation direction and continuous range of the current curve relative to the normal change range together constitute the initial comparison result. When the current OTDR curve is entirely within the normal change range, the monitoring server generates a line normal result and maintains the regular monitoring cycle, and the current monitoring process ends; when a suspected quality difference section exists, S2 outputs suspected quality difference information, which includes the target line number, target fiber core number, test terminal identifier, current line version number, current test scheme number, historical normal data identifier under the same conditions, segment reference event, suspected quality difference section, difference manifestation type, effective continuous deviation ratio, and initial comparison result, and sends the suspected quality difference information to S3.

[0035] S3: Based on the difference manifestation type in the suspected quality difference information, candidate verification schemes are selected from the test scheme library. The difference segments are mapped to the sampling sequences of the candidate verification schemes, and a discrimination applicability value is calculated. Based on the discrimination applicability value, a verification scheme is determined. The number of verification executions is determined based on the difference manifestation type and the effective deviation ratio. Verification judgment conditions are formed by combining the difference manifestation type. The task information, including the verification scheme, execution count, and judgment conditions, is combined into a verification test task, specifically including: S3 receives the suspected quality difference information output by S2. The monitoring server determines the actual fiber optic channel corresponding to the verification measurement based on the target line number and target fiber core number. It maintains the observation direction of the verification measurement consistent with the initial measurement based on the test end identifier. It limits the engineering structure corresponding to the verification reference data based on the current line version number. It reads the test conditions used in the initial measurement based on the current test scheme number, and determines the verification target section based on the segmented reference events and suspected quality difference sections. Historical normal data identifiers under the same conditions are retained with the verification test task and used to record the range of historical normal data used in the initial comparison.

[0036] The monitoring server reads the preset test scheme library from the OTDR control system and extracts test schemes with corresponding historical normal records according to the target line number, target fiber core number, test terminal identifier, and current line version number. Then, based on the segmented reference events before and after the suspected quality difference section, it retains candidate verification test schemes whose measurement range can cover the previous segmented reference event, the suspected quality difference section, and the subsequent segmented reference event. For continuous amplitude deviation type differences, candidate schemes with an average number of occurrences higher than the current test scheme are used, while maintaining consistency in test direction, test wavelength, pulse width, and measurement range. For event overlap type differences, candidate schemes with a pulse width shorter than the current test scheme and a measurement range that can cover the target verification section are used. In cases of insufficient remote effectiveness, when the remote fixed event can still be identified but the continuous scattering data fluctuates significantly, a candidate scheme with an increased average number of occurrences is used; when both the remote fixed event response and the continuous scattering data weaken simultaneously, a candidate scheme with a larger pulse width that can cover the line termination event is used.

[0037] The monitoring server maps suspected quality discrepancy sections to the sampling sequence of each candidate verification test scheme, and counts the sampling locations within the target sampling range that are within the measurement coverage of the candidate schemes and have historical normal references for the current line version. It then identifies locations that overlap with the fixed event blind zones of the candidate schemes. The applicable criteria for the candidate verification test schemes are: ; in, Indicates the discriminant value for candidate verification test schemes; This indicates the number of sampling locations within the measurement coverage of the candidate scheme and with normal historical references for the current line version after the suspected quality difference section is mapped to the candidate scheme. The sampling locations include those that coincide with the fixed event blind zone. express The number of sampling locations included that overlap with the fixed event blind zone of the candidate scheme; This represents the total number of sampling locations after the suspected quality difference segment is mapped to the candidate scheme; This indicates the number of test conditions that have changed relative to the current test plan, in addition to the main test conditions that need to be changed according to the difference performance type. , and All of these are the number of sampling locations. The number of test condition variations, and Not greater than , Not greater than , For non-negative integers, the formula yields... It is a dimensionless value; when When the value is zero, no calculation is performed. And the corresponding candidate scheme is marked as the target segment cannot be covered. This indicates the number of sampling locations that are within the measurement coverage area, have historical normal references, and are located outside the fixed event blind zone. Used to reduce the selection order of candidate solutions that involve changes to other test conditions.

