An optical fiber line fault diagnosis remote positioning control system

Abstract

The present application relates to the technical field of optical fiber fault diagnosis, and particularly relates to an optical fiber line fault diagnosis remote positioning control system, comprising an optical fiber line management center, a deployment collection module, a marking and association module, an anomaly verification module, a section division module, an interval positioning module and an execution control module; the present application is a full-process closed-loop mechanism through collection-preprocessing-fault identification-anomaly verification, uses a fault identification model to preliminarily determine a fault type, uses a weighted matching algorithm to accurately verify an anomaly detection point, effectively filters false positives caused by parameter fluctuations, greatly improves the accuracy and reliability of fault identification, avoids the waste of invalid maintenance costs, simultaneously realizes accurate landing from "interval positioning" to "point positioning" in single anomaly detection point and multiple anomaly detection point scenarios, and realizes dynamic adjustment and verification of a repair scheme by monitoring the parameter compliance in a real-time repair process, thereby improving the intelligent level of operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of optical fiber fault diagnosis technology, and in particular to a remote positioning and control system for optical fiber line fault diagnosis. Background Technology

[0002] With the rapid development of core digital economy industries such as 5G communication, cloud computing, and the Internet of Things, fiber optic communication networks, as the core infrastructure for information transmission, are experiencing continuous expansion in coverage and increasingly complex topologies. This places stringent demands on network operational stability and fault response timeliness. During long-term service, fiber optic lines are susceptible to factors such as natural environment (e.g., geological disasters, extreme weather), human-caused construction damage, and equipment aging and wear, which can lead to faults such as fiber breakage, abnormal signal attenuation, and loose connectors. If these faults are not located and repaired in a timely manner, they can cause large-scale communication outages, resulting in serious losses to industrial production, public services, and social governance.

[0003] Currently, traditional fiber optic fault detection and location technologies mainly rely on manual inspections and single-point testing with optical time domain reflectometers (OTDRs), which have significant limitations. On the one hand, traditional technologies lack systematic analysis of optical signal characteristic parameters and fault correlation verification mechanisms, making it difficult to effectively distinguish between real faults and false alarms caused by parameter fluctuations. Furthermore, the fault range division is vague and the degree of visualization is low, making it difficult for maintenance personnel to quickly locate the fault point. On the other hand, they fail to fully integrate information such as the physical topology of fiber optic lines and historical fault data, and lack digital and intelligent fault management methods, which cannot meet the needs of efficient operation and maintenance of modern fiber optic networks.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a remote location control system for optical fiber line fault diagnosis. It realizes real-time acquisition and monitoring of optical signal characteristic parameters by setting detection points along the line. After a fault occurs, it can quickly complete the abnormal marking, interval division, location and repair response, forming a fully automated closed loop of "monitoring-identification-location-repair-verification", which greatly shortens the fault response and repair cycle and reduces the economic losses and social impact caused by communication interruption.

[0006] The objective of this invention can be achieved through the following technical solution: a remote positioning and control system for optical fiber line fault diagnosis, comprising an optical fiber line management center, a deployment and acquisition module, a marking and association module, an anomaly verification module, a segment division module, an interval positioning module, and an execution control module;

[0007] The deployment acquisition module collects optical signal characteristic parameters of the target optical fiber line in real time by deploying multiple detection points along the target optical fiber line. The collected optical signal characteristic parameters are preprocessed to obtain an initial optical signal dataset, which is then sent to the optical fiber line management center for storage.

[0008] The labeling and association module is used to perform fault identification, anomaly labeling, and association construction analysis on the initial optical signal dataset, and to obtain anomaly detection points and a standardized fault type-core feature parameter mapping relationship library;

[0009] The anomaly verification module is used to perform feature matching and anomaly labeling feedback analysis on the parameter features of the extracted anomaly detection points to obtain a set of valid anomaly detection points;

[0010] The segment division module performs abnormal region division and visualization marking analysis on the set of valid abnormal detection points to obtain an initial fault segment visualization.

[0011] The interval location module is used to perform interval fault location marking analysis on the initial fault section visibility chart to obtain the fault location map;

[0012] The execution control module is used to respond to the fault location map and display it immediately. At the same time, it realizes the process of comparing and analyzing the data collected after fault repair with the preset standards until all parameters meet the standards, and generates a visual verification report and displays it immediately.

