Intelligent migration path generation method and system for optical cable migration and change service

Abstract

The invention discloses a method and a system for generating an intelligent migration path of an optical cable migration and change service. The method comprises the steps of obtaining optical cable relocation event information; positioning an affected service list and an original path; constructing a weighted graph network model and temporarily removing the relocation and relocation optical cable; adopting a multi-strategy rerouting algorithm to calculate a plurality of migration paths for each service; evaluating feasibility, economy and risk indexes of each path; a recommendation scheme is generated through comprehensive score sorting, and alternative path analysis is carried out; and finally outputting a standardized relocation scheme report. The system can automatically and efficiently generate the optimal migration path and guarantee smooth migration of services.

Description

Technical Field

[0001] This application relates to the field of communication network management technology, and in particular to a method and system for generating intelligent migration paths for optical cable relocation services. Background Technology

[0002] With the continuous upgrading of urban infrastructure, the relocation of communication optical cables is becoming increasingly frequent. In existing technologies, service migration path planning before optical cable relocation mainly relies on manual operation, which presents the following significant technical problems: 1. Inefficient and error-prone: Network operations and maintenance personnel need to manually query the topology database, locate affected services and their original paths one by one in the complex network topology, and then design detour routes based on their personal experience. This process is time-consuming and prone to omissions and errors in highly complex networks.

[0003] 2. Lack of a globally optimal perspective, resulting in a high risk of service interruption: Manual planning makes it difficult to conduct a comprehensive analysis of network resources across all conditions and multiple constraints. This may lead to migration plans that only consider the recovery of a single service, while consuming the backup resources of other important services, or causing localized network congestion, thus failing to truly achieve "minimized service interruption".

[0004] 3. The evaluation of the relocation plan is highly subjective and lacks quantitative basis: The evaluation of the economic (such as cost) and feasibility (such as path length and optical attenuation) of the relocation plan relies heavily on the experience of engineers. There is a lack of unified and objective data models for quantitative analysis and comparison of multiple plans, which leads to insufficient scientific decision-making.

[0005] 4. Slow response speed, unable to cope with emergencies: From planned relocation of optical cables to emergency repair of faults, the speed of manual route planning cannot meet the needs of rapid service recovery, thus prolonging the service downtime.

[0006] Therefore, the technical problem to be solved by the present invention is: how to overcome the above-mentioned defects of the prior art and provide an automated and intelligent method and system that can quickly and accurately generate service migration paths in optical cable relocation and quantitatively evaluate them, so as to optimize the economy and feasibility of the relocation plan while ensuring the continuity of core services. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for generating intelligent migration paths for optical cable relocation services. This method can quickly and accurately generate service migration paths during optical cable relocation and quantitatively evaluate them, thereby optimizing the economy and feasibility of the relocation plan while ensuring the continuity of core services.

[0008] To achieve the above objectives, this application adopts the following technical solution: This application provides a method for generating intelligent migration paths for optical cable relocation services, the method comprising: S1 receives user input or automatically obtains optical cable relocation event information from an external work order system. The optical cable relocation event information includes at least: the unique identifier of the target optical cable to be relocated, the relocation time window, and the geographical location of the breakpoint. S2 uses the target optical cable relocation identifier as the key index to perform a reverse query in the network resource topology database to locate all service circuits passing through the target optical cable relocation and generate a list of affected services. Each service in the service list includes a service identifier and a service level. S3 reads the original path of each business from the business history configuration or performance management database for each business in the business list. S4. Construct the current communication network as a weighted graph model. The graph model includes a set of nodes, a set of edges, and a set of weights. The set of nodes represents all communication stations in the network, the set of edges represents the optical cable segments connecting the stations, and the set of weights assigns one or more weight values ​​to each edge. The key constraint of the graph model is to temporarily remove the edge corresponding to the optical cable being relocated from the graph model or set the weight to infinity. S5 employs a multi-strategy optical path dynamic rerouting algorithm, aiming to find the optimal migration path for each service. The input is the source site and destination site of the service, and the output is several migration paths. The multi-strategy optical path dynamic rerouting algorithm supports the shortest path strategy, the maximum bandwidth strategy, and the lowest cost strategy. S6 calculates feasibility, economic and risk indicators for each migration path. Feasibility indicators include total path length, path hop count and estimated total optical attenuation. Economic indicators include resource usage cost and required new equipment. Risk indicators include bottleneck bandwidth utilization and sharing with important services. S7. Based on the feasibility, economic and risk indicators of each migration path, a weighted scoring method or multi-attribute decision model is used to rank each migration path by comprehensive score. The migration path with the highest comprehensive score is marked as the recommended solution, and other migration paths and their advantages and disadvantages are listed. S8 generates a standardized migration plan report based on the recommended scheme and other migration paths and their advantages and disadvantages.

