Optical power monitoring and fault testing linkage quick response method and system

Abstract

The invention relates to the technical field of communication, and discloses an optical power monitoring and fault testing linkage quick response method and system, and the method comprises the steps: receiving optical power monitoring data of an optical power monitoring service, and generating an optical power change track point sequence; establishing a three-layer anchor point network structure according to the track point sequence, and generating a decision link state mapping matrix based on the three-layer anchor point network structure; receiving OTDR test service feedback information, generating a composite time sequence identifier, and executing association matching verification with the decision link state mapping matrix to obtain qualified feedback information; constructing a time sequence reconstruction buffer area based on the qualified feedback information, executing switching synchronous control, receiving a switching completion signal of the OLP switching service, calculating response track characteristics based on the switching completion signal, and executing adaptive decision parameter adjustment; according to the invention, through spatialization state management and intelligent time sequence control, the response efficiency and coordination precision of distributed optical network fault processing are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, more particularly, the present application relates to a method and system for optical power monitoring and fault test linkage fast response. BACKGROUND

[0002] With the continuous expansion of fiber-optic communication network scale and the increasing complexity of service demand, optical power monitoring and fault handling system is developing towards distributed and intelligent direction, among which micro-service architecture is widely adopted due to its good scalability and modularization characteristics.

[0003] In the prior art, a power grid operation method and device, medium, equipment and product disclosed in Chinese patent application No. CN118868257A select corresponding strategies to realize power self-coordination based on a preset autonomic power supply model in normal operation state, adopt island operation mode for short-time power supply in fault operation state, and realize black start through source-load-storage coordination. Chinese patent application No. CN118890092A discloses an optical power monitoring system and method applied to optical fiber, which analyzes the stable stage of optical power data and sets state boundary through components such as optical power state identification module, abnormal signal detection module and state conversion analysis module, identifies and labels abnormal signals by threshold division, thereby improving fault detection accuracy and response speed.

[0004] However, when the existing optical power monitoring system is distributedly deployed by adopting Spring-Cloud micro-service architecture, since optical power monitoring service, OTDR test service and OLP switching service are distributed on different physical nodes, when the optical power monitoring service detects power anomaly and generates a trigger signal, the signal needs to be transmitted through network to reach OTDR test service and OLP switching service respectively. Due to network delay difference of each micro-service node, uneven processing load and service restart and other factors, the time of the same trigger signal reaching different services may be out of order. For example, when the optical power drops from-20dBm to-35dBm to trigger fault detection, the ideal processing sequence should be “monitoring trigger→OTDR test→OLP switching”, but in fact, the OLP switching service may receive the signal before the OTDR test service, thereby destroying the timing logic of the standard decision link and causing the transmitting and receiving light switch to fail to complete synchronous switching within the required 25ms time difference range, ultimately affecting the reliability of the entire linkage response mechanism and the accuracy of fault handling. SUMMARY

[0005] The application is mainly applied to the fault monitoring and automatic protection scene of large optical fiber communication network, and is particularly suitable for distributed optical network management system adopting Spring-Cloud micro service architecture, such as backbone network, metropolitan area network and enterprise-level optical fiber private network of a telecom operator, and other communication infrastructures requiring high reliability and fast fault response. In order to overcome the above-mentioned defects of the prior art, the application provides an optical power monitoring and fault test linkage fast response method and system, which spatializes the monitoring data by establishing a three-dimensional coordinate system, realizes distributed state coordination by constructing a three-layer anchor point network structure, and ensures accurate synchronous execution of switching instructions by using a time sequence reconstruction buffer. The method effectively solves the problem of time sequence disorder of data flow under the micro service architecture, significantly improves the response speed and coordination accuracy of optical network fault processing, and ensures the reliability of the linkage response mechanism in the distributed environment.

[0006] In order to achieve the above-mentioned purpose, the application provides the following technical scheme:

[0007] An optical power monitoring and fault test linkage fast response method, comprising:

[0008] Receiving optical power monitoring data of an optical power monitoring service in a Spring-Cloud micro service architecture, and generating an optical power change trajectory point sequence;

[0009] Establishing a distributed node state anchor point grid according to the optical power change trajectory point sequence, and constructing a three-layer anchor point network structure;

[0010] Generating a decision link state mapping matrix based on the three-layer anchor point network structure;

[0011] Receiving feedback information of an OTDR test service in the Spring-Cloud micro service architecture, generating a composite time sequence identifier, and performing associated matching verification with the decision link state mapping matrix to obtain qualified feedback information;

[0012] Receiving a switching operation expected target of an OLP switching service in the Spring-Cloud micro service architecture, calculating an optimal time sequence path based on the qualified feedback information, constructing a time sequence reconstruction buffer based on the optimal time sequence path, and performing switching synchronization control based on the time sequence reconstruction buffer;

[0013] Receiving a switching completion signal of the OLP switching service, marking a linkage response completion point based on the switching completion signal, calculating response trajectory characteristics based on the linkage response completion point, and performing adaptive decision parameter adjustment according to the response trajectory characteristics.

[0014] Further, the optical power monitoring service, the OTDR test service and the OLP switching service are cooperatively linked.

[0015] Further, the optical power monitoring data comprises optical power values, monitoring time stamps and optical cable physical positions;

[0016] The method for generating the optical power change trajectory point sequence comprises:

[0017] Determine the spatial position of the optical power monitoring point, set the optical power monitoring point as the origin, set the direction of the increase of the optical power value as the positive direction of the Z axis, set the actual laying direction of the physical extension of the optical cable as the positive direction of the X axis, set the direction of time lapse as the positive direction of the Y axis, and establish an optical power change trajectory three-dimensional coordinate system;

[0018] Mark the boundary of the three-dimensional decision area in the optical power change trajectory three-dimensional coordinate system, and generate the three-dimensional decision area; record the optical power change trajectory point sequence when the optical power monitoring data enters the three-dimensional decision area.

[0019] Further, the three-layer anchor point network structure is a parallel structure.

[0020] Further, the method for constructing the three-layer anchor point network structure comprises:

[0021] Equidistantly divide the three-dimensional decision area to obtain n grid units, and n is the total number of grid units; calculate the spatial size and geometric center point coordinates of each grid unit, assign a unique grid number to each grid unit, and establish an index mapping relationship table of grid numbers and geometric center point coordinates;

[0022] Based on the grid number and geometric center point coordinate mapping relationship table, allocate colorized state anchors in the grid units to construct a three-layer anchor point network structure.

[0023] Further, the method for allocating colorized state anchors in the grid units to construct a three-layer anchor point network structure comprises:

[0024] Allocate red monitoring anchors for the optical power monitoring service to form a red monitoring anchor network;

[0025] Allocate blue test anchors for the OTDR test service to form a blue test anchor network;

[0026] Allocate green switching anchors for the OLP switching service to form a green switching anchor network;

[0027] The red monitoring anchor network, the blue test anchor network and the green switching anchor network constitute a three-layer anchor point network structure.

[0028] Further, the method for generating the decision link state mapping matrix comprises:

[0029] Obtain the anchor activation state distribution in the three-layer anchor point network structure;

[0030] According to the anchor activation state distribution, a decision link state mapping matrix is established; the anchor activation state distribution refers to the activation state distribution of red monitoring anchors, blue test anchors and green switching anchors.

[0031] Further, the method for obtaining the anchor activation state distribution in the three-layer anchor network structure comprises:

[0032] Based on the colorized state anchor, an anchor activation state conversion relationship is established;

[0033] Based on the anchor activation state conversion relationship, a neighboring grid anchor cascade propagation relationship is set;

[0034] Based on the anchor activation state conversion relationship and the neighboring grid anchor cascade propagation relationship, the anchor activation state distribution in the three-layer anchor network structure is obtained.

[0035] Further, the method for obtaining the anchor activation state distribution in the three-layer anchor network structure comprises:

[0036] The test completion signal and the fault positioning result data of the OTDR test service are collected, the fault feature information is extracted, and a three-dimensional coordinate representation is converted and generated;

[0037] According to the three-dimensional coordinate representation of the fault feature information, a composite time sequence identifier is generated;

[0038] Based on the composite time sequence identifier, an associated matching verification is performed with the decision link state mapping matrix;

[0039] Based on the associated matching verification result, feedback information quality evaluation and screening are performed, and qualified feedback information is screened out.

[0040] An optical power monitoring and fault test linkage fast response system is used to implement the above-mentioned optical power monitoring and fault test linkage fast response method, and the system comprises:

[0041] A trajectory monitoring module is used to receive optical power monitoring data of an optical power monitoring service in a Spring-Cloud microservice architecture, and generate an optical power change trajectory point sequence;

[0042] A network construction module is used to establish a distributed node state anchor grid according to the optical power change trajectory point sequence, and construct a three-layer anchor network structure;

[0043] A mapping module is used to generate a decision link state mapping matrix based on the three-layer anchor network structure;

[0044] The feedback verification module is used for receiving feedback information of an OTDR test service in the Spring-Cloud microservice architecture, generating a composite time sequence identifier, and performing associated matching verification with a decision link state mapping matrix to obtain qualified feedback information.

[0045] The switching control module is used for receiving a switching operation expected target of an OLP switching service in the Spring-Cloud microservice architecture, calculating an optimal time sequence path based on the qualified feedback information, constructing a time sequence reconstruction buffer based on the optimal time sequence path, and performing switching synchronization control based on the time sequence reconstruction buffer.

[0046] The parameter adjustment module is used for receiving a switching completion signal of the OLP switching service, marking a linkage response completion point based on the switching completion signal, calculating response trajectory features based on the linkage response completion point, and performing adaptive decision parameter adjustment according to the response trajectory features.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] The present application converts optical power monitoring data into a trajectory point sequence by constructing a three-dimensional coordinate system, realizes unified management of three dimensions of time, space and power, and provides a geometrized mathematical basis for state coordination in a distributed microservice environment; by establishing a three-layer anchor network structure and a decision link state mapping matrix, the abstract microservice state is converted into a quantifiable mathematical expression, solving the technical problems of scattered state information and difficult coordination under the Spring-Cloud architecture; through the associated matching verification mechanism of the composite time sequence identifier, the accurate correspondence between the OTDR test feedback information and the system state is ensured, effectively avoiding the problem of data flow time sequence disorder caused by network delay and uneven node load; through the calculation of the optimal time sequence path and the construction of the time sequence reconstruction buffer, intelligent scheduling and accurate time sequence control of the optical line protection switching instruction are realized, ensuring the synchronous execution of the receiving and transmitting optical switches; through response trajectory feature analysis and adaptive decision parameter adjustment, a closed-loop optimization mechanism of system performance is established, so that the entire linkage response system has the ability of continuous self-improvement, fundamentally improving the reliability, response speed and coordination accuracy of distributed optical network fault processing. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0050] Figure 1A flow chart of a light power monitoring and fault test linkage fast response method provided for an embodiment of the present application is shown in FIG. 1.