[0013] Preferably, the analysis process of the tagging and association module is as follows:

[0014] S1: Input the initial optical signal dataset into the pre-set fault identification model and output the fault type identification result;

[0015] S2: Call the initial optical signal dataset for each detection point;

[0016] S3: Perform discrimination processing on the initial optical signal dataset of each detection point. If the average value of the parameter obtained from three consecutive detections in the initial optical signal dataset exceeds the preset threshold range, the corresponding detection point is judged as an abnormal detection point.

[0017] S4: Retrieve historical fault data and fault characteristic parameters of the optical fiber line. Based on the preprocessing of historical fault data and fault characteristic parameters of the optical fiber line, construct a standardized fault type-core characteristic parameter mapping relationship library.

[0018] Preferably, the analysis process of the anomaly verification module is as follows:

[0019] Based on the fault type-core feature parameter mapping relationship library, a weighted matching algorithm is used to calculate the matching degree between abnormal parameters and the output results of the fault diagnosis model;

[0020] Set a matching degree threshold, compare the matching degree with the matching degree threshold to obtain the valid detection points of anomalies, and construct a set of valid detection points of anomalies based on the valid detection points of anomalies.

[0021] Preferably, the matching degree acquisition and analysis process is as follows:

[0022] Obtain the current output fault type identification result, set the fault type in the fault type identification result as the target fault type, if the abnormal parameter of the abnormal detection point contains a certain core related parameter, and the parameter change pattern is consistent with the description in the mapping library, it is counted as a matching item, obtain the pre-set weight coefficient corresponding to each matching item, and set the sum of the pre-set weight coefficients corresponding to each matching item as the cumulative matching weight;

[0023] Matching degree = Cumulative matching weight / Total weight of core associated parameters of the target fault type (total weight is the sum of matching weights of all core parameters under this fault type).

[0024] Preferably, the analysis process of the segmentation module is as follows: the set of valid abnormal detection points is initially divided. If the set of valid abnormal detection points contains a single valid abnormal detection point, it is determined to be a single abnormal detection point scenario. If the set of valid abnormal detection points contains multiple valid abnormal detection points, it is determined to be a multi-abnormal detection point scenario.

[0025] Preferably, in the single anomaly detection point scenario: if there is only one valid anomaly detection point, denoted as A, then the fault interval is two consecutive segments formed by the detection point, the preceding adjacent detection point (denoted as A-1), and the subsequent adjacent detection point (denoted as A+1), namely the [A-1,A] segment and the [A,A+1] segment;

[0026] If the valid detection point for the anomaly is at the beginning of the line (without A-1), then the fault range is only the [A,A+1] section; if it is at the end of the line (without A+1), then the fault range is only the [A-1,A] section.

[0027] Preferably, in the multi-anomaly detection point scenario: if there are multiple consecutive valid anomaly detection points, the first valid anomaly detection point is denoted as A, and the last valid anomaly detection point is denoted as A+n, where n is a natural number greater than zero. Then the fault interval is the continuous segment between the preceding detection point (A-1) of the first valid anomaly detection point and the subsequent detection point (A+n+1) of the last valid anomaly detection point, i.e., the [A-1, A+n+1] segment.

[0028] If the parameter of a certain detection point (A) does not reach the abnormal threshold, but the difference between the parameter of the adjacent detection point (A-1) exceeds the gradient threshold and shows a continuous gradient change trend, then the fault interval is determined to be the [A-1,A] segment and marked as the gradient abnormal interval.

[0029] The core information of the faulty section is synchronized to the pre-constructed fiber optic line twin map. The faulty section is marked in yellow in the fiber optic line twin map, and the non-faulty section is marked in green in the fiber optic line twin map, thus obtaining the initial faulty section visualization.

[0030] Preferably, the analysis process of the interval positioning module is as follows:

[0031] The optical fiber physical parameters and topology corresponding to the fault section in the optical fiber line twin map are called to construct a transmission simulation model; the topology data within the fault section is extracted from the optical fiber line twin map, the path component units are identified, and the optical fiber physical parameters corresponding to each unit are associated.

[0032] Based on the pre-calibrated transmission simulation model, the abnormal optical signal characteristic parameters are input, and the propagation process of the abnormal optical signal in each path unit is simulated.

[0033] Let A and B be two adjacent detection points within the fault section, with a distance of L_AB (extracted from the fiber optic line twin map), and the fault point be F. We need to reverse-calculate the distances D_AF (distance between F and A) and D_BF (distance between F and B, D_BF=L_AB-D_AF) to obtain the latitude and longitude coordinates of the fault point, and finally output the fault location map.