[0009] As a preferred technical solution, in S1, receiving user input or automatically obtaining optical cable relocation event information from an external work order system includes: When an external system creates a work order of type "optical cable relocation" and enters the "pending planning" state, it sends a standardized event notification through a predefined RESTful API or message middleware. Upon receiving the notification, it actively calls the query interface provided by the external system based on the unique work order number to obtain complete optical cable relocation event information. A data adapter is used to parse and map the optical cable relocation event information, converting it into internally defined standardized data. A task creation web interface is built for manual input by operations and maintenance personnel. The task creation web interface includes a fiber optic cable identifier input box, a time window selector, a geographic location input, and the upload of associated information. The task creation web interface checks whether the input fiber optic cable identifier exists in the resource database, ensures that the end time is later than the start time, and checks the validity of the geographic coordinates.

[0010] As a preferred technical solution, in S2, the step of using the target relocated optical cable identifier as a key index to perform a reverse query in the network resource topology database, locating all service circuits passing through the target relocated optical cable, and generating a list of affected services includes: A network resource topology database is constructed. The network resource topology database uses a graph database to store topology data. B+ tree indexes are created for the optical cable identifier and service identifier fields, and bitmap indexes are added for core services. Upon receiving the target optical cable identifier, perform a direct query using SQL or Cypher's graph database query language to obtain the query results. If the service spans multiple optical cables, ensure that the service is included in the service list only if the target relocated optical cable is a necessary segment. Convert the query results into a JSON format list of affected services.

[0011] As a preferred technical solution, in S3, reading the original routing path of each service in the service list from the service history configuration or performance management database includes: For each business identifier, the original path is obtained according to the following priority: query the real-time business path table to obtain the latest reported path; obtain a snapshot of the valid path within the current time window from the historical paths; and read the initial path from the static configuration of the CMDB system. The acquired raw path is converted into a unified structured object. If there is a failed optical cable in the path, an alarm is triggered and the path is marked as abnormal. If the path is incomplete, the path is automatically completed through topology reasoning. For all business identifiers in the business list, use the IN statement or batch API request to query them all at once; cache frequently accessed business paths in Redis, set the TTL to 5 minutes, and balance real-time performance and performance; preload the paths of core businesses into memory based on historical statistics.

[0012] As a preferred technical solution, in S4, constructing the current communication network into a weighted graph model includes: Extract communication site data, optical cable segment data, and real-time status data from the network resource topology database; Each communication site is mapped to a node in the graph, and the node ID directly corresponds to the unique identifier of the communication site; each optical cable is transformed into an undirected edge connecting two nodes; each edge is assigned a multi-dimensional weight, including physical length weight, bandwidth weight, quality weight, and economic weight. The graph model is checked for splitting into multiple isolated subgraphs due to the removal of the fiber optic cable using a depth-first search. If a split occurs, an alert is issued immediately and manual intervention is required. All weight values ​​are ensured to be valid positive numbers, and abnormal cases with zero length or negative bandwidth are excluded.