[0038] Taking suspected quality differences due to overlapping events as an example, the first candidate scheme maps 500 target sampling locations, of which 470 locations are within the measurement coverage area and have historical normal references under the current line version. Of these 470 locations, 30 are located in fixed event blind zones. This scheme only changes the pulse width, resulting in a calculated discrimination applicability value of 0.88. The second candidate scheme has 490 locations within the measurement coverage area and with historical normal references, of which 20 are located in blind zones. It also changes the pulse width and test wavelength, resulting in a calculated discrimination applicability value of 0.47. The monitoring server ranks the candidate schemes from highest to lowest discrimination applicability value and determines the verification test scheme from those that meet the preset effective coverage conditions. The preset effective coverage conditions are determined by the verification coverage requirements in the test scheme configuration and are saved based on the effective reference ratio of the target section, the proportion of non-blind zone locations, or the line length level. When the discrimination applicability values ​​are the same, the scheme with the most historical normal records, the shortest expected measurement duration, and the one that the current OTDR port can execute is selected in sequence.

[0039] The monitoring server determines the number of verification executions based on the difference type and the effective continuous deviation ratio. In one embodiment, when the effective continuous deviation ratio of a continuous amplitude deviation type reaches the section confirmation condition but is less than 1.5 times that condition, verification is performed 3 times; when the effective continuous deviation ratio reaches 1.5 times the section confirmation condition, verification is performed 2 times. Both event overlap type differences and remote validity insufficient type differences are verified 3 times. The number of verification executions, multiple conditions, continuous confirmation conditions, and event decomposition consistency counts are all used as verification judgment parameters and are saved during system deployment according to the difference type, line length level, test scheme type, or operation and maintenance parameter table. The monitoring server forms verification judgment conditions based on the difference type: continuous amplitude deviation type differences are used to determine whether the verification curve continues to deviate continuously in the direction of the first comparison record within the verification target section; event overlap type differences are used to determine whether the original wide event is decomposed into multiple events, and whether the decomposed events exceed the normal range of events under the verification conditions; remote validity insufficient type differences are used to determine whether a stable deviation in the same direction is formed after improving remote measurement capabilities.

[0040] S3 outputs a verification test task, which includes the target line number, target fiber core number, test terminal identifier, current line version number, current test scheme number, verification test scheme number, number of verification executions, segment reference event, verification target segment, difference performance type, effective continuous deviation ratio, judgment applicable value, first comparison result, historical normal data identifier under the same conditions used in the first comparison, historical normal data identifier corresponding to the verification test scheme, and verification judgment conditions, and sends the verification test task to S4.

[0041] S4: Based on the verification test task, control the OTDR to perform multiple verification measurements to determine the valid verification OTDR curve. Retrieve historical normal OTDR curves and compare them with the valid verification OTDR curves. Determine the line quality status based on the difference type and verification comparison results. Generate corresponding monitoring and handling instructions and line quality monitoring results based on the quality status, specifically including: S4 receives the verification test task output by S3. The monitoring server controls the optical switch to connect to the target optical fiber based on the target line number, target fiber core number, and test end identifier. It reads the pre-configured test direction, test wavelength, pulse width, measurement range, averaging count, and sampling configuration according to the verification test scheme number, and controls the OTDR device to complete the verification measurement according to the verification execution count. After each measurement, the OTDR device returns the verification OTDR curve, actual execution parameters, device operating status, and measurement sequence identifier. The monitoring server matches the actual execution parameters with the verification test scheme configuration and determines the curve with normal operating status and consistent parameters as a valid verification OTDR curve. If the number of valid verification OTDR curves is less than the number specified in the verification task, the monitoring server continues to execute the remaining verification measurements. If the specified number is not obtained after reaching the device's preset retest count, the line quality status is marked as "concerned," and a device measurement verification record is generated. The device's preset retest count is determined by the OTDR device's allowed retest count, the expected measurement duration of the test scheme, and line monitoring cycle constraints, and is saved along with the test scheme configuration.

[0042] The monitoring server retrieves the historical normal OTDR curves for verification conditions based on the current line version number and the historical normal data identifier corresponding to the verification test plan. It then identifies fixed events before and after the target verification section according to segmented reference events. The monitoring server maps each valid verification OTDR curve and the historical normal OTDR curve for verification conditions to the same relative position sequence defined by the segmented reference events before and after. Following the processing method in S2, it forms the normal variation range of the continuous scattering section under verification conditions, as well as the position, curve drop, reflection performance, and continuous coverage range of the fixed events. The monitoring server compares each valid verification OTDR curve with the normal variation range to form a verification comparison result. This comparison result includes the deviation position, deviation direction, and continuous range of the continuous scattering section, as well as the decomposition results of the fixed events, changes in event position, curve drop, reflection performance, and coverage range. The discrimination application value formed in S3 is saved with the task as a verification test plan selection record. Line quality confirmation is based on the actually obtained valid verification OTDR curves and verification comparison results.