[0034] Preferably, the analysis process of the execution control module is as follows:

[0035] After the engineer completes the fault repair, the existing fiber optic fault repair remote control module deployed at one end of the target fiber optic line is put into operation, while receiving fault repair verification instructions uploaded from the application terminal.

[0036] The system automatically switches the fiber optic fault repair remote control module to the light receiving mode, continuously collects light receiving power and signal stability data, compares the collected data with preset standards in real time until all parameters meet the standards, generates a visual verification report and displays it immediately.

[0037] The beneficial effects of this invention are as follows:

[0038] (1) This invention uses a closed-loop mechanism of data acquisition, preprocessing, fault identification, and anomaly verification. It uses a fault identification model to initially determine the fault type and a weighted matching algorithm to accurately verify the anomaly detection points. This effectively filters out false alarms caused by parameter fluctuations, greatly improves the accuracy and reliability of fault identification, and avoids the waste of ineffective maintenance costs.

[0039] (2) This invention also establishes differentiated fault interval division rules for single and multiple anomaly detection point scenarios. Combining the physical topology data and physical parameters of the optical fiber line twin map, the specific coordinates of the fault point are deduced through the transmission simulation model, realizing the accurate implementation from "interval positioning" to "point positioning". It also automatically matches the preset repair standards, monitors the parameter compliance status in the repair process in real time, realizes the dynamic adjustment and verification of the repair plan, reduces the difficulty of manual operation, and improves the level of intelligent operation and maintenance. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings;

[0041] Figure 1 This is a partial system block diagram of the present invention;

[0042] Figure 2 This is a system flowchart of Embodiment 2 of the present invention;

[0043] Figure 3 This is a reference diagram for the mapping relationship library analysis of this invention. Detailed Implementation

[0044] 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.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0046] Example 1: Please refer to Figures 1 to 3 As shown, this invention is a remote positioning and control system for optical fiber line fault diagnosis, including an optical fiber line management center, a deployment and acquisition module, a marking and association module, an anomaly verification module, a segment division module, an interval positioning module, and an execution control module. The optical fiber line management center has bidirectional communication connections with the deployment and acquisition module and the marking and association module, a unidirectional communication connection with the segment division module, a unidirectional communication connection with the anomaly verification module, a unidirectional communication connection with the optical fiber line management center, a unidirectional communication connection with the interval positioning module, and a unidirectional communication connection with the execution control module.

[0047] The deployment acquisition module collects optical signal characteristic parameters of the target optical fiber line in real time by deploying multiple detection points along the target optical fiber line. The optical signal characteristic parameters include optical power, optical wavelength, optical reflectivity, etc. Each detection point is equipped with an optical fiber sensor.

[0048] The collected optical signal feature parameters are preprocessed to obtain the initial optical signal dataset, which is then sent to the fiber optic line management center for storage. The preprocessing includes noise reduction and enhancement.

[0049] The labeling and association module is used to perform fault identification, anomaly labeling, and association construction analysis on the initial optical signal dataset. The specific fault identification, anomaly labeling, and association construction analysis process is as follows:

[0050] S1: Input the initial optical signal dataset into the pre-set fault identification model and output the fault type identification result (such as fiber breakage, abnormal optical signal attenuation, etc.).

[0051] S2: Call the initial optical signal dataset (including standardized data such as optical power and optical attenuation) of each detection point;

[0052] S3: Perform discrimination processing on the initial optical signal dataset of each detection point. If the average value of the parameter obtained from three consecutive detections in the initial optical signal dataset exceeds the preset threshold range, the corresponding detection point is judged as an abnormal detection point.

[0053] S4: Retrieve historical fault data and fault characteristic parameters (such as abnormal values, changing trends, etc.) of the optical fiber line. After preprocessing (such as noise reduction, enhancement, etc.) based on the historical fault data and fault characteristic parameters of the optical fiber line, construct a standardized fault type-core characteristic parameter mapping relationship library, clarify the key abnormal parameters and parameter change rules corresponding to each fault type, and send the fault type-core characteristic parameter mapping relationship library to the optical fiber line management center for storage.