[0013] As a preferred technical solution, in S5, the multi-strategy optical path dynamic rerouting algorithm aims to find the optimal migration path for each service. The inputs are the source and destination sites of the service, and the output is a number of migration paths, including: The system automatically selects or manually specifies optimization strategies based on business level and type, calculates the weight value corresponding to the strategy for each edge in the graph model, and verifies the connectivity between the source site and the destination site. Based on the weights of the selected strategy, the Dijkstra algorithm is used to calculate the first shortest path; for each deviation point of the found path, the path segment is temporarily removed, the shortest path from the deviation point to the destination is calculated, and the segments are merged to form new candidate paths, thereby calculating the second to the Kth shortest paths in sequence; check whether the bandwidth meets the business requirements, verify whether the number of hops is within the tolerance range, and exclude paths that include relocation of optical cables to verify the effectiveness of the path. Remove duplicate or highly similar paths and sort the paths in ascending order according to their policy weight values.

[0014] As a preferred technical solution, in S6, the calculation of feasibility indicators, economic indicators, and risk indicators for each migration path includes: Based on the length field in the optical cable attribute table, sum the physical lengths of all optical cable segments in the path to obtain the total path length; count the number of communication stations the path passes through to obtain the path hop count; estimate the total optical attenuation, total optical attenuation = Σ(optical cable length × attenuation coefficient) + Σ(station hop count × insertion loss); Calculate resource occupancy cost: resource occupancy cost = basic rental fee + bandwidth usage fee + distance surcharge; check if the path requires activation of new ports or devices, and convert port or device requirements into equivalent costs based on equipment purchase and installation costs; Calculate the bottleneck bandwidth utilization rate: bottleneck bandwidth utilization rate = bandwidth required by the service / available bandwidth of the narrowest segment in the path; query the network resource database to identify the number of core services carried by each optical cable, and calculate the proportion of optical cable segments in the path that are shared with core services to obtain the sharing degree of important services.

[0015] As a preferred technical solution, in S7, the weighted scoring method is used to rank each migration path by comprehensive score, which includes: converting indicators of different dimensions into a unified scale, allocating the weight of each indicator using a weight allocation strategy, calculating the comprehensive score of each migration path using a weighted scoring method, and ranking each migration path by comprehensive score. In S7, a multi-attribute decision model is used to rank each migration path by comprehensive score, including: constructing a decision matrix, where the rows of the decision matrix represent each candidate path and the columns represent each evaluation indicator; calculating positive and negative ideal solutions, where the positive ideal solution is the set of optimal values ​​for each indicator and the negative ideal solution is the set of worst values ​​for each indicator; calculating the relative proximity, where relative proximity = distance to the negative ideal solution / (distance to the positive ideal solution + distance to the negative ideal solution); and using the relative proximity as the comprehensive score to rank each migration path by comprehensive score.

[0016] This application also provides an intelligent migration path generation system for optical cable relocation services, the system comprising: The information acquisition module is used to acquire information about optical cable relocation events. The information includes at least the unique identifier of the target optical cable to be relocated, the relocation time window, and the geographical location of the breakpoint. The impact analysis module is used to query and locate all service circuits passing through the optical cable in the network resource topology database using the target relocated optical cable identifier as an index, generate a list of affected services containing service identifiers and service levels, and read the original path of each service. The path calculation module is used to construct the current communication network into a weighted graph model, and temporarily remove the edge corresponding to the target optical cable from the model or set its weight to infinity according to the migration event; the module adopts a multi-strategy optical path dynamic rerouting algorithm to calculate one or more migration paths from the source site to the destination site for each service in the list of affected services; The solution decision module is used to calculate feasibility, economic and risk indicators for each migration path, and to comprehensively evaluate and rank the paths based on these indicators using weighted scoring or multi-attribute decision models, and output the recommended solution and the advantages and disadvantages analysis of other alternative paths. The report generation module is used to automatically generate standardized relocation plan reports based on the output of the plan decision module.