[0043] For discrepancies with continuous magnitude deviations and discrepancies with insufficient remote validity, the monitoring server calculates the verification consistency ratio based on multiple valid verification OTDR curves: ; in, Indicates the percentage of verifications that remain consistent; This represents the cumulative number of common valid mapping locations that exceed the normal range of change in the verification along the direction recorded in the first comparison within the target segment of the verification OTDR curve; This indicates the number of valid verification OTDR curves actually obtained; This represents the number of common valid mapping locations within the verification target segment for each valid verification OTDR curve. After mapping all valid verification OTDR curves to the same common valid location sequence, the total number of valid observations is... . , and All are quantities, obtained from the formula. For dimensionless proportions; when zero or When the value is zero, no calculation is performed. And if there is insufficient valid verification data, a status of concern or equipment measurement verification record will be generated.

[0044] In the implementation of a continuous amplitude deviation type difference, the verification test task was performed three times consecutively. The three valid verification OTDR curves each had 400 common valid mapping positions within the verification target segment. Of these, 320, 330, and 310 positions, respectively, exceeded the normal range of verification along the direction of the initial comparison record, totaling 960. It is 3. With a value of 400, the calculated verification consistency ratio is 0.8. When the continuity confirmation criterion in the verification decision criteria is 0.75, the difference is determined to be persistent. The continuity confirmation criterion is determined by the verification decision parameters formed in S3 and is saved with the verification test task. The effective continuous deviation ratio is used to determine the coverage of suspected differences in the initial measurement, and the verification consistency ratio is used to determine the persistence of the difference in multiple verification measurements.

[0045] For continuous deviations, when the proportion of consistent verification reaches the verification judgment condition, and the stable deviation section in multiple verification curves continuously overlaps with the initial suspected quality difference section, the line quality status is determined to be confirmed abnormal, and the overlapping section is determined to be the core abnormal section; when the corresponding position enters the normal range of verification after increasing the average number of times, the line quality status is determined to be normal. For deviations due to insufficient remote effectiveness, when the corresponding position enters the normal range of verification after improving remote measurement capabilities, the line quality status is determined to be normal; when the same physical segment forms a stable deviation in the same direction and the proportion of consistent verification reaches the verification judgment condition, the line quality status is determined to be confirmed abnormal; when the verification results are consistently near the boundary of the normal range of change, the line quality status is determined to be of concern.

[0046] For events with overlapping characteristics, the monitoring server statistically analyzes the event decomposition results from multiple valid verification OTDR curves. When the number of consistency criteria specified for verification is reached, an event verification conclusion is formed based on the consistent event decomposition results. If the original wide event is stably decomposed into multiple events, and each decomposed event falls within the normal range of variation corresponding to the event under the verification conditions, the line quality status is determined to be normal. If any decomposed event continuously exceeds the corresponding normal range of variation in the verification curve after reaching the specified number of consistency criteria, the segment containing that event is determined to be confirmed as abnormal. If the event decomposition results are inconsistent across multiple verification curves, the line quality status is determined to be of concern. The verification comparison results for events with overlapping characteristics record the number of events, event location, curve drop, reflection performance, and the final consistent event decomposition result adopted for each verification curve.

[0047] The monitoring server generates monitoring and handling instructions based on the line quality status. When the line quality status is normal, the current regular monitoring cycle is maintained and the current verification test task is closed. When the line quality status is "attention level," the target line number, target fiber core number, test terminal identifier, current line version number, and verification target section are written into the key monitoring task, and the pre-configured key monitoring cycle is invoked. When the line quality status is confirmed as abnormal, a line quality abnormality alarm and a line maintenance task are generated. The line maintenance task includes the target line number, target fiber core number, test terminal identifier, current line version number, confirmed abnormal section, difference manifestation type, initial comparison result, verification comparison result, and corresponding before and after segment reference events. The regular monitoring cycle and key monitoring cycle are configured by the line level, service carrying capacity level, or operation and maintenance parameter table. Monitoring and handling instructions are output through the line monitoring platform, alarm interface, or maintenance work order interface.