[0054] The anomaly verification module is used to perform feature matching and anomaly labeling feedback analysis on the parameter features extracted from anomaly detection points. The specific feature matching and anomaly labeling feedback analysis process is as follows:

[0055] Extracting parameter features of anomaly detection points: For the marked anomaly detection points, extract all abnormal parameters that exceed the threshold, and record the parameter name, abnormal value, trend of change (such as sudden rise, sudden drop, continuous increase, fluctuation, etc.) and magnitude of change.

[0056] Matching degree calculation: Based on the fault type-core feature parameter mapping relationship library, a weighted matching algorithm is used to calculate the matching degree between abnormal parameters and the output results of the fault diagnosis model. The specific weighted matching algorithm is as follows:

[0057] Obtain the current output fault type identification result and set the fault type in the fault type identification result as the target fault type;

[0058] Based on the matching between the target fault type and the fault type-core feature parameter mapping relationship library, all core associated feature parameters corresponding to the target fault type in the fault type-core feature parameter mapping relationship library are filtered.

[0059] If the abnormal parameters of an anomaly detection point contain a certain core related parameter, and the parameter change pattern is consistent with the description in the mapping library, it is counted as a matching item;

[0060] If the abnormal parameter contains the core related parameter, but the change pattern is inconsistent (e.g., a fiber break should correspond to a sudden drop in optical power, but in reality it is a sudden increase in optical power), it is counted as a mismatch item.

[0061] If the abnormal parameter does not include the core related parameter, it is counted as a missing item;

[0062] Obtain the pre-set weight coefficients corresponding to each matching item, and set the sum of the pre-set weight coefficients corresponding to each matching item as the cumulative matching weight;

[0063] Matching degree = Cumulative matching weight / Total weight of core association parameters of the target fault type (total weight is the sum of matching weights of all core parameters under this fault type);

[0064] Judgment of matching results: Set a matching degree threshold. If the calculated matching degree is greater than or equal to the matching degree threshold, it is determined that the abnormal parameter matches the fault type, and the corresponding abnormal detection point is set as an effective abnormal detection point. If the matching degree is less than the matching degree threshold, it is determined that the parameter is abnormal and has no relation to the fault type, which is a false alarm, and the abnormal label of the detection point is removed.

[0065] Based on the matching results, valid anomaly detection points are determined, a set of valid anomaly detection points is constructed, and the set of valid anomaly detection points is sent to the fiber optic line management center for storage.

[0066] Example 2: The segmentation module performs anomaly region segmentation and visual labeling analysis on the set of valid anomaly detection points. The specific anomaly region segmentation and visual labeling analysis process is as follows:

[0067] The set of valid anomaly detection points is initially divided. If the set of valid anomaly detection points contains a single valid anomaly detection point, it is determined to be a single anomaly detection point scenario. If the set of valid anomaly detection points contains multiple valid anomaly detection points, it is determined to be a multi-anomaly detection point scenario.

[0068] In a single anomaly detection point scenario: if there is only one valid anomaly detection point, let A be the valid detection point, then the fault interval is the two consecutive segments formed by the detection point, the preceding adjacent detection point (let's call it A-1), and the subsequent adjacent detection point (let's call it A+1), namely the [A-1,A] segment and the [A,A+1] segment.

[0069] If the valid detection point for the anomaly is at the beginning of the line (without A-1), then the fault range is only the [A,A+1] segment; if it is at the end of the line (without A+1), then the fault range is only the [A-1,A] segment.

[0070] Multi-anomaly detection point scenario: If there are multiple consecutive valid anomaly detection points, the first valid anomaly detection point is denoted as A, and the last valid anomaly detection point is denoted as A+n, where n is a natural number greater than zero. Then the fault interval is the continuous segment between the preceding detection point (A-1) of the first valid anomaly detection point and the subsequent detection point (A+n+1) of the last valid anomaly detection point, i.e. the [A-1, A+n+1] segment.

[0071] If the parameter of a certain detection point (A) does not reach the abnormal threshold, but the difference between the parameter of the adjacent detection point (A-1) exceeds the gradient threshold (e.g., the difference in light attenuation exceeds 2dB / km), and shows a continuous gradient change trend, then the fault interval is determined to be the [A-1,A] segment and marked as the gradient abnormal interval.

[0072] Output the core information of the fault range, including the range number, the identifiers and coordinates of the starting and ending detection points, the range length, the abnormal detection point number, and the associated fault type;

[0073] The core information of the faulty section is synchronized to the pre-constructed fiber optic line twin map. The faulty section is marked in yellow in the fiber optic line twin map, and the non-faulty section is marked in green in the fiber optic line twin map, thus obtaining the initial faulty section visualization.