[0017] Compared with the prior art, the beneficial effects of this application are as follows: This application automates traditionally time-consuming and error-prone processes that rely on manual experience by automatically acquiring information from external systems, reverse-engineering affected services, and intelligently calculating migration paths. This significantly shortens solution development time, reduces human error, and improves the overall efficiency of migration work.

[0018] In step S2, a reverse query based on the network resource topology database can quickly and accurately locate all service circuits passing through the target relocated optical cable, generating a complete list of affected services. This avoids omissions that may occur during manual sorting and provides a reliable data foundation for subsequent accurate migration.

[0019] This application constructs a weighted graph model and applies a multi-policy rerouting algorithm to calculate optimal or suboptimal paths under various policies for services with different levels and requirements (such as pursuing the shortest latency, maximum bandwidth, or lowest cost). This flexibility allows the solution to better adapt to the diverse needs of real-world networks.

[0020] This application not only calculates the path but also quantitatively evaluates each candidate path from three dimensions: feasibility (technical level), economics (cost level), and risk (robustness level). Then, it comprehensively ranks the paths using weighted scoring or multi-attribute decision-making models, ensuring that the final recommended path is the optimal solution that is technically feasible, economically reasonable, and risk-controllable, making the decision-making process more scientific and objective.

[0021] The final standardized relocation plan report not only provides the recommended solution but also clearly lists other alternative solutions and their advantages and disadvantages. This makes the decision-making process transparent, facilitating review and selection by operations and maintenance personnel. It also standardizes the format of the output plan, improving its readability and feasibility, and facilitating the smooth progress of subsequent work.

[0022] This application, through proactive and pre-defined path calculation and risk assessment, can complete the secure migration of services before physical fiber optic cable breaks, minimizing service interruption time, effectively reducing the impact of migration operations on existing services, and enhancing the resilience and reliability of the network in the face of infrastructure changes. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the specific embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application.

[0024] This application provides a method for generating intelligent migration paths for optical cable relocation services, the method comprising: S1 receives user input or automatically obtains optical cable relocation event information from an external work order system. The optical cable relocation event information includes at least: the unique identifier of the target optical cable to be relocated (such as G01), the relocation time window, and the geographical location of the breakpoint.

[0025] S2 uses the target relocated optical cable identifier as the key index to perform a reverse query in the network resource topology database to locate all service circuits that pass through the target relocated optical cable and generate a list of affected services. Each service in the service list includes a service identifier and service level (such as core, important, and normal).

[0026] S3, for each service in the service list, reads the original service path from the service history configuration or performance management database. This original service path is stored in a structured format: [Source Site] -> [Through Fiber Optic Cable 1] -> [Transit Site 1] -> [Through Fiber Optic Cable 2] -> ... -> [Destination Site]. This step forms the basis for developing a communication transition plan and provides a comparison benchmark for subsequent path calculations.

[0027] S4. Construct the current communication network as a weighted graph model G(V,E,W). The graph model includes a set of nodes V, a set of edges E, and a set of weights W. The set of nodes represents all communication stations in the network, the set of edges represents the optical cable segments connecting the stations, and the set of weights assigns one or more weight values ​​to each edge. Weight values ​​may include: physical length, idle bandwidth, preset cost coefficient, reliability level, etc.

[0028] The key constraint of the graph model is to temporarily remove the edge corresponding to the fiber optic cable being relocated from the graph model or set its weight to infinity to ensure that the calculated new path does not include this failed resource.

[0029] S5 employs a multi-strategy dynamic optical path rerouting algorithm, aiming to find the optimal migration path for each service. The input consists of the source and destination sites of the service, and the output is several migration paths. The multi-strategy dynamic optical path rerouting algorithm supports shortest path strategy, maximum bandwidth strategy, and lowest cost strategy. Shortest path strategy: Targets the shortest physical distance, suitable for services sensitive to transmission latency. Maximum bandwidth strategy: Targets maximizing the segment with the minimum remaining bandwidth in the path (bottleneck bandwidth), suitable for services with high bandwidth requirements. Lowest cost strategy: Targets minimizing the sum of predefined resource usage costs, pursuing optimal economic efficiency. The multi-strategy dynamic optical path rerouting algorithm can execute multiple strategies sequentially or in parallel, generating multiple alternative solutions.