[0051] Figure 2 A flow chart of step S10 provided for an embodiment of the present application is shown in FIG. 2.

[0052] Figure 3 A flow chart of step S20 provided for an embodiment of the present application is shown in FIG. 3.

[0053] Figure 4 A flow chart of step S22 provided for an embodiment of the present application is shown in FIG. 4.

[0054] Figure 5 A structure schematic diagram of a three-layer anchor point network structure provided for an embodiment of the present application is shown in FIG. 5.

[0055] Figure 6 A flow chart of step S30 provided for an embodiment of the present application is shown in FIG. 6.

[0056] Figure 7 A flow chart of step S31 provided for an embodiment of the present application is shown in FIG. 7.

[0057] Figure 8 A principle schematic diagram of an anchor point activation state conversion relationship provided for an embodiment of the present application is shown in FIG. 8.

[0058] Figure 9 A principle schematic diagram of a neighboring mesh anchor point cascade propagation relationship provided for an embodiment of the present application is shown in FIG. 9.

[0059] Figure 10 A flow chart of step S40 provided for an embodiment of the present application is shown in FIG. 10.

[0060] Figure 11 A flow chart of step S44 provided for an embodiment of the present application is shown in FIG. 11.

[0061] Figure 12 A flow chart of step S50 provided for an embodiment of the present application is shown in FIG. 12.

[0062] Figure 13 A flow chart of step S51 provided for an embodiment of the present application is shown in FIG. 13.

[0063] Figure 14 A flow chart of step S52 provided for an embodiment of the present application is shown in FIG. 14.

[0064] Figure 15 A flow chart of step S60 provided for an embodiment of the present application is shown in FIG. 15.

[0065] Figure 16 A flow chart of step S61 provided for an embodiment of the present application is shown in FIG. 16.

[0066] Figure 17 A functional module diagram of a light power monitoring and fault test linkage quick response system provided by an embodiment of the present application is shown in the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0068] Embodiment 1

[0069] Referring to Figure 1 The embodiment provides a light power monitoring and fault test linkage quick response method, which comprises the following steps:

[0070] In step S10, light power monitoring data of a light power monitoring service in a Spring-Cloud microservice architecture is received, and a light power change trajectory point sequence is generated.

[0071] Further, as Figure 2 shown, step S10 comprises the following steps:

[0072] In step S11, a light power monitoring point space position is determined, the light power monitoring point is set as an origin, a direction in which a light power value increases is set as a positive direction of a Z axis, an actual laying direction of a physical extension of an optical cable is set as a positive direction of an X axis, and a direction of time lapse is set as a positive direction of a Y axis, so as to establish a light power change trajectory three-dimensional coordinate system.

[0073] Specifically, light power monitoring data of a Spring-Cloud microservice architecture is received, a light power monitoring point space position is determined, and a light power change trajectory three-dimensional coordinate system is established. The light power monitoring point is set as an origin of the light power change trajectory three-dimensional coordinate system, a direction in which a light power value increases is set as a positive direction of a Z axis, an actual laying direction of a physical extension of an optical cable is set as a positive direction of an X axis, and a direction of time lapse is set as a positive direction of a Y axis. The Spring-Cloud microservice architecture involves the cooperative linkage of three core business services of a light power monitoring service, an OTDR test service and an OLP switching service.

[0074] The Spring-Cloud microservice architecture is a software architecture pattern that decomposes an application into multiple small, independently deployed service units, each of which is responsible for a specific business function. In the present embodiment, the Spring-Cloud microservice architecture involves three core business services: an optical power monitoring service, an OTDR (Optical Time Domain Reflectometer) test service, and an OLP (Optical Line Protection) switching service. The optical power monitoring service is responsible for real-time collection of optical power values of each monitoring point in the optical cable network; the OTDR test service is responsible for fault positioning testing of the optical cable when an anomaly is detected; and the OLP switching service is responsible for performing optical path switching operations after confirming a fault. The optical power monitoring data includes optical power values, monitoring time stamps, and optical cable physical locations, among other key parameters. The optical power values are in units of dBm, i.e., decibels relative to one milliwatt, representing the logarithmic ratio of optical power relative to one milliwatt; the monitoring time stamps are time records accurate to the millisecond level; and the optical cable physical locations include geographic coordinates and location information in the optical cable network topology. The system obtains these monitoring data in real time through a data collection interface of the optical power monitoring device and determines the spatial location of the optical power monitoring point in the entire optical cable network.

[0075] In the process of establishing the three-dimensional coordinate system, the optical power monitoring point is set as the origin O (0, 0, 0) of the coordinate system. The reason for this setting is that the monitoring point is the trigger starting point of the entire linkage response system, and setting it as a spatial reference benchmark can simplify subsequent coordinate calculation and spatial positioning. The Z-axis represents the change in optical power, and when the optical power value increases, the Z-coordinate value increases; when the optical power value decreases, i.e., attenuation occurs, the Z-coordinate value decreases. For example, a normal optical power of -20 dBm corresponds to Z = 0, and an attenuation to -30 dBm corresponds to Z = -1.0, which is calculated according to the standardization coefficient of 10 dBm. The positive direction of the X-axis represents the actual laying direction of the optical cable, i.e., the direction of the optical cable extending from the monitoring point to the far end, and the X-coordinate value increases. The positive direction of the Y-axis represents the direction of time passing, and as the monitoring time increases, the corresponding Y-coordinate value increases. By generating an origin position identification code in the format of "MonitorOrigin_Main Optical Cable Number_Monitoring Device Serial Number", a mapping table of the origin position identification code and the physical coordinates is established, realizing the accurate correspondence between the abstract coordinate system and the actual physical location.

[0076] Step S12, marking the boundary of the three-dimensional decision area in the optical power change trajectory three-dimensional coordinate system to generate a three-dimensional decision area;

[0077] The stereoscopic decision area is a cuboid space with the coordinate origin as the geometric center, and the side length is N x M x P, where N represents the monitoring range of the optical power attenuation threshold, the unit is dBm, M represents the monitoring range of the optical cable distance, the unit is km, and P represents the monitoring range of the decision time window, the unit is s. Based on N, M, and P, the eight vertex coordinates of the stereoscopic decision area are calculated with the origin of the three-dimensional coordinate system as the geometric center.

[0078] For example, N can be set to 20 dBm, indicating that the system is concerned about the optical power variation range of ± 10 dBm; M can be set to 50 km, indicating the monitored optical cable length range; and P can be set to 300 s, indicating that the time window for decision response is 5 minutes. The eight vertex coordinates of the cuboid are: (25, 150, 10), (-25, 150, 10), (-25, -150, 10), (25, -150, 10), (25, 150, -10), (-25, 150, -10), (-25, -150, -10), (25, -150, -10). In the three-dimensional coordinate system, the above eight vertexes are connected to form a closed cuboid boundary, marking the spatial range of the stereoscopic decision area.

[0079] In the process of establishing the decision trigger condition, the system continuously monitors the three-dimensional coordinate position of the optical power data. When the coordinate point (x, y, z) of the monitored data satisfies the condition: |x|≤N / 2 and |y|≤P / 2 and |z|≤M, it is determined that the data point enters the stereoscopic decision area, triggering the subsequent decision state mapping process. This spatial trigger mechanism can consider the power variation, time factor and spatial distance in three dimensions, avoiding false triggering caused by single parameter fluctuation compared with the traditional single threshold trigger method.

[0080] Step S13, record the optical power change trajectory point sequence when the optical power monitoring data enters the stereoscopic decision area; each trajectory point contains three basic information: three-dimensional coordinate position, monitoring time stamp, and optical power value.

[0081] The recording of the trajectory point adopts real-time data stream processing technology. When the coordinate calculation result of the monitoring data meets the boundary constraint condition of the stereoscopic decision area, that is, the coordinate point (x, y, z) of the monitoring data meets |x|≤N / 2 and |y|≤P / 2 and |z|≤M, the system immediately creates a new trajectory point object and stores it into the trajectory sequence. The three-dimensional coordinate position is calculated by a coordinate conversion algorithm. The X coordinate value is equal to the physical distance of the optical cable divided by the distance normalization coefficient. The Y coordinate value is equal to the timestamp minus the reference time divided by the time normalization coefficient. The Z coordinate value is equal to the optical power value minus the reference power divided by the power normalization coefficient. The independent variable of the optical power change trajectory point sequence refers to the parameter that changes independently as input in the trajectory point sequence, including the X axis coordinate value and the Y axis coordinate value. The X axis coordinate value corresponds to the actual distance of the physical extension of the optical cable, reflects the change of the spatial position, and is a spatial independent variable independent of the optical power. The Y axis coordinate value corresponds to the standardized value of the time lapse, reflects the change of the time dimension, and is a time independent variable independent of the optical power. The dependent variable refers to the parameter that changes with the independent variable, that is, the Z axis coordinate value, which corresponds to the optical power value, and its change depends on the changes of the X axis and the Y axis, and is the core observation result variable in the trajectory point sequence. The optical power change trajectory point sequence is used to observe the dynamic change rule of the optical power value (Z axis) with the spatial position (X axis) and the time (Y axis), that is, the optical power fluctuation at different times and different optical cable positions, so as to capture the trajectory characteristics of the abnormal change of the optical power.

[0082] The Y axis time coordinate value is a standardized value based on the rules of the three-dimensional coordinate system, for example, the timestamp minus the reference time divided by the time normalization coefficient. It is a relative value in the coordinate system, used for spatial analysis and geometric positioning of the trajectory point, without actual time unit (or standardized unit), and is generated by standardized conversion of the timestamp, which is a derived variable of the timestamp. The monitoring timestamp is the original time record when the optical power monitoring data is collected, accurate to the millisecond level, such as "2025-08-01 12:34:56.789", which is an absolute time identifier, reflecting the real time of data collection, with actual time unit (year, month, day, hour, minute, second, millisecond), and is the original basis for the Y axis time coordinate value without standardized processing. The space position identification code adopts "Trajectory_X_coordinate value_Y_coordinate value_Z_coordinate value_timestamp", which ensures the uniqueness and traceability of each trajectory point. The linked list storage structure is implemented by a double-linked list, which supports sequential access and reverse access of the trajectory point, facilitating trajectory analysis and backtracking operation. The timestamp incremental sorting ensures the time continuity of the trajectory sequence, providing an ordered data basis for subsequent time series analysis.