[0048] S4 ultimately outputs line quality monitoring results and monitoring and handling instructions. Line quality monitoring results include line quality status, confirmed abnormal sections, type of discrepancy, and verification comparison results. For discrepancies with continuous amplitude deviations and insufficient remote effectiveness, the line quality monitoring results also include the verification consistency ratio. For discrepancies with overlapping events, the line quality monitoring results also include event decomposition consistency results. Monitoring and handling instructions are used to maintain routine monitoring cycles, switch key monitoring cycles, issue abnormal alarms, or generate line maintenance tasks.

[0049] In one system implementation, the real-time monitoring system for fiber optic line quality includes a monitoring task access module, an optical switch control module, an OTDR measurement control module, a line version and fixed event reading module, a historical normal data filtering module, a curve segmentation mapping module, a normal change range construction module, a suspected difference identification module, a verification test scheme generation module, a verification result confirmation module, a monitoring and handling output module, and a storage module. The monitoring task access module receives the target line number, target fiber core number, test terminal identifier, and test plan number; the optical switch control module and OTDR measurement control module control the target fiber core access and perform routine and verification measurements; the line version and fixed event reading module obtains the line version number and fixed event ledger from the line resource management system; the historical normal data filtering module, curve segmentation mapping module, and normal change range construction module perform historical curve filtering, event correspondence, relative position mapping, and normal range formation in S1 and S2; the suspected difference identification module, verification test plan generation module, and verification result confirmation module calculate and judge the effective continuous deviation ratio, discrimination applicable value, verification continuous consistency ratio, and event decomposition consistency results; the monitoring and handling output module outputs monitoring cycle adjustments, abnormal alarms, or line maintenance tasks; and the storage module saves test plan configuration, fixed event blind zone configuration, historical normal OTDR curves, current OTDR curves, verification OTDR curves, initial comparison results, verification test tasks, and line quality monitoring results. The aforementioned modules are deployed in the processors of monitoring servers, edge monitoring hosts, OTDR control systems, or line monitoring platforms, and are connected to optical switch control devices, OTDR devices, line resource management systems, historical normal monitoring databases, and alarm maintenance systems via communication interfaces, enabling the above methods and steps to be executed by specific hardware interfaces and data processing modules.

[0050] refer to Figure 2 As shown, another aspect of this application embodiment proposes a real-time monitoring system for fiber optic line quality based on big data, including: The monitoring data construction module acquires the monitoring task of the optical fiber line, controls the OTDR device to execute the strategy and generate the current OTDR curve based on the monitoring task, filters the current OTDR curve to obtain the current valid OTDR curve, extracts the historical OTDR curve from the historical database, performs verification on the historical OTDR curve, and combines the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, which includes historical normal data status. The difference information construction module is used to establish a segmented reference structure and perform position mapping based on the historical normal data status and the same conditions of the historical normal OTDR curve. It forms a normal variation range according to the mapped historical normal OTDR curve, compares the current effective OTDR curve with the normal variation range position by position, calculates the effective deviation ratio based on the continuous out-of-range positions and fixed event blind spots, determines the difference manifestation type by combining the ratio, deviation direction and event comparison results, and combines the effective deviation ratio and difference manifestation type into suspected quality difference information. The test task construction module is used to filter candidate verification schemes from the test scheme library according to the difference manifestation type in the suspected quality difference information, map the difference segment to the sampling sequence of the candidate verification scheme and calculate the discrimination applicability value, determine the verification scheme based on the discrimination applicability value, determine the number of verification executions according to the difference manifestation type and the effective deviation ratio, and form verification judgment conditions in combination with the difference manifestation type, and combine the task information containing the verification scheme, the number of executions and the judgment conditions into a verification test task; The monitoring execution module controls the OTDR to perform multiple verification measurements according to the verification test task to determine the effective verification OTDR curve, retrieves historical normal OTDR curves and compares them with the effective verification OTDR curves, determines the line quality status based on the difference type and verification comparison results, and generates corresponding monitoring and handling instructions and line quality monitoring results based on the quality status.