[0074] The fiber optic twin map includes the following core components:

[0075] Based on the physical topology of the target optical fiber line, a twin map of the optical fiber line is constructed. The physical topology includes physical information such as the distribution of detection points, optical fiber direction, connector location, connector deployment, and geographical environment along the line (such as terrain, buildings, pipeline routes), and labels the physical parameters of each component (such as optical fiber type, length, refractive index, connector loss threshold, etc.).

[0076] The pre-processed data and fault type identification results of each detection point are synchronized in real time, and the line operation status is presented in a visual way in the map (such as color marking: normal sections are green and the preliminary fault location section is yellow);

[0077] It supports fault information association query. By clicking on the initial fault location interval in the map, you can view the optical signal characteristic parameter change curve, historical fault records, maintenance records and other related data in that interval, which provides intuitive support for subsequent accurate location and fault analysis. Through the fiber optic line twin map, the fault location, fault type and line physical entity are digitally mapped, which improves the visualization and intelligence level of fault management.

[0078] Within the initially determined fault zone, combining the topology data and physical parameters of the fiber optic line twin map, a precise location algorithm is activated to calculate the specific coordinates of the fault point, thus obtaining its exact location. The zone location module is used to perform zone fault location marking analysis on the initial fault zone visualization. The specific zone fault location marking analysis process is as follows:

[0079] By calling the optical fiber physical parameters (such as fiber refractive index N, line length L, and fiber core diameter D) and topology (such as straight segments and curved segments) corresponding to the fault section in the optical fiber line twin map, a transmission simulation model is constructed.

[0080] Extract the topology data within the fault section from the optical fiber line twin map, identify the path components (length of straight segment L1, L2..., radius of curvature of curved segment R1, R2..., number of joints and location coordinates P1, P2...), and associate the corresponding optical fiber physical parameters of each unit (such as the preset correction value of refractive index NX of curved segment and the interface refractive index JZ at the joint).

[0081] Based on the pre-calibrated transmission simulation model, the abnormal characteristic parameters of the input optical signal (such as the abnormal reflectivity peak position P) are used. a The propagation process of the abnormal optical signal in each path unit is simulated by the transmission delay time increment Δt.

[0082] Straight line segment: Calculate the path according to the uniform propagation model (s=v×t, where v=c / n, c is the speed of light in vacuum);

[0083] Curved section: Considering the path offset corresponding to the bending loss, the actual propagation distance is calculated according to the radius of curvature R (s'=s×(1+k / R), k is the preset bending loss coefficient).

[0084] At the junction: Based on the law of interface reflection / refraction, calculate the reflection path or transmission path of the abnormal signal to clarify the change in the propagation direction of the signal at the junction;

[0085] Let A and B be two adjacent detection points within the faulty section, with a distance of L_AB (extracted from the fiber optic line twin map), and the fault point be F. We need to reverse-calculate the distances D_AF (distance between F and A) and D_BF (distance between F and B, D_BF = L_AB - D_AF). The specific steps are as follows:

[0086] The total transmission time of the abnormal optical signal from detection point A to fault point F and then reflected back to A is obtained from the transmission simulation model: t_A = 2 × D_AF / v (v = c / N).

[0087] The actual acquired transmission delay time increment Δt = t_A - t_0 (t_0 is 1 / 2 of the normal signal transmission time from A to B and then back to A, i.e., t_0 = L_AB / v).

[0088] Derivation of the simultaneous formulas: D_AF=(Δt×v+t_0×v) / 2=(Δt×c / N+L_AB) / 2;

[0089] If there are multiple path units (such as those containing bends and joints), then t_A is decomposed into the sum of the transmission times of each unit (t_A = t1 + t2 + ...), the propagation distance of each unit is calculated, and finally the sum is obtained to get D_AF;

[0090] By combining the latitude and longitude coordinates of the detection points corresponding to the fault section in the fiber optic line twin map and the line direction, the specific location of the fault point is marked in the fiber optic line twin map and the latitude and longitude coordinates are labeled to obtain the fault location map.

[0091] The execution control module is used to respond to the fault location map and display it immediately, and at the same time select the corresponding preset standard (such as "home broadband splicing repair standard") according to the fault type (such as "breakpoint splicing").