[0083] Step S10 realizes the spatial expression and geometric management of the optical power monitoring data by establishing a three-dimensional coordinate system and a trajectory point sequence. The traditional optical power monitoring method can only provide time series data, lacks the ability of spatial dimension correlation analysis, and leads to insufficient fault positioning accuracy and prolonged response time. Step S10 maps the three dimensions of time, space, and power into a three-dimensional coordinate system, so that the originally scattered monitoring data has geometric characteristics and spatial relationships, providing a unified mathematical foundation for subsequent state analysis and decision-making. The introduction of the Spring-Cloud microservices architecture solves the problem of high service coupling and poor scalability in traditional monolithic systems, and realizes the independent deployment and elastic scaling of the monitoring, testing, and switching three core functions through service decoupling. The setting of the three-dimensional decision area establishes an intelligent triggering mechanism, avoiding the complex process of manually setting complex threshold rules in traditional methods, and automatically identifying abnormal states that need to be processed through spatial inclusion judgment. The generation of the trajectory point sequence converts discrete monitoring events into continuous spatial motion trajectories, providing rich feature information for fault pattern recognition and prediction analysis. The establishment of the three-dimensional coordinate system not only solves the data expression problem, but also creates conditions for multi-dimensional correlation analysis, enabling the system to consider multiple characteristics such as the amplitude, speed, and direction of power changes, significantly improving the accuracy of anomaly detection and the reliability of fault prediction.

[0084] Step S20, according to the optical power change trajectory point sequence, establishes a distributed node state anchor point grid, and constructs a three-layer anchor point network structure;

[0085] Further, as shown in Figure 3 , step S20 includes:

[0086] Step S21, equally divide the three-dimensional decision area to obtain n grid units, and n is the total number of grid units; calculate the spatial size and geometric center point coordinates of each grid unit, assign a unique grid number to each grid unit, and establish an index mapping relationship table between the grid number and the geometric center point coordinates;

[0087] Specifically, the grid division adopts a uniform division strategy, and the decision space of N×M×P is divided into a grid manner of Q×R×S, where Q represents the number of grid divisions in the X-axis direction, R represents the number of grid divisions in the Y-axis direction, and S represents the number of grid divisions in the Z-axis direction. The total number of grid units n = Q×R×S.

[0088] For example, for a decision space of 20dBm x 50km x 300s, Q = 10, R = 10, S = 10 can be set to obtain 1000 grid cells. The spatial size of each grid cell is (2dBm) x (5km) x (30s), representing a power variation range of 2dBm, a distance range of 5km, and a time window of 30s covered by each grid cell.

[0089] The geometric center point coordinate of each grid cell is calculated by the following formula: Wherein, i∈[1, Q], j∈[1, R], k∈[1, S] represent the index positions of the grid in the X, Y, Z axis directions respectively. For example, when i = 6, j = 6, k = 6, the geometric center point coordinate of the corresponding grid cell is (1dBm, 2.5km, 15s).

[0090] The system assigns a unique grid number to each grid cell, and the number format is "Grid_i_j_k", for example, "Grid_6_6_6". An index mapping relationship table of grid number and geometric center point coordinate is established to realize the bidirectional fast query of grid number and spatial position. The mapping relationship table is stored in a hash table data structure, taking the grid number as the key and the geometric center point coordinate as the value, supporting O(1) time complexity query operation.

[0091] In step S22, based on the index mapping relationship table of grid number and geometric center point coordinate, colorization state anchor points are distributed in the grid cells to construct a three-layer anchor point network structure.

[0092] Further, as shown in Figure 4 step S22 includes:

[0093] In step S221, a red monitoring anchor point is assigned to the optical power monitoring service based on the grid number and geometric center point coordinate mapping relationship table, and a red monitoring anchor point network is formed.

[0094] In step S222, a blue test anchor point is assigned to the OTDR test service based on the grid number and geometric center point coordinate mapping relationship table, and a blue test anchor point network is formed.

[0095] In step S223, a green switching anchor point is assigned to the OLP switching service based on the grid number and geometric center point coordinate mapping relationship table, and a green switching anchor point network is formed.

[0096] In step S224, the red monitoring anchor point network, the blue test anchor point network, and the green switching anchor point network constitute a three-layer anchor point network structure.

[0097] Please refer to Figure 5As shown, the colorized anchor point is a visual state representation method, which distinguishes different types of service node states by different colors. The red monitoring anchor point represents the state information of the optical power monitoring service, and the anchor point identification format is "RedAnchor_monitoring service node IP address_grid number", for example, "RedAnchor_192.168.1.100_Grid_6_6_6". Based on the index mapping relationship table of the grid number and the geometric center point coordinates, the specific process of allocating anchor points for each service is as follows: first, query the unique number of each grid unit and its geometric center point coordinates (X, Y, Z) through the mapping relationship table; then, when allocating the red monitoring anchor point for the optical power monitoring service, bind the anchor point to the center point coordinates of the corresponding grid unit, and the anchor point identification format is "RedAnchor_monitoring service node IP address_grid number" (such as "RedAnchor_192.168.1.100_Grid_6_6_6"), forming a red monitoring anchor point network; similarly, allocate the blue test anchor point for the OTDR test service, and the identification format is "BlueAnchor_test service node IP address_grid number", forming a blue test anchor point network; allocate the green switching anchor point for the OLP switching service, and the identification format is "GreenAnchor_switching service node IP address_grid number", forming a green switching anchor point network.

[0098] Each red monitoring anchor point establishes a binding relationship with a specific optical power monitoring service instance to ensure accurate correspondence of state information. The "specific" means that each red monitoring anchor point corresponds to only one specific and unique optical power monitoring service instance, which has a clear physical deployment location (such as a specific monitoring device in a machine room), network identification (such as a fixed IP address), and service ID, ensuring that the state information (such as abnormal optical power activation state) reflected by the anchor point can be accurately associated with the monitoring data collected by the instance, avoiding confusion of state information between different service instances, and ensuring the accuracy of state transmission and analysis. The initial state of the red monitoring anchor point is set to "standby state", with a value of 0.0, indicating that no abnormal optical power change has been detected in the grid cell. The blue test anchor point represents the state information of the OTDR test service, with an anchor point identification format of "BlueAnchor_TestServiceNodeIP Address_GridNumber". The OTDR test service is responsible for precise fault location testing after detecting abnormal optical power, by sending optical pulses and analyzing reflected signals to determine the fault point location. The initial state of the blue test anchor point is also set to "standby state", with a value of 0.0. The green switching anchor point represents the state information of the OLP switching service, with an anchor point identification format of "GreenAnchor_SwitchingServiceNodeIP Address_GridNumber". The OLP switching service is responsible for performing switching operations between primary and backup optical paths after confirming the fault, ensuring business continuity. The initial state of the green switching anchor point is set to "standby state", with a value of 0.0. In each grid cell, three colors of anchor points are deployed simultaneously, forming three independent but interrelated state monitoring layers. The first layer is the red monitoring anchor point network, responsible for initial detection of optical power abnormalities; the second layer is the blue test anchor point network, responsible for precise positioning of faults; and the third layer is the green switching anchor point network, responsible for execution control of optical path switching. The three-layer anchor point network structure is a parallel structure, i.e., the red monitoring anchor point network, the blue test anchor point network, and the green switching anchor point network form a three-layer anchor point network structure in parallel.

[0099] A communication interface is established between the three-layer anchor point network, and a publish-subscribe mode is used to realize the transmission of state information. When the state of an anchor point in a certain layer changes, the relevant anchor points in other layers are notified through an event publishing mechanism to ensure state synchronization between different services. For example, when a red monitoring anchor point detects an anomaly and activates, it sends a "test preparation" signal to the blue test anchor point and a "switch standby" signal to the green switching anchor point within the same grid cell.

[0100] Step S20 realizes the spatial management and visual control of Spring-Cloud microservice state by establishing a distributed node state anchor grid and a three-layer anchor network structure. The traditional distributed system state management method relies on centralized state storage and polling mechanism, which has problems such as large state update delay, poor system scalability, single point failure risk, etc. This step maps the abstract service state to a specific spatial location through the spatial division of the anchor grid, so that the state management has spatial locality and geometric intuitiveness, significantly improving the efficiency of state query and update. The parallel deployment of the three-layer anchor network solves the independence and coordination problem of different types of microservice state management, realizes the intuitive distinction of service type through color classification, and realizes the ordered control of service priority through hierarchical management. The grid-based spatial division establishes the propagation mechanism of state changes, and the state association between adjacent grids provides a spatial basis for fault diffusion analysis and impact assessment. The binding relationship between anchor points and microservice nodes realizes the accurate mapping of virtual state space and physical service instances, ensuring the authenticity of state information and the effectiveness of operation instructions. The establishment of anchor grid not only solves the problem of state management, but also creates new possibilities for load balancing and resource scheduling of distributed systems. By analyzing the anchor activation density and distribution pattern, the system can identify service hotspots and load imbalance, providing decision basis for dynamic resource allocation and service migration. The cooperative working mechanism of the three-layer network enables different types of services to work efficiently while maintaining independence, avoiding the system vulnerability caused by tight coupling between services in traditional methods, and significantly improving the reliability and fault tolerance of the overall system. The real-time synchronization and propagation mechanism of anchor state reduces the complexity of global state synchronization through spatial proximity constraints, improving the response speed and processing capacity of the system.

[0101] Step S30, based on the three-layer anchor network structure, generates a decision link state mapping matrix;

[0102] Further, as shown in Figure 6 Step S30 includes:

[0103] Step S31, obtain the anchor activation state distribution in the three-layer anchor network structure;

[0104] Further, as shown in Figure 7 Step S31 includes:

[0105] Step S311, based on the colorized state anchor, establish the anchor activation state transition relationship;

[0106] Step S312, based on the anchor activation state transition relationship, set the adjacent grid anchor cascading propagation relationship;

[0107] Step S313, based on the anchor activation state transition relationship and the adjacent grid anchor cascade propagation relationship, the anchor activation state distribution in the three-layer anchor network structure is obtained, and the anchor activation state distribution includes the activation state distribution of the red monitoring anchor, the blue test anchor and the green switching anchor.