[0051] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for real-time monitoring of fiber optic line quality based on big data, characterized in that, include: The system acquires the monitoring task of the optical fiber line, controls the OTDR device to execute the strategy and generate the current OTDR curve based on the monitoring task, filters the current OTDR curve to obtain the current valid OTDR curve, extracts the historical OTDR curve from the historical database, performs verification on the historical OTDR curve, and combines the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, which includes historical normal data status. Based on the historical normal data status, a segmented reference structure is established using the historical normal OTDR curves under the same conditions and position mapping is performed. A normal variation range is formed based on the mapped historical normal OTDR curves. The current effective OTDR curve is compared with the normal variation range position by position. The effective deviation ratio is calculated based on the continuous out-of-range positions and fixed event blind spots. The difference manifestation type is determined by combining the ratio, deviation direction and event comparison results. The effective deviation ratio and difference manifestation type are combined into suspected quality difference information. Candidate verification schemes are selected from the test scheme library based on the type of difference in the suspected quality difference information. The difference segment is mapped to the sampling sequence of the candidate verification scheme and the discrimination applicability value is calculated. The verification scheme is determined based on the discrimination applicability value. The number of verification executions is determined based on the type of difference and the effective deviation ratio. The verification judgment condition is formed by combining the type of difference. The task information containing the verification scheme, the number of executions and the judgment condition is combined into a verification test task. The OTDR is controlled to perform multiple verification measurements according to the verification test task to determine the effective verification OTDR curve. The historical normal OTDR curve is retrieved and compared with the effective verification OTDR curve. The line quality status is determined according to the difference performance type and the verification comparison result. The corresponding monitoring and handling instructions and line quality monitoring results are generated according to the quality status.

2. The method for real-time monitoring of fiber optic line quality based on big data according to claim 1, characterized in that, The verification of the historical OTDR curve specifically includes: By reading the device execution parameters saved in the history record, the device execution parameters are matched one by one with the test scheme configuration marked in the record, and the historical OTDR curves with the actual test direction, test wavelength, pulse width, measurement range and average number of times are retained to be consistent with the test scheme configuration, thus completing the actual test condition verification. Based on the equipment operating status, backscatter data continuity and line termination events, determine whether the historical OTDR curve completely covers the target line. Retain the historical OTDR curves that are collected normally, have continuous valid data and cover the line termination section to complete the curve integrity verification. By reading the OTDR event records corresponding to the historical OTDR curves and matching the order of occurrence of test terminal connection events, registered connection events, and line termination events with the fixed event log of the current line version, the fixed event structure verification is completed.

3. The method for real-time monitoring of fiber optic line quality based on big data according to claim 1, characterized in that, Before combining the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, the method further includes: Count the number of valid records of historical normal OTDR curves under the same conditions; If the number of valid records reaches the preset minimum usage limit, the historical normal data status will be marked as available. If the minimum usage quantity is not reached, it is marked as pending accumulation, and normal monitoring records of the target line continue to be collected. The minimum usage quantity is pre-configured based on the line length, number of fixed events, or stability requirements of the test plan.

4. The method for real-time monitoring of fiber optic line quality based on big data according to claim 1, characterized in that, The establishment of a segmented reference structure based on historical normal OTDR curves under the same conditions and the subsequent position mapping specifically include: The monitoring server establishes a segmented reference structure based on the corresponding event records of historical OTDR curves under the same conditions; Test connection events, line termination events, and registered connection events that appear continuously in the same order in multiple historical normal OTDR curves are identified as segmented reference events, and a continuous comparison segment is formed between two adjacent segmented reference events.

5. The method for real-time monitoring of fiber optic line quality based on big data according to claim 1, characterized in that, The determination of the difference type by combining the ratio, deviation direction, and event comparison results specifically includes: When the current valid OTDR curve continuously exceeds the normal range of change in the same direction within the same continuous comparison segment and reaches the segment confirmation condition, and when the continuous coverage range exceeds the historical normal coverage range and covers the adjacent fixed event location, the difference type is determined to be continuous amplitude deviation type. When the continuous coverage area of ​​a current fixed event exceeds the historical normal coverage area and covers the location of an adjacent fixed event, the difference type is determined to be event overlap type; When the current effective OTDR curve crosses the upper and lower boundaries of the normal range of change multiple times within the continuous comparison segment and the deviation direction changes continuously, and the continuous coverage range exceeds the historical normal coverage range and covers the location of adjacent fixed events, the difference type is determined to be insufficient remote effectiveness.