[0030] The multi-strategy optical path dynamic rerouting algorithm aims to find the optimal detour path for each service. Its input is the source / destination site of the service, and its output is K (K≥1) optimal paths. The algorithm can adopt an enhanced variant of the classic graph theory algorithm (such as the KSP-K shortest path algorithm).

[0031] S6 calculates feasibility, economic and risk indicators for each migration path.

[0032] Feasibility metrics include total path length, path hop count, and estimated total optical attenuation. Total path length: directly related to signal attenuation. Path hop count: the number of stations traversed; a higher hop count generally indicates lower reliability. Estimated total optical attenuation: calculated based on path length, hop count, and equipment insertion loss models.

[0033] Economic metrics include resource occupancy costs and required new equipment. Resource occupancy costs: estimated rental or usage costs based on path length and bandwidth usage. Required new equipment: whether new ports or equipment need to be activated, and their associated costs.

[0034] Risk indicators include bottleneck bandwidth utilization and sharing with critical services. Bottleneck bandwidth utilization: the utilization rate of the most strained remaining bandwidth segment on the path; a higher value indicates greater risk. Sharing with critical services: whether the new path shares critical links with other core services in the network to avoid risk concentration.

[0035] S7 ranks each migration path based on its feasibility, economic, and risk indicators using a weighted scoring method or a multi-attribute decision model (such as TOPSIS). Operations personnel can customize the weights of each indicator (e.g., assigning higher weights to "risk indicators" for core business operations). The migration path with the highest overall score is marked as the recommended solution, and other migration paths and their advantages and disadvantages are listed.

[0036] S8 generates a standardized migration plan report based on recommended solutions and other migration paths, along with their advantages and disadvantages. The report is structured and includes: a summary of the migration event; a list of affected services and details of the original paths; details of the recommended migration path for each service (text description + topology diagram); a comparison analysis table of alternative solutions; and a list of specific operational instructions (e.g., on which devices cross-connections need to be created).

[0037] This application automates traditionally time-consuming and error-prone processes that rely on manual experience by automatically acquiring information from external systems, reverse-engineering affected services, and intelligently calculating migration paths. This significantly shortens solution development time, reduces human error, and improves the overall efficiency of migration work.

[0038] In step S2, a reverse query based on the network resource topology database can quickly and accurately locate all service circuits passing through the target relocated optical cable, generating a complete list of affected services. This avoids omissions that may occur during manual sorting and provides a reliable data foundation for subsequent accurate migration.

[0039] This application constructs a weighted graph model and applies a multi-policy rerouting algorithm to calculate optimal or suboptimal paths under various policies for services with different levels and requirements (such as pursuing the shortest latency, maximum bandwidth, or lowest cost). This flexibility allows the solution to better adapt to the diverse needs of real-world networks.

[0040] This application not only calculates the path but also quantitatively evaluates each candidate path from three dimensions: feasibility (technical level), economics (cost level), and risk (robustness level). Then, it comprehensively ranks the paths using weighted scoring or multi-attribute decision-making models, ensuring that the final recommended path is the optimal solution that is technically feasible, economically reasonable, and risk-controllable, making the decision-making process more scientific and objective.

[0041] The final standardized relocation plan report not only provides the recommended solution but also clearly lists other alternative solutions and their advantages and disadvantages. This makes the decision-making process transparent, facilitating review and selection by operations and maintenance personnel. It also standardizes the format of the output plan, improving its readability and feasibility, and facilitating the smooth progress of subsequent work.

[0042] This application, through proactive and pre-defined path calculation and risk assessment, can complete the secure migration of services before physical fiber optic cable breaks, minimizing service interruption time, effectively reducing the impact of migration operations on existing services, and enhancing the resilience and reliability of the network in the face of infrastructure changes.