[0108] Step S32, according to the anchor activation state distribution, a decision link state mapping matrix is established.

[0109] Specifically, the colorized state anchor includes a red monitoring anchor, a blue test anchor and a green switching anchor; the decision link state mapping matrix is a data structure for converting the state information in the distributed anchor network into a mathematical matrix form, and each element of the matrix represents the activation degree of a specific state combination. The establishment of the state mapping matrix needs to abstract the anchor activation state in the three-dimensional space into a numerical expression in the multi-dimensional mathematical space, realizing the conversion from the geometric space to the mathematical space. The anchor activation state distribution refers to the activation mode and distribution rule of the red monitoring anchor, the blue test anchor and the green switching anchor in the entire grid space.

[0110] In the optical cable network monitoring process, when the optical power monitoring service detects an abnormal state, the red monitoring anchor in the corresponding grid unit needs to be activated. The judgment basis of the abnormal state includes that the optical power value deviates from the normal range by more than a preset threshold, the optical power change rate exceeds the normal fluctuation range, the optical power value continuously decreases, etc. For example, when the optical power is monitored to suddenly decrease from -20dBm to -35dBm, the decrease amplitude reaches 15dB, and the system determines that it is an abnormal state. The state transition relationship defines the activation transmission rule and time sequence dependence relationship between anchors of different colors. Please refer to Figure 8 When the optical power monitoring service detects an abnormal state, the state of the red monitoring anchor in the corresponding grid unit is converted from the value 0.0 of the "standby state" to the value 1.0 of the "activated state", and the state transition adopts a step function to realize, and the specific method is: when the optical power monitoring service detects an abnormal state, the step function jumps from "0.0 (standby state)" to "1.0 (activated state)" at the moment of abnormal triggering, and remains 1.0 unchanged during the abnormal duration. The mathematical expression of the step function can be simplified as:

[0111]

[0112] Wherein, u(t) is the step function value, and such sudden change characteristics ensure the definiteness of state transition, avoid the state ambiguity caused by intermediate value, and meet the clear judgment requirement in fault detection.

[0113] The activated red monitoring anchor point sends a "test ready" state synchronization signal to the blue test anchor point in the same grid cell through an internal message passing mechanism, with a signal format of "SYNC_TEST_READY_Source Anchor ID_Target Anchor ID_Time Stamp", and sends a "switch standby" state synchronization signal to the green switching anchor point, with a signal format of "SYNC_SWITCH_STANDBY_Source Anchor ID_Target Anchor ID_Time Stamp". The target anchor ID refers to the unique identification of the anchor point receiving the synchronization signal, which is consistent with the anchor point identification rule of the corresponding service. If the target is a blue anchor point for the OTDR test service, the identification format is "BlueAnchor_Test Service Node IP Address_Grid Number", and if the target is a green anchor point for the OLP switching service, the identification format is "GreenAnchor_Switching Service Node IP Address_Grid Number". The target anchor ID is used to accurately locate the signal receiving object, ensuring that the signal can accurately reach the corresponding anchor point when the services in the same grid cell are cooperatively linked, and avoiding miscommunication across services or grids. The delay time parameter for signal transmission between anchor points is set to A milliseconds, where A represents the maximum allowed delay time range for state synchronization between anchor points. The setting of the delay time parameter A for signal transmission between anchor points needs to consider factors such as network transmission delay, service processing time, system response speed, etc. In practical applications, the value of A is usually set to be within the range of 50-200 milliseconds. For example, in a metropolitan area network environment, considering the network transmission delay of about 20 milliseconds, the service processing time of about 30 milliseconds, and the reserved buffer time of 50 milliseconds, the value of A can be set to 100 milliseconds. This parameter is obtained through statistical analysis of historical response data, and the upper limit value of the 95% confidence interval is taken as the maximum allowed delay time.

[0114] The cascading propagation relationship simulates the diffusion process of fault impact in space. When the anchor point of a certain grid cell is activated, the activation signal will propagate to its spatially adjacent grid cells, forming a propagation pattern similar to the diffusion of ripples. The definition of adjacent grid cells is based on the six-neighbor relationship in three-dimensional space, i.e., each grid cell has one adjacent grid in the positive and negative directions of the X-axis, the Y-axis, and the Z-axis, respectively, for a total of six adjacent grid cells. The propagation of the activation signal uses the communication mechanism between anchor points of the same color. The activation signal of the red monitoring anchor point only propagates to the red monitoring anchor point in the adjacent grid, and the propagation rules for the blue test anchor point and the green switching anchor point are similar, ensuring the independence of the propagation of different service type states. The diffusion range control parameter is set to B-layer grid, where B represents the upper limit of the number of layers that the activation source point diffuses outward, determining the spatial range of cascading propagation. The setting of B value needs to balance the accuracy of fault impact range and system computing overhead. In practical applications, the B value is usually set to 2-5 layers. For example, for a single-point optical fiber breakage fault, the impact range is relatively limited, and the B value can be set to 2 layers; for large-scale cable damage, the B value can be set to 5 layers. The B value is determined by analyzing the statistical data of the actual impact range in historical fault cases, and the number of layers covering 90% of the fault impact range is taken as the parameter value. The signal strength attenuation control of cascading propagation is established, and a linear attenuation model is used. The strength of the propagation signal decreases by a fixed step with the increase of the diffusion distance, and the initial value of the signal strength is set to 1.0. The signal strength decreases by C units for each layer of grid diffusion. The determination of C value is based on the transmission attenuation characteristics of optical signals in optical fibers, and the typical value is 0.2-0.3. For example, please refer to Figure 9As shown, when C = 0.25, the signal strength of the first layer neighborhood is 0.75, the second layer is 0.5, the third layer is 0.25, and the fourth layer is 0. This linear decay model simplifies the computational complexity while maintaining the reasonableness of the physical meaning. The propagation termination condition is controlled by the threshold D. When the signal strength decreases below D, the propagation stops. The setting of the D value needs to consider the noise tolerance and the discrimination degree of the effective signal. The D value is usually set in the range of 0.1-0.2, for example, when D = 0.15, it means that the signal strength below 15% no longer has practical significance, and stopping propagation can avoid invalid calculation and false triggering. It is worth noting that the propagation termination condition (threshold D) and the diffusion range control parameter (B layer grid) are dual constraints that are effective in parallel in the cascade propagation process, and the two are synergistic: the diffusion range control parameter B limits the maximum grid layer of the propagation from the spatial range, for example, B = 3 means that the diffusion is limited to the third layer grid, avoiding the waste of computing resources caused by unlimited signal diffusion; the propagation termination condition D limits the effective boundary of the propagation from the signal strength, for example, D = 0.15 means that the propagation stops when the signal strength is below 15%, ensuring that only signals with effective intensity can continue to diffuse, filtering out noise or invalid signals after attenuation. During the propagation process, if any of the following conditions is met, the diffusion is terminated: the diffusion layer reaches B layers (even if the signal strength is still higher than D); the signal strength decreases below D (even if the diffusion layer does not reach B layers). There is no absolute priority between the two, and the first one that meets the termination triggers the termination, which together ensures the efficiency and accuracy of the propagation process.

[0115] Based on the anchor activation state transition relationship and the adjacent grid anchor cascade propagation relationship, the complete anchor activation state distribution in the three-layer anchor network structure is calculated comprehensively. This process involves multiple rounds of iterative calculation until the entire network reaches a stable state. The iteration termination condition for the network to reach a stable state can be defined as follows: when the difference in the anchor activation state distribution of the three-layer anchor network structure in two consecutive iteration calculations meets all the following conditions, it is determined that the network has reached a stable state: the first condition is that the sum of the absolute values of the changes in the activation state values of all anchors in the two iterations or the maximum change value is less than the preset state change threshold, for example, 0.01, that is, the fluctuation of the activation state is within an acceptable range; the second condition is that the number of newly activated anchors is 0, and there is no state from activation to non-activation in the activated anchors; the third condition is that all signals being propagated have stopped propagating due to meeting the diffusion range control parameter B or the propagation termination condition D, and there is no incomplete propagation path. When the above conditions are met at the same time, it indicates that the activation state distribution of the anchors in the network no longer changes significantly, and the iteration calculation can be terminated. The calculation process uses a parallel processing architecture, and the anchor network of three colors updates the state simultaneously. The red monitoring anchor network updates the activation state according to the real-time monitoring data; the blue test anchor network updates according to the activation signal of the red monitoring anchor and its own test readiness state; the green switching anchor network updates according to the state information of the previous two layers and the availability of switching resources. This parallel processing method fully utilizes the computing resources of the distributed system and significantly improves the state update efficiency. The data structure of the anchor activation state distribution uses a sparse matrix representation, because in the normal operating state, most anchors are in standby state, and only a few anchors are activated. The sparse matrix only stores non-zero elements, i.e. activated anchors, which greatly reduces the storage space and computing overhead. For example, for a system of 1000 grid cells, if only 10 anchors are activated, the sparse matrix only needs to store 10 elements, instead of 1000.

[0116] The establishment of the state mapping matrix requires converting the state distribution information in the three-layer anchor network into a standardized matrix data structure. The dimensions of the matrix are designed as a three-dimensional structure of T x U x V, where the T dimension corresponds to the number of state categories of the optical power monitoring state change sequence, the U dimension corresponds to the number of state categories of the OTDR test state change sequence, and the V dimension corresponds to the number of state categories of the OLP switching state change sequence. The determination of the matrix dimensions needs to balance the expression ability and the computational complexity. The T dimension is usually set to 10-20, covering various optical power levels from normal state to severe failure; the U dimension is set to 5-10, representing different stages of OTDR testing; and the V dimension is set to 3-5, corresponding to the main states of OLP switching. For example, a typical configuration is T=15, U=8, V=4, generating a matrix of 480 state combination units. The numerical value of the matrix elements is represented in the continuous interval [0, 1], providing more rich state information than traditional binary representation. The value 0 represents that the corresponding state combination is completely inactive, 1 represents complete activation, and the intermediate value represents the degree of partial activation. This continuous numerical representation can capture the subtle changes of the system state, providing a basis for accurate control. The index table establishes the numerical mapping relationship between the matrix elements and the grid anchor activation state, and records the grid position set and anchor identification list corresponding to each matrix element, supporting fast lookup and reverse mapping of matrix elements to specific anchor states. The update of the state mapping matrix adopts an incremental update mechanism, only recalculating the matrix elements that have changed states, reducing the computational overhead and system load.