6. The method for real-time monitoring of fiber optic line quality based on big data according to claim 5, characterized in that, The step of selecting candidate verification schemes from the test scheme library based on the type of difference in the suspected quality difference information specifically includes: For continuous magnitude deviations, select the option with a higher average number of deviations than the current test option and other main conditions being the same; For events with overlapping differences, select a scheme with a pulse width shorter than the current test scheme and a range that covers the target segment; For discrepancies due to insufficient remote effectiveness, when remote events are identifiable but scattering data fluctuates greatly, the option to increase the average number of occurrences should be selected. When both remote events and scattering data weaken, the option with a larger pulse width and coverage of line termination events should be selected.

7. The method for real-time monitoring of fiber optic line quality based on big data according to claim 6, characterized in that, The determination of line quality status based on the type of difference and verification comparison results specifically includes: For discrepancies of continuous magnitude deviation or insufficient remote validity, when the verification consistency ratio reaches the verification judgment condition and the stable deviation segment continuously overlaps with the first suspected discrepancy segment, it is confirmed as abnormal. For events with overlapping differences, the original wide event is considered normal when it is stably decomposed into multiple events and all decomposed events are within the normal range; it is considered abnormal when any decomposed event continuously exceeds the corresponding normal range; and it is considered concerning when multiple decomposition results are inconsistent.

8. The method for real-time monitoring of fiber optic line quality based on big data according to claim 1, characterized in that, The process of generating corresponding monitoring and handling instructions and line quality monitoring results based on the quality status specifically includes: When the quality status is normal, an instruction is generated to maintain the current routine monitoring cycle; When the quality status is "concerned", an instruction to switch to the key monitoring cycle is generated, and the target section is written into the key monitoring task; When the quality status is confirmed as abnormal, a line quality abnormality alarm is generated, along with a line maintenance task containing the abnormal section and the type of difference in performance.

9. The method for real-time monitoring of fiber optic line quality based on big data according to claim 7, characterized in that, The verification consistency ratio is calculated and generated by the effective verification OTDR curve, the number of effective verification OTDR curves, and the number of common valid mapping positions. The effective verification OTDR curve is the cumulative number of common valid mapping positions that exceed the normal range of verification along the direction recorded in the first comparison within the verification target segment.

10. A real-time monitoring system for fiber optic line quality based on big data, characterized in that: include: The monitoring data construction module acquires the monitoring task of the optical fiber line, controls the OTDR device to execute the strategy and generate the current OTDR curve based on the monitoring task, filters the current OTDR curve to obtain the current valid OTDR curve, extracts the historical OTDR curve from the historical database, performs verification on the historical OTDR curve, and combines the current valid OTDR curve and the verified historical OTDR curve into comparable monitoring data, which includes historical normal data status. The difference information construction module is used to establish a segmented reference structure and perform position mapping based on the historical normal data status and the same conditions of the historical normal OTDR curve. It forms a normal variation range according to the mapped historical normal OTDR curve, compares the current effective OTDR curve with the normal variation range position by position, calculates the effective deviation ratio based on the continuous out-of-range positions and fixed event blind spots, determines the difference manifestation type by combining the ratio, deviation direction and event comparison results, and combines the effective deviation ratio and difference manifestation type into suspected quality difference information. The test task construction module is used to filter candidate verification schemes from the test scheme library according to the difference manifestation type in the suspected quality difference information, map the difference segment to the sampling sequence of the candidate verification scheme and calculate the discrimination applicability value, determine the verification scheme based on the discrimination applicability value, determine the number of verification executions according to the difference manifestation type and the effective deviation ratio, and form verification judgment conditions in combination with the difference manifestation type, and combine the task information containing the verification scheme, the number of executions and the judgment conditions into a verification test task; The monitoring execution module controls the OTDR to perform multiple verification measurements according to the verification test task to determine the effective verification OTDR curve, retrieves historical normal OTDR curves and compares them with the effective verification OTDR curves, determines the line quality status based on the difference type and verification comparison results, and generates corresponding monitoring and handling instructions and line quality monitoring results based on the quality status.

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