[0092] After the engineer completes the fault repair (such as splicing, adjusting the line, replacing the jumper), the existing fiber fault repair remote control module deployed at one end of the target fiber line is controlled to work, and at the same time, the fault repair verification command uploaded from the application terminal (mobile phone / PDA) is received.

[0093] Automatically switch the fiber optic fault repair remote control module to optical receiving mode and continuously collect optical receiving power and signal stability data;

[0094] The collected data is compared with preset standards in real time, and the current status of the data is dynamically displayed on the terminal interface, such as received optical power -15dBm, meeting the standard, signal fluctuation 0.2dBm, meeting the standard, etc.

[0095] If any parameter exceeds the standard, such as the received optical power -30dBm, which is not up to standard, an immediate pop-up warning will be issued. The pop-up warning will include the name of the parameter that exceeds the standard and its corresponding value.

[0096] Based on the pop-up warning, the repair plan for the target fiber optic line is adjusted (such as re-splicing, line reorganization). After the adjustment, click to re-verify until all parameters meet the standards. A visual verification report is generated and displayed immediately. The visual verification report includes the repair results (qualified / unqualified), parameter values, etc.

[0097] In summary, this system employs a closed-loop mechanism encompassing data acquisition, preprocessing, fault identification, and anomaly verification. It utilizes a fault identification model to initially determine the fault type, and a weighted matching algorithm to accurately verify anomaly detection points. This effectively filters false alarms caused by parameter fluctuations, significantly improving the accuracy and reliability of fault identification and avoiding wasted maintenance costs. Furthermore, it establishes differentiated fault zone division rules for single and multiple anomaly detection point scenarios. Combining the physical topology data and physical parameters of the fiber optic line twin map, it uses a transmission simulation model to infer the specific coordinates of the fault point, achieving precise "zone location" to "point location." It also automatically matches preset repair standards, monitors parameter compliance during the repair process in real time, and enables dynamic adjustment and verification of repair plans, reducing the difficulty of manual operation and improving the level of intelligent operation and maintenance.

[0098] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0099] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0100] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An optical fiber line fault diagnostic remote positioning control system, characterized by, It includes a fiber optic line management center, a deployment and acquisition module, a marking and association module, an anomaly verification module, a segment division module, an interval positioning module, and an execution control module; The deployment acquisition module collects optical signal characteristic parameters of the target optical fiber line in real time by deploying multiple detection points along the target optical fiber line. The collected optical signal characteristic parameters are preprocessed to obtain an initial optical signal dataset, which is then sent to the optical fiber line management center for storage. The labeling and association module is used to perform fault identification, anomaly labeling, and association construction analysis on the initial optical signal dataset, and to obtain anomaly detection points and a standardized fault type-core feature parameter mapping relationship library; The anomaly verification module is used to perform feature matching and anomaly labeling feedback analysis on the parameter features of the extracted anomaly detection points to obtain a set of valid anomaly detection points; The segment division module performs abnormal region division and visualization marking analysis on the set of valid abnormal detection points to obtain an initial fault segment visualization. The interval location module is used to perform interval fault location marking analysis on the initial fault section visibility chart to obtain the fault location map; The execution control module is used to respond to the fault location map and display it immediately. At the same time, it realizes the process of comparing and analyzing the data collected after fault repair with the preset standards until all parameters meet the standards, and generates a visual verification report and displays it immediately.

2. A fiber optic line fault diagnostic remote positioning control system according to claim 1, wherein, The analysis process for the tagging and association modules is as follows: S1: Input the initial optical signal dataset into the pre-set fault identification model and output the fault type identification result; S2: Call the initial optical signal dataset for each detection point; S3: Perform discrimination processing on the initial optical signal dataset of each detection point. If the average value of the parameter obtained from three consecutive detections in the initial optical signal dataset exceeds the preset threshold range, the corresponding detection point is judged as an abnormal detection point. S4: Retrieve historical fault data and fault characteristic parameters of the optical fiber line. Based on the preprocessing of historical fault data and fault characteristic parameters of the optical fiber line, construct a standardized fault type-core characteristic parameter mapping relationship library.

3. The fiber optic line fault diagnosis remote positioning control system according to claim 1, characterized in that, The analysis process of the anomaly verification module is as follows: Based on the fault type-core feature parameter mapping relationship library, a weighted matching algorithm is used to calculate the matching degree between abnormal parameters and the output results of the fault diagnosis model; Set a matching degree threshold, compare the matching degree with the matching degree threshold to obtain the valid detection points of anomalies, and construct a set of valid detection points of anomalies based on the valid detection points of anomalies.