[0117] Step S30 realizes the mathematical expression and structured management of the distributed anchor point network state by establishing a decision link state mapping matrix. The traditional state management method of distributed system relies on centralized state storage and global state synchronization, which has problems such as difficulty in maintaining state consistency, limited system scalability, and low state query efficiency. Step S30 establishes the anchor activated state transition relationship, converts the complex inter-service coordination problem into clear state transition rules, eliminates the uncertainty of inter-service coordination, and improves the predictability and controllability of system response. The setting of cascading propagation relationship simulates the spatial diffusion characteristics of fault impact, enabling the system to predict the potential impact range of the fault and provide a scientific basis for preventive measures. The establishment of the state mapping matrix integrates the dispersed anchor point state information into a unified mathematical expression, providing a standardized data basis for subsequent state analysis, pattern recognition, and decision optimization. The cascading propagation mechanism of the anchor activated state not only solves the problem of identifying the scope of fault impact, but also creates a new load balancing strategy. By analyzing the propagation pattern and density distribution of the activated state, the system can identify hot and cold areas of service load, providing decision-making basis for dynamic resource scheduling and service migration. The multi-dimensional structure of the state mapping matrix enables the system to have the ability of multi-level state correlation analysis, enabling it to discover the implicit correlation between different service types, providing a new analysis dimension for system optimization and fault prediction. The matrix state expression method also creates conditions for the application of machine learning algorithms, enabling the system to achieve intelligent fault prediction and automatic parameter optimization through learning from historical state data, significantly improving the system's self-adaptation ability and operation efficiency.

[0118] Step S40, receiving feedback information of the OTDR test service in the Spring-Cloud microservice architecture, generating a composite time sequence identifier, and performing associated matching verification with the decision link state mapping matrix to obtain qualified feedback information;

[0119] Further, as shown in Figure 10 Step S40 includes:

[0120] Step S41, collecting the test completion signal and fault positioning result data of the OTDR test service, extracting fault feature information, and converting to generate a three-dimensional coordinate representation;

[0121] The physical characteristics and fault location of the optical fiber link are detected by sending optical pulses to the optical fiber and analyzing the reflected signals. The test completion signal of the OTDR test service contains rich test metadata. The time stamps of the test start time and the completion time are accurate to the millisecond level, which is used to calculate the test duration and evaluate the response efficiency. The physical distance position of the fault point in the optical cable is calculated by analyzing the time delay of the reflected signal, and the accuracy can reach meters. The optical fiber attenuation curve records the change of the optical power with distance along the entire link, and the reflection peak coordinates identify the possible fault point position. The extraction process of fault feature information involves signal processing and pattern recognition technology. The fault point type identification is determined by analyzing the waveform characteristics of the reflected signal, including optical fiber breakage (represented by strong reflection peak), fiber bending (represented by progressive attenuation), connector failure (represented by specific reflection mode), etc. The attenuation degree value is calculated by comparing the optical power levels before and after the fault, and the unit is dB. The reflection characteristic parameters include reflection coefficient, pulse width, rise time, etc., which together constitute the complete feature description of the fault. The three-dimensional coordinate conversion follows the coordinate system definition rules established in step S11. The X coordinate of the fault point is equal to the physical length of the optical cable from the fault point to the monitoring origin, and this distance is calculated by dividing the optical path measured by the OTDR by the refractive index of the optical fiber. For example, if the OTDR measures the optical path to be 30 km and the refractive index of the optical fiber is 1.5, the actual physical distance is 20 km, and the X coordinate value is 20. The Y coordinate of the fault point is equal to the time difference between the test completion time and the monitoring trigger time, reflecting the response time from detecting the anomaly to completing the fault positioning. The Z coordinate of the fault point is equal to the negative value of the optical power attenuation at the fault point, keeping consistent with the definition of the coordinate system.

[0122] Step S42, generating a composite time sequence identifier according to the three-dimensional coordinate representation of the fault feature information;

[0123] The composite time sequence identifier adopts a three-segment structure of "space coordinate_time sequence number_prefix state hash value", and each segment of information carries specific semantic content. The space coordinate segment records the accurate position of the fault point in the three-dimensional coordinate system, and the format is a continuous digital string "X coordinate value Y coordinate value Z coordinate value". The coordinate value is represented by a fixed-point number, with two digits after the decimal point, to ensure accuracy while controlling the length of the identifier. For example, "20.50120.75-15.30" indicates that the fault point is located at a position 20.5 km away from the origin, with a response time of 120.75 seconds and an attenuation of 15.3 dB. This compact representation facilitates the transmission and storage of the identifier. The time sequence number segment records the sequential position of the test feedback information in the entire decision link. The numbering of the time sequence number segment is derived from the time sequence counter generated by the system after the start of the linkage response process, which has no direct correlation with the three-dimensional coordinates of the fault feature information. The specific rules are as follows: when the optical power monitoring service detects an anomaly and triggers a linkage response, i.e., the optical power data enters the stereoscopic decision area, the system automatically starts the time sequence counting mechanism, taking the "monitoring trigger time" as the starting point, and assigns a unique number to each feedback information generated by the subsequent OTDR test service in chronological order. The numbering starts from 1 and is generated in an incremental rule, using a 6-digit integer format (such as "000001" "000002" … "999999"), supporting a maximum of 999999 event numbers. The time sequence number not only identifies the order of events, but also detects event loss and repetition. For example, if the feedback number 000156 is received, the next feedback number should be 000157, and if 000158 is received, it indicates that there is a missing event in between. The prefix state hash value segment is obtained by hashing the pre-monitoring state information that triggered the test. The hashing algorithm uses SHA-256 to generate a 64-bit hexadecimal string. The pre-state information includes key parameters such as the optical power value at the time of triggering, the monitoring point position, and the anomaly type. The role of the hash value is to ensure the correspondence between the test feedback and the triggering event, preventing false association. For example, two different triggering events, even if they occur at similar times and locations, will have completely different hash values, avoiding confusion.

[0124] Step S43, based on the composite time sequence identifier, performing association matching verification with the decision link state mapping matrix;

[0125] The association matching verification is performed to ensure the consistency of the test feedback information and the system state, preventing errors or outdated information from affecting decisions. The association matching verification is a multi-level verification process, including spatial matching verification, hash consistency verification, and timeliness verification. Spatial matching verification maps the spatial coordinate information in the composite time sequence identifier to the corresponding position in the decision link state mapping matrix. By calculating the index position of the spatial coordinate in the matrix, the corresponding matrix element value is found. If the matrix element value is greater than the activation threshold, it means that there is indeed an abnormal state that needs to be tested at that position, the spatial matching is successful, and the activation threshold is usually set to 0.5. The matching accuracy is evaluated by calculating the distance between the actual coordinate and the center of the nearest activated grid, the smaller the distance, the higher the accuracy. Hash consistency verification is performed by comparing the pre-state hash value in the composite time sequence identifier with the state hash value recorded in the state mapping matrix. The matrix records the hash value of each activated state when it is updated, which is used for subsequent verification. If the two hash values are exactly the same, it means that the test is indeed caused by the corresponding monitoring trigger event, and the consistency verification is passed. This verification mechanism effectively prevents the wrong attribution of test results. Timeliness verification is performed by calculating the correspondence between the time sequence number and the matrix update time. The system maintains a mapping table of time sequence numbers and timestamps, from which the actual time corresponding to each number can be obtained. Calculate the interval between the test feedback time and the trigger time, if the interval is within a reasonable range, the timeliness verification is passed, the reasonable range is usually 1-300 seconds. Feedback beyond the range may be outdated information caused by network delay or system failure. The matching comprehensive score is calculated by weighted summation of spatial matching accuracy, hash consistency degree, and timeliness rationality level. Spatial matching accuracy accounts for 40% of the weight, scoring is linearly decreasing according to distance deviation; hash consistency accounts for 35% of the weight, full match gets full score, no match gets 0; timeliness rationality accounts for 25% of the weight, scoring is segmented according to time interval. For example, a feedback with a spatial deviation of 2km (score 80), hash full match (score 100), and time interval of 45 seconds (score 90), the comprehensive score is 80x0.4+100x0.35+90x0.25=89.5. When the matching comprehensive score exceeds the qualified score line G, the verification is passed and the subsequent processing flow is entered, the qualified score line G is determined by statistical analysis of historical data. Collect a large amount of scoring data of normal feedback and abnormal feedback, draw a scoring distribution histogram, and select a threshold that can distinguish between the two types of feedback. In practical application, the value of G is usually set to be in the range of 75-85, which ensures the strictness of the verification and avoids information loss caused by excessive screening.

[0126] Step S44, based on the association matching verification result, feedback information quality assessment and screening are performed to screen out qualified feedback information.

[0127] Further, as Figure 11As shown, step S44 includes:

[0128] Step S441, based on the association matching verification result, calculate the integrity score, data accuracy score, timeliness validity score;

[0129] Step S442, based on the information integrity score, data accuracy score, timeliness validity score, calculate the quality comprehensive score, and screen out qualified feedback information.

[0130] The information integrity score is calculated by checking the completeness of the necessary fields. The necessary fields include fault point type identification (such as 25% weight), attenuation degree value (such as 30% weight), reflection characteristic parameter (such as 25% weight), and test timestamp (such as 20% weight). The weight allocation reflects the importance of each field to fault diagnosis. For example, a feedback lacking the reflection characteristic parameter field has an integrity score of 100-25=75. This quantitative scoring method makes information quality assessment more objective and comparable. The data accuracy score is calculated by comparing the actual value with the normal range. The normal range is determined by statistical analysis of the distribution characteristics of historical data, and the range boundary is taken as the mean value plus or minus 3 times the standard deviation. For example, the historical data of optical power attenuation value shows that the mean value is -30dBm and the standard deviation is 10dB, so the normal range is [-60dBm, 0dBm]. In actual application, considering extreme cases, the range is expanded to [-70dBm, -10dBm]. The deviation calculation adopts a normalized method to ensure the comparability of data with different dimensions. The timeliness score is calculated according to the feedback delay time segmentation. The linear score in the normal timeliness range [0s, 60s] decreases from 100 to 85; the score in the acceptable range [60s, 300s] is fixed at 70; the score in the overtime range greater than 300s is 30. This segmented function design reflects the requirements for real-time performance and also considers the influence of uncontrollable factors such as network delay. The weight setting of quality comprehensive score is determined by the analytic hierarchy process. Invite 5-8 experts in the field of optical communication to compare the importance of integrity, accuracy, and timeliness pairwise, using 1-9 scale method, 1 representing equal importance, and 9 representing extreme importance; calculate the consistency index CI and consistency ratio CR of the judgment matrix, when CR<0.1, the matrix is considered consistent; solve the characteristic vector corresponding to the maximum eigenvalue of the judgment matrix by eigenvalue method, and normalize to get the weight value, such as integrity 40%, accuracy 35%, and timeliness 25%.

[0131] The ROC curve analysis method is used to set the access threshold H of the feedback information quality, qualified / unqualified feedback samples are collected, the true positive rate and false positive rate under different thresholds are calculated, the ROC curve is drawn, and the threshold corresponding to the point closest to the upper left corner of the curve is selected. Generally, the H value is set to 70-80 points, which can balance the quality of information and the integrity of information. The feedback information with a comprehensive quality score of H points or more is selected as qualified feedback information, and a qualified feedback information set is established.

[0132] The composite time sequence identifier in step S40 realizes accurate identification and tracking of test feedback information. The three-part structure carries information in space, time and causality dimensions respectively, so that each feedback has a unique and semantically rich identifier. Compared with the traditional simple numbering, the information carrying capacity is improved by more than 5 times, while the storage format remains compact. The multi-dimensional correlation matching verification mechanism ensures the consistency of feedback information and system state. Through the comprehensive verification of space, hash and time effectiveness, the incorrect, repeated and outdated information is effectively filtered. The hierarchical quality evaluation system realizes the quantitative management of feedback information quality. Through independent evaluation and comprehensive scoring in three aspects of integrity, accuracy and timeliness, the information quality becomes a measurable, comparable and optimized index. This quantitative management method provides a clear improvement direction for subsequent adaptive optimization. If step S40 is missing, the system will not be able to verify the effectiveness of the test feedback, may make wrong decisions based on incorrect information, cannot evaluate the quality of feedback information, and low-quality information will pollute the decision-making process. Without the establishment of the correspondence between feedback and trigger, fault localization errors will occur. Step S40 ensures that only accurate, complete and timely high-quality information enters the decision-making process, providing a guarantee for the reliable operation of the entire linkage response system.

[0133] In step S50, the expected target of the switching operation of the OLP switching service in the Spring-Cloud microservice architecture is received, and based on the qualified feedback information, the optimal time sequence path is calculated, the time sequence reconstruction buffer is constructed based on the optimal time sequence path, and the switching synchronization control is executed based on the time sequence reconstruction buffer.

[0134] Further, as shown in Figure 12 , step S50 includes:

[0135] In step S51, the expected target of the switching operation of the OLP switching service in the Spring-Cloud microservice architecture is received, and based on the qualified feedback information, the current system state point and the switching completion point are determined, and the optimal time sequence path is calculated according to the current system state point and the switching completion point.

[0136] Further, as shown in Figure 13 , step S51 includes:

[0137] Step S511, determining the three-dimensional coordinates of the current system state point based on the filtered qualified feedback information;

[0138] Step S512, obtaining the expected target of the OLP switching operation, and setting the target three-dimensional coordinates of the switching completion point based on the expected target of the OLP switching operation;

[0139] Step S513: calculating the optimal timing path based on the three-dimensional coordinates of the current system state point and the target three-dimensional coordinates of the switching completion point, and establishing the path node sequence of the optimal timing path.

[0140] Specifically, the system state point is a spatial coordinate point quantifying the current network running state in the three-dimensional coordinate system of the optical power change trajectory: the X-axis corresponds to the spatial position of the fault point, the Y-axis corresponds to the time span from abnormal monitoring to test completion, and the Z-axis corresponds to the optical power abnormality degree. The timing reconstruction buffer is a specially designed instruction scheduling system that uses a priority queue data structure to realize timing optimization and synchronization control of the optical line protection switching instruction. The switching synchronization control coordinates the action time difference of the transmitting and receiving optical switches through the timing synchronization window parameter, ensuring the signal continuity of the switching process. The optimal timing path uses a multi-objective optimization algorithm to plan the path from the current state point to the target completion point in the three-dimensional coordinate space, considering factors such as time consumption, state transition smoothness, and resource occupation. The current system state point is obtained by extracting the optical power value, timestamp, and attenuation degree parameters in the qualified feedback information filtered in step S44, and performing three-dimensional coordinate conversion according to the coordinate axis definition rules established in step S11.

[0141] The coordinate conversion process strictly follows the coordinate system definition of step S11. The X-coordinate of the current state point is calculated by subtracting the reference optical power value from the current optical power value and dividing by the power normalization coefficient 10 dBm, the Y-coordinate is calculated by subtracting the monitoring trigger timestamp from the current timestamp and dividing by the time normalization coefficient 3600 seconds, and the Z-coordinate is calculated by taking the negative of the current attenuation degree and dividing by the attenuation normalization coefficient 10 dBm. The setting of the normalization coefficient is based on the typical parameter range of the optical communication system and the numerical accuracy requirement of the coordinate system. The power normalization coefficient 10 dBm corresponds to the commonly used dynamic range of optical power monitoring, the time normalization coefficient 3600 seconds corresponds to the typical time window of fault handling, and the attenuation normalization coefficient 10 dBm corresponds to the typical attenuation range of optical fiber transmission. The state point position identification code is generated in the format of "CurrentState_X coordinate value_Y coordinate value_Z coordinate value_timestamp", ensuring the uniqueness and traceability of the state point.

[0142] The expected target of the OLP switching operation is determined by analyzing the transmission characteristics and the current fault state of the standby optical path, and the target optical power value after switching is calculated by subtracting the total transmission path loss, including the cumulative values of fiber loss, connector loss, and splitter loss, from the emission power of the standby optical path. The expected completion time is determined based on the switching performance parameters of the OLP device and the system load conditions, including technical indicators such as device switching time specifications, processing capacity, and response delay. The expected value of the attenuation degree after switching is calculated by comparing the transmission loss difference between the standby optical path and the primary optical path. The target state information is converted into three-dimensional coordinates according to the same coordinate conversion rule, and the target identification code is generated in the format "TargetState_X coordinate value_Y coordinate value_Z coordinate value_target timestamp".

[0143] The optimal timing path calculation uses an improved A* algorithm, which adds a multi-objective optimization function based on the traditional A* algorithm. The optimization objectives include time consumption, state transition smoothness, and resource occupation, with weight distribution of time consumption 0.5, state transition smoothness 0.3, and resource occupation 0.2. The weight setting is based on the characteristics of high timeliness and strong stability requirements in optical network fault handling. Time consumption is calculated by adding the time cost of each segment of the path, state transition smoothness is evaluated by the Euclidean distance between adjacent state points, and resource occupation is quantitatively evaluated by calculating the consumption of resources, storage resources, and network resources. The heuristic function uses a weighted combination of the Euclidean distance from the current point to the target point and the estimated time consumption. The path function uses a parameterized curve equation P(t) = (X(t), Y(t), Z(t)), where t is the path parameter, the value range is [0, 1], t = 0 corresponds to the current state point, and t = 1 corresponds to the target completion point. Path feasibility verification is achieved by checking whether all sampling points on the path are within the range of the three-dimensional decision area. The path node sequence is divided according to equal time intervals, and the node interval time parameter T has a value range of 30-120 seconds, which is determined based on the time granularity requirements of OLP switching operations. The node number uses the format "PathNode_node number_X coordinate value_Y coordinate value_Z coordinate value", and the storage uses a linked list structure to support sequential traversal. Table 1 shows an example of the node path.

[0144] Table 1 Node path example

[0145] Path node example X coordinate Y coordinate Z coordinate Time interval (seconds) PathNode_1 2.5 0.8 -1.2 0 PathNode_2 3.1 1.6 -0.8 60 PathNode_3 3.8 2.4 -0.4 120

[0146] Step S52, based on the path node sequence of the optimal timing path, constructs a timing reconstruction buffer.

[0147] Further, as shown in Figure 14 Step S52 includes:

[0148] Step S521, generating a transceiver light switch control instruction sequence based on the path node sequence of the optimal timing path;

[0149] Step S522, establishing an instruction dependency graph based on the transceiver light switch control instruction sequence;

[0150] Step S523, constructing a priority queue type timing reconstruction buffer based on the instruction dependency graph.

[0151] Specifically, the timing reconstruction buffer adopts a priority queue data structure, and the timing optimization of the instructions is realized through four links of instruction generation, time correction, dependency analysis, and priority sorting. In the instruction generation process, the switch action type is determined according to the coordinate change characteristics of the path node, and the differential calculation method is used to compare the coordinate difference between adjacent nodes. When the Z coordinate change exceeds 0.5 (determined based on the minimum detectable change of optical power monitoring), a “PowerSwitch” power switching instruction is generated, and when the X coordinate change exceeds 1.0 (determined based on the minimum switching unit of the optical cable network), a “PathSwitch” path switching instruction is generated. The transceiver light switch pairing relationship is determined through optical network topology analysis, and each optical path contains transmission channels in two directions of sending and receiving, and the pairing information is stored in an association table. The instruction format adopts the structure of “SwitchCommand_NodeSerialNumber_SwitchType_ActionType_ExpectedExecutionTime”, and the initial priority is assigned in descending order according to the path node position.

[0152] Dependency analysis determines the execution order constraints by identifying the logical relationship between instructions. The graph adopts a directed graph structure, the node represents the control instruction, the edge represents the dependency relationship, and the edge weight represents the dependency strength level. The dependency relationship type includes strong dependency, weak dependency, and no dependency, strong dependency means strict sequential execution, weak dependency means priority order execution, and no dependency means concurrent execution. The critical path is identified by the longest path algorithm, which represents the longest dependency link from the starting instruction to the ending instruction. The dependency relationship matrix is represented by an adjacency matrix, and the matrix element value represents the dependency relationship type.