4. The fiber optic line fault diagnosis remote positioning control system according to claim 3, characterized in that, The matching degree acquisition and analysis process is as follows: Obtain the current output fault type identification result, set the fault type in the fault type identification result as the target fault type, if the abnormal parameter of the abnormal detection point contains a certain core related parameter, and the parameter change pattern is consistent with the description in the mapping library, it is counted as a matching item, obtain the pre-set weight coefficient corresponding to each matching item, and set the sum of the pre-set weight coefficients corresponding to each matching item as the cumulative matching weight; Matching degree = cumulative matching weight / total weight of core associated parameters of the target fault type. The total weight is the sum of the matching weights of all core parameters under this fault type.

5. The fiber optic line fault diagnosis remote positioning control system according to claim 1, characterized in that, The analysis process of the segmentation module is as follows: the set of valid abnormal detection points is initially divided. If the set of valid abnormal detection points contains a single valid abnormal detection point, it is determined to be a single abnormal detection point scenario. If the set of valid abnormal detection points contains multiple valid abnormal detection points, it is determined to be a multi-abnormal detection point scenario.

6. The fiber optic line fault diagnosis remote positioning control system according to claim 5, characterized in that, In the single anomaly detection point scenario: if there is only one valid anomaly detection point, denoted as A, then the fault interval is the two continuous segments formed by the detection point and the preceding adjacent detection point, denoted as A-1, and the subsequent adjacent detection point, denoted as A+1, namely the [A-1,A] segment and the [A,A+1] segment. If the valid detection point for the anomaly is at the beginning of the line, i.e. there is no A-1, then the fault range is only the [A,A+1] section; if it is at the end of the line, i.e. there is no A+1, then the fault range is only the [A-1,A] section.

7. The fiber optic line fault diagnosis remote positioning control system according to claim 5, characterized in that, In the scenario with multiple anomaly detection points: if there are multiple consecutive valid anomaly detection points, the first valid anomaly detection point is denoted as A, and the last valid anomaly detection point is denoted as A+n, where n is a natural number greater than zero. Then the fault interval is the continuous segment between the preceding detection point A-1 of the first valid anomaly detection point and the subsequent detection point A+n+1 of the last valid anomaly detection point, i.e., the [A-1, A+n+1] segment. If the parameter of a certain detection point A does not reach the abnormal threshold, but the difference between the parameter of the adjacent detection point A-1 exceeds the gradient threshold and shows a continuous gradient change trend, then the fault interval is determined to be the [A-1,A] segment and marked as the gradient abnormal interval. The core information of the faulty section is synchronized to the pre-constructed fiber optic line twin map. The faulty section is marked in yellow in the fiber optic line twin map, and the non-faulty section is marked in green in the fiber optic line twin map, thus obtaining the initial faulty section visualization.

8. The fiber optic line fault diagnosis remote positioning control system according to claim 7, characterized in that, The analysis process of the interval positioning module is as follows: The optical fiber physical parameters and topology corresponding to the fault section in the optical fiber line twin map are called to construct a transmission simulation model; the topology data within the fault section is extracted from the optical fiber line twin map, the path component units are identified, and the optical fiber physical parameters corresponding to each unit are associated. Based on the pre-calibrated transmission simulation model, the abnormal optical signal characteristic parameters are input, and the propagation process of the abnormal optical signal in each path unit is simulated. Let A and B be two adjacent detection points within the fault zone, with a distance of L_AB, and the fault point be F. We need to reverse the distances D_AF and D_BF, where D_BF = L_AB - D_AF, to obtain the latitude and longitude coordinates of the fault point, and finally output the fault location map.

9. The fiber optic line fault diagnosis remote positioning control system according to claim 1, characterized in that, The analysis process of the execution control module is as follows: After the engineer completes the fault repair, the existing fiber optic fault repair remote control module deployed at one end of the target fiber optic line is put into operation, while receiving fault repair verification instructions uploaded from the application terminal. The system automatically switches the fiber optic fault repair remote control module to the light receiving mode, continuously collects light receiving power and signal stability data, compares the collected data with preset standards in real time until all parameters meet the standards, generates a visual verification report and displays it immediately.

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