[0153] The priority queue adopts a heap data structure to realize efficient insertion and deletion operations. The comprehensive priority score is calculated by weighting the time priority and the dependency priority, and the weight allocation is time priority 0.6 and dependency priority 0.4, which is balanced based on the importance of timeliness and dependency relationship. The time priority is calculated according to the corrected timestamp, and the earlier the time, the higher the priority. The dependency priority is calculated according to the topological position of the instruction in the dependency graph, and the instructions on the critical path have higher priority. The buffer management mechanism includes enqueue, dequeue, update, and delete operations, and the state monitoring indicators include queue length, average waiting time, and queue utilization rate.

[0154] Step S53, setting the timing synchronization window control parameter based on the timing reconstruction buffer;

[0155] Step S54, executing the switching instruction synchronization release based on the set timing synchronization window control parameter.

[0156] The setting of the timing synchronization window control parameter W is achieved through the following detailed steps: the system extracts all instructions marked as paired from the priority queue of the timing reconstruction buffer. The identification of paired instructions is achieved by analyzing the switch type field in the instruction identifier. When the node numbers of two instructions are the same but the switch types are light-emitting switch and light-receiving switch respectively, they are determined as paired instructions. The corrected timestamp difference between each pair of paired instructions is calculated. The timestamp difference is equal to the corrected timestamp of the light-receiving switch instruction minus the corrected timestamp of the light-emitting switch instruction. All timestamp difference data is collected, and the mean μ and standard deviation σ of the distribution are calculated using the normal distribution fitting method. The normal distribution fitting uses the maximum likelihood estimation method. The mean μ is equal to the arithmetic mean of all differences, and the standard deviation σ is equal to the square sum of the difference from the mean divided by the sample size minus 1 and then taking the square root. The calculation formula of the timing synchronization window control parameter W is W equal to μ plus 2σ. This formula is based on the 3σ principle of normal distribution, ensuring that 95% of the paired instructions can complete synchronization execution within the synchronization window range. The synchronization release mechanism monitors the execution timestamps of the instructions in the buffer. When the timestamp differences of multiple related instructions are within the synchronization window range, they are released simultaneously. The release strategy uses batch release, which reduces the time interval between instructions and improves the synchronization of switching.

[0157] Step S50 achieves intelligent scheduling and precise timing control of optical line protection switching through the construction of the timing reconstruction buffer and the optimal timing path calculation. Traditional optical line protection systems use fixed switching strategies and simple timing control, which have problems such as unpredictable switching time, non-smooth switching process, and difficulty in coordinating multiple devices. Step S50 calculates the optimal timing path, mathematically models and optimizes the conversion path from the current state to the target state of the switching process, making the switching process predictable and controllable, and significantly improving the success rate and efficiency of switching. The establishment of the timing reconstruction buffer solves the problem of inconsistent instruction execution timing in a distributed environment. Through timestamp correction and dependency analysis, it ensures that instructions are executed in the correct order and at the right time, avoiding switching failures caused by timing disorder.

[0158] Step S60, receiving the switching completion signal of the OLP switching service, and marking the linkage response completion point based on the switching completion signal, and calculating the response trajectory feature based on the linkage response completion point, and executing adaptive decision parameter adjustment according to the response trajectory feature.

[0159] Please refer to Figure 15As shown, further, step S60 comprises:

[0160] Step S61, receiving the switching completion signal of the OLP switching service, and marking the linkage response completion point based on the switching completion signal, and calculating the response trajectory feature based on the linkage response completion point; the response trajectory feature comprises the trajectory geometric feature parameter and the time feature parameter;

[0161] Further, as shown in Figure 16 Step S61 comprises:

[0162] Step S611, receiving the switching completion signal sent by the OLP switching service and extracting the actual state information after switching;

[0163] Step S612, marking the linkage response completion point in the three-dimensional coordinate system based on the actual state information after switching;

[0164] Step S613, calculating the complete response trajectory based on the monitoring trigger point and the response completion point;

[0165] Step S614, extracting the trajectory geometric feature parameter and the trajectory time feature parameter based on the complete response trajectory.

[0166] Specifically, the linkage response completion point refers to the position marker of the actual state of the system after the OLP switching operation is completed in the three-dimensional coordinate system, and the response trajectory features include two types of quantitative description indexes, i.e., the trajectory geometric feature parameters and the time feature parameters. The adaptive decision parameter adjustment is a feedback control mechanism for dynamically optimizing the key parameters of the system based on the response effect evaluation results. The calculation of the response trajectory features needs to comprehensively analyze the complete process trajectory from the monitoring trigger to the switching completion, and extract the quantitative feature parameters of the trajectory through geometric analysis and time analysis. The switching completion signal adopts the format of "SwitchComplete_Service IP_Completion Time Stamp_Result State Code", and the result state code includes the switching success, switching failure, partial success and the like. The system extracts the execution results of the switching operation from the signal, including the switching success flag, the actual switching completion time, the line state after switching and the like. The actual optical power value after switching is obtained by calling the real-time data interface of the optical power monitoring service, and the interface calling adopts a synchronous mode to ensure the real-time of the data. The actual optical power attenuation degree after switching is calculated by subtracting the optical power value after switching from the optical power value before switching, and the value reflects the actual influence of the switching operation on the optical power level. The time stamp accuracy of the switching completion time reaches the millisecond level, and the accuracy of the time is ensured through the network time protocol synchronization. The coordinate conversion of the linkage response completion point strictly follows the coordinate axis definition rules established in step S11, the X coordinate value is calculated by subtracting the reference optical power value from the actual optical power value after switching and dividing by the power standardization coefficient 10 dBm, the Y coordinate value is calculated by subtracting the monitoring trigger time from the switching completion time and dividing by the time standardization coefficient 3600 seconds, and the Z coordinate value is calculated by taking the negative of the actual attenuation degree after switching and dividing by the attenuation standardization coefficient 10 dBm. The marker of the completion point in the three-dimensional decision region adopts the same marking method as the aforementioned state point, and the position identification code is generated in the format of "CompletePoint_X coordinate value_Y coordinate value_Z coordinate value_Completion Time Stamp".

[0167] The complete response trajectory contains an ordered coordinate sequence of four key nodes. The monitoring trigger point is extracted from the recorded trajectory point sequence in step S13, which is the starting point of the trajectory point sequence entering the stereoscopic decision region. The OTDR test point is obtained from the fault location result in step S41. The switching start point is obtained from the current system state point determined in step S511. The response completion point is obtained from the completion point marked in step S612. Trajectory connection adopts a piecewise linear interpolation method to establish linear connection between adjacent key nodes, forming a continuous spatial trajectory path. The mathematical expression of the trajectory adopts a parameterized representation, and each segment of the trajectory is represented as a straight line segment equation in three-dimensional space. The trajectory geometric feature parameters include five indexes: total length, straight line distance, bending degree, deviation distance, and turning number. The total length is calculated by accumulating the Euclidean distances between adjacent nodes. The Euclidean distance formula is the square root of the sum of the coordinate differences between two points. The straight line distance is the three-dimensional straight line distance from the trigger point to the completion point. The bending degree is calculated by dividing the total length by the straight line distance. The larger the ratio, the more tortuous the trajectory. The deviation distance is obtained by calculating the perpendicular distance from each point on the trajectory to the straight line path. The turning point is identified by analyzing the direction change of the trajectory. When the angle between adjacent line segments exceeds 30 degrees, it is determined as a turning point. The trajectory time feature parameters include total time consumption, monitoring response time, decision processing time, switching execution time, and time distribution percentage. Each time parameter is calculated by the difference in Y coordinates of the corresponding nodes.

[0168] Step S62, according to the response trajectory characteristics, establish a response effect evaluation model, and perform response effect evaluation;

[0169] Step S63, based on the response effect evaluation result, perform quality change trend analysis and intelligent identification of optimization requirements, and obtain optimization requirement identification result;

[0170] Step S64, according to the optimization requirement identification result, execute adaptive decision parameter adjustment; the decision parameters include the boundary parameters of the stereoscopic decision region and the time sequence synchronization window control parameters.

[0171] Specifically, the response effect evaluation model adopts a multi-dimensional comprehensive scoring method, and the theoretical trajectory is a straight line path from the trigger point to the completion point, serving as the evaluation benchmark. The trajectory deviation degree is calculated by subtracting the theoretical trajectory length from the actual trajectory length and dividing by the theoretical trajectory length. The comprehensive scoring model considers four dimensions: trajectory deviation degree, time consumption efficiency, switching success rate level, and synchronization accuracy index, with weight distribution of 30% for trajectory deviation, 25% for time efficiency, 25% for success rate, and 20% for synchronization accuracy. The weight setting is determined based on the importance of each index in optical network fault handling. The time consumption efficiency is equal to the theoretical shortest processing time ÷ actual processing time × 100%. The linkage response quality comprehensive score ranges from 0 to 100, which is a standardized interval. The quality trend analysis uses time series analysis method to collect the quality comprehensive score data of the last I times of linkage response to establish a trend model. The value of I ranges from 10 to 50, which is determined based on the sample size requirement of statistical analysis. Trend anomaly detection is determined by the condition that the score decreases for consecutive J times and the decrease amplitude exceeds K%. The value of J is 3-5, and the value of K is 10%-20%. The parameter setting is determined based on the normal range of system performance fluctuations. The optimization requirement identification automatically identifies the system parameter category and optimization direction that need to be adjusted according to the main reason category of score decrease. The decision parameter adjustment includes adaptive optimization of the three-dimensional decision region boundary parameters N, M, P and the time sequence synchronization window control parameter W. When the trajectory deviation degree is large, such as when the response trajectory deviation degree > 20%, the decision range is expanded by increasing the decision space size parameter by 1.1-1.3 times. When the time consumption efficiency is low, such as when the time consumption efficiency < 60%, the processing efficiency is improved by reducing the decision space size parameter by 0.8-0.9 times. When the synchronization accuracy index is insufficient, such as when the time sequence synchronization error > 50 ms, the synchronization accuracy is improved by reducing the window time length parameter W by 0.9 times. When the switching success rate level is low, such as less than 80%, the switching fault tolerance is improved by appropriately increasing the window time length parameter W by 1.1 times. The boundary constraints are: 10 dBm ≤ N ≤ 30 dBm, 10 km ≤ M ≤ 100 km, 60 s ≤ P ≤ 600 s, and 10 ms ≤ W ≤ 500 ms. The boundary constraints of parameter adjustment ensure that the adjusted values are within a reasonable range, avoiding excessive adjustment that may cause system instability. The parameter adjustment effect is monitored by a tracking verification mechanism, and the improvement degree is evaluated in the next 5 consecutive response processes after adjustment.

[0172] Step S60 realizes the closed-loop optimization control of the optical power monitoring and fault test linkage system by establishing a complete response trajectory analysis and adaptive decision parameter adjustment mechanism. The traditional optical network fault handling system lacks effective performance evaluation and parameter optimization mechanism, which leads to the degradation of system performance over time and the gradual reduction of response efficiency. Step S60 converts the abstract system performance into measurable geometric and time parameters through quantitative analysis of the response trajectory characteristics, providing objective quantitative indicators for performance evaluation. The establishment of the response effect evaluation model enables the system to automatically identify performance bottlenecks and optimization space, providing a scientific basis for parameter adjustment. The synergy of step S60 and the aforementioned steps forms a complete intelligent fault handling system. The three-dimensional coordinate system provides a unified spatial foundation for trajectory analysis, the anchor network provides distributed support for state monitoring, and the time sequence reconstruction buffer provides optimization scheduling for switching control. The organic combination of these technical features enables the entire system to have high intelligence and adaptability, significantly improving the efficiency and reliability of optical network fault handling.

[0173] Embodiment 2

[0174] This embodiment is based on embodiment 1 and provides an optical power monitoring and fault test linkage fast response system, as shown in Figure 17 , which includes:

[0175] Trajectory monitoring module: for receiving optical power monitoring data of the optical power monitoring service in the Spring-Cloud microservice architecture, and generating a sequence of optical power change trajectory points;

[0176] Network construction module: for establishing a distributed node state anchor grid based on the sequence of optical power change trajectory points, and constructing a three-layer anchor network structure;

[0177] Mapping module: for generating a decision link state mapping matrix based on the three-layer anchor network structure;

[0178] Feedback verification module: for receiving feedback information of the OTDR test service in the Spring-Cloud microservice architecture, generating a composite time sequence identifier, and performing association matching verification with the decision link state mapping matrix to obtain qualified feedback information;

[0179] Switching control module: for receiving the switching operation expected target of the OLP switching service in the Spring-Cloud microservice architecture, calculating the optimal time sequence path based on the qualified feedback information, constructing the time sequence reconstruction buffer based on the optimal time sequence path, and performing switching synchronization control based on the time sequence reconstruction buffer;

[0180] The parameter adjustment module is configured to receive a switching completion signal of the OLP switching service, mark a linkage response completion point based on the switching completion signal, calculate a response trajectory feature based on the linkage response completion point, and perform adaptive decision parameter adjustment according to the response trajectory feature.

[0181] The switching control module comprises:

[0182] The path calculation unit is configured to receive a switching operation expected target of the OLP switching service in the Spring-Cloud microservice architecture, determine a current system state point and a switching completion point based on qualified feedback information, and calculate an optimal timing path according to the current system state point and the switching completion point.

[0183] The construction unit is configured to construct a timing reconstruction buffer based on a path node sequence of the optimal timing path.

[0184] The setting unit is configured to set timing synchronization window control parameters based on the timing reconstruction buffer.

[0185] The execution unit is configured to execute switching instruction synchronization release based on the set timing synchronization window control parameters.

[0186] The parameter adjustment module comprises:

[0187] The trajectory calculation unit is configured to receive a switching completion signal of the OLP switching service, mark a linkage response completion point based on the switching completion signal, and calculate a response trajectory feature based on the linkage response completion point; the response trajectory feature comprises a trajectory geometric feature parameter and a time feature parameter.

[0188] The establishment unit is configured to establish a response effect evaluation model according to the response trajectory feature, and perform response effect evaluation.

[0189] The evaluation unit is configured to perform quality change trend analysis and intelligent identification of optimization requirements based on a response effect evaluation result, and obtain an optimization requirement identification result.

[0190] The adjustment unit is configured to perform adaptive decision parameter adjustment according to the optimization requirement identification result; the decision parameters comprise boundary parameters of a three-dimensional decision region and timing synchronization window control parameters.

[0191] The method and system of the present application can be implemented in many ways. For example, the method and system of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the above specifically described order, unless otherwise specifically stated.

[0192] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive repetition.

[0193] The specific embodiments described above are further explained in connection with the purposes, technical solutions, and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for rapid response of optical power monitoring and fault testing linkage, characterized in that: The method comprises: Receive optical power monitoring data from the optical power monitoring service in the Spring-Cloud microservice architecture and generate a sequence of optical power change trajectory points; Establishing a distributed node state anchor point grid according to the optical power change trajectory point sequence, and constructing a three-layer anchor point network structure; Based on the three-layer anchor network structure, generating a decision link state mapping matrix; Receive feedback information from the OTDR test service in the Spring-Cloud microservice architecture, generate a composite timing identifier, and perform correlation matching verification with the decision link state mapping matrix to obtain qualified feedback information; Receiving an expected target of a switching operation of an OLP switching service in the Spring-Cloud microservice architecture, calculating an optimal timing path based on the qualified feedback information, constructing a timing reconstruction buffer based on the optimal timing path, and performing switching synchronization control based on the timing reconstruction buffer; Receive a switching completion signal of the OLP switching service, mark a linkage response completion point based on the switching completion signal, calculate a response trajectory feature based on the linkage response completion point, and perform adaptive decision parameter adjustment according to the response trajectory feature.

2. The optical power monitoring and fault test linkage rapid response method according to claim 1 is characterized in that: The optical power monitoring service, the OTDR testing service and the OLP switching service are coordinated and linked.

3. The optical power monitoring and fault test linkage rapid response method according to claim 2 is characterized in that: The optical power monitoring data includes an optical power value, a monitoring timestamp, and a physical location of the optical cable; The method for generating a sequence of optical power change trajectory points comprises: Determine the spatial position of the optical power monitoring point, set the optical power monitoring point as the origin, set the direction of increase of the optical power value as the positive direction of the Z axis, set the actual laying direction of the physical extension of the optical cable as the positive direction of the X axis, and set the direction of time passage as the positive direction of the Y axis, and establish a three-dimensional coordinate system for the optical power change trajectory; Marking a three-dimensional decision area boundary in the three-dimensional coordinate system of the optical power change trajectory to generate a three-dimensional decision area; When the optical power monitoring data enters the three-dimensional decision area, the optical power change trajectory point sequence is recorded.

4. The optical power monitoring and fault test linkage rapid response method according to claim 3 is characterized in that: The three-layer anchor point network structure is a parallel structure.

5. The optical power monitoring and fault test linkage rapid response method according to claim 4 is characterized in that: The method for constructing a three-layer anchor point network structure includes: Divide the 3D decision area into equidistant three-dimensional units to obtain n grid cells, where n is the total number of grid cells; calculate the spatial size and geometric center point coordinates of each grid cell, assign a unique grid number to each grid cell, and establish an index mapping relationship table between the grid number and the geometric center point coordinates; Based on the index mapping relationship table between the grid numbers and the geometric center point coordinates, color-coded state anchor points are allocated in the grid units to construct the three-layer anchor point network structure.

6. The optical power monitoring and fault test linkage rapid response method according to claim 5 is characterized in that: The method of allocating colored state anchor points in the grid units and constructing the three-layer anchor point network structure includes: Allocating a red monitoring anchor point for the optical power monitoring service based on an index mapping relationship table between the grid number and the geometric center point coordinates to form a red monitoring anchor point network; Allocating a blue test anchor point for the OTDR test service based on an index mapping relationship table between the grid number and the geometric center point coordinates to form a blue test anchor point network; Allocating a green handover anchor point for the OLP handover service based on an index mapping relationship table between the grid number and the geometric center point coordinates to form a green handover anchor point network; The red monitoring anchor network, the blue test anchor network, and the green switching anchor network constitute the three-layer anchor network structure.

7. The optical power monitoring and fault test linkage rapid response method according to claim 6, characterized in that: The method for generating a decision link state mapping matrix includes: Obtaining a distribution of anchor point activation states in the three-layer anchor point network structure; The decision link state mapping matrix is ​​established according to the anchor point activation state distribution; the anchor point activation state distribution includes the activation state distribution of red monitoring anchor points, blue test anchor points and green switching anchor points.

8. The optical power monitoring and fault test linkage rapid response method according to claim 7, characterized in that: The method for obtaining the distribution of anchor point activation states in the three-layer anchor point network structure includes: Based on the colorized state anchor points, establish the anchor point activation state transition relationship; Based on the anchor point activation state conversion relationship, setting the adjacent grid anchor point cascade propagation relationship; Based on the anchor point activation state conversion relationship and the adjacent grid anchor point cascade propagation relationship, the anchor point activation state distribution in the three-layer anchor point network structure is obtained.

9. The optical power monitoring and fault test linkage rapid response method according to claim 8, characterized in that: The method for obtaining qualified feedback information includes: Collecting the test completion signal and fault location result data of the OTDR test service, extracting fault feature information, and converting and generating a three-dimensional coordinate representation; generating a composite time series identifier according to the three-dimensional coordinate representation of the fault characteristic information; Based on the composite timing identifier, performing an association matching verification with the decision link state mapping matrix; Based on the correlation matching verification result, the feedback information quality is evaluated and screened to select the qualified feedback information.

10. An optical power monitoring and fault test linkage rapid response system, which is used to implement an optical power monitoring and fault test linkage rapid response method according to any one of claims 1 to 9, characterized in that: The system comprises: Trajectory monitoring module: used to receive optical power monitoring data from the optical power monitoring service in the Spring-Cloud microservice architecture and generate a sequence of optical power change trajectory points; Network construction module: used to establish a distributed node state anchor point grid according to the optical power change trajectory point sequence and build a three-layer anchor point network structure; Mapping module: used to generate a decision link state mapping matrix based on the three-layer anchor network structure; Feedback verification module: used to receive feedback information from the OTDR test service in the Spring-Cloud microservice architecture, generate a composite timing identifier, and perform correlation matching verification with the decision link state mapping matrix to obtain qualified feedback information; The switching control module is configured to receive the expected target of the switching operation of the OLP switching service in the Spring-Cloud microservice architecture, calculate the optimal timing path based on the qualified feedback information, construct a timing reconstruction buffer based on the optimal timing path, and perform switching synchronization control based on the timing reconstruction buffer; A parameter adjustment module is configured to receive a switching completion signal of the OLP switching service, mark a linkage response completion point based on the switching completion signal, calculate a response trajectory feature based on the linkage response completion point, and perform adaptive decision parameter adjustment according to the response trajectory feature.

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