[0043] Furthermore, in S1, receiving user input or automatically obtaining optical cable relocation event information from an external work order system includes: When an external system creates a work order of type "fiber optic cable relocation" and enters the "pending planning" state, it sends a standardized event notification via a predefined RESTful API or message middleware. Upon receiving the notification, the system actively calls the query interface provided by the external system based on the unique work order number to obtain complete fiber optic cable relocation event information. A data adapter is used to parse and map the fiber optic cable relocation event information, converting it into internally defined standardized data. API integration with external work order systems is the preferred and most automated implementation method. The system integrates with existing network management systems, engineering project management systems, or maintenance work order systems through application programming interfaces (APIs). An API gateway is used for unified management, and JSONSchema or Protocol Buffers are used for data validation to ensure the integrity and correctness of received data.

[0044] A web interface for task creation, allowing maintenance personnel to manually input data, is constructed. This interface includes a fiber optic cable identifier input box, a time window selector, a geographic location input box, and the ability to upload associated information. The fiber optic cable identifier input box supports direct input or selection from a dropdown list. It provides auto-completion; when a user enters "G0", the system will suggest all fiber optic cable IDs starting with "G0" to prevent input errors. The time window selector provides a graphical calendar and time selection component, ensuring standardized time formats. The geographic location input box integrates an online map service. Maintenance personnel can directly input an address description. A better approach is to directly click on the precise breakpoint location on the map. The system automatically converts the clicked latitude and longitude coordinates into a standard structured address using a geocoding service and displays it in the address bar, ensuring accurate location. The associated information upload box allows uploading attachments such as construction drawings and on-site photos, providing more context for subsequent solution design. The task creation web interface checks whether the entered fiber optic cable identifier exists in the resource database, ensures that the end time is later than the start time, and verifies the validity of the geographic coordinates.

[0045] Regardless of the method used, once the standardized migration event information is successfully received and verified, the system will: internally create a unique migration task instance with a status set to "in progress"; automatically bind the task ID to the received event information and persist it to the database; and immediately trigger subsequent step S2, thereby starting the entire automated pipeline.

[0046] Furthermore, in S2, the process of using the target relocated optical cable identifier as a key index to perform a reverse query in the network resource topology database, locating all service circuits passing through the target relocated optical cable, and generating a list of affected services includes: A network resource topology database is constructed, which uses a graph database to store topology data. B+ tree indexes are created for the optical cable identifier and service identifier fields, and bitmap indexes are added for core services.

[0047] Upon receiving the target fiber optic cable identifier, a direct query is performed using SQL or Cypher's graph database query language to obtain the query results. If the service spans multiple fiber optic cables (e.g., A→G01→B→G02→C), it must be ensured that the service is included in the service list only if the target relocation fiber optic cable is a necessary segment. The query results are then converted into a JSON-formatted list of affected services. The generated service list will be passed as core input to steps S3 and S4 to ensure that subsequent processes are optimized independently for each service. The sorting logic of the list (prioritizing core services) also directly affects the priority strategy for path calculation.

[0048] Furthermore, in S3, reading the original routing path of each service in the service list from the service history configuration or performance management database includes: For each business identifier, the original path is retrieved according to the following priority: Query the `circuit_realtime_path` table to obtain the latest reported path. Retrieve a snapshot of valid paths within the current time window from the `circuit_path_history` historical path database. Read the initial path from the static configuration of the CMDB system; manual verification is required to confirm its continued validity.

[0049] The acquired raw path is converted into a unified structured object. If there is a failed optical cable in the path (such as a decommissioned G99), an alarm is triggered and the path is marked as abnormal. If the path is incomplete (such as missing source / destination sites), the path is automatically completed through topology reasoning.

[0050] For all business identifiers in the business list, use IN statements or batch API requests to query them all at once, reducing database connection overhead. Caching frequently accessed business paths in Redis with a TTL of 5 minutes balances real-time performance and overall performance. Preloading the paths of core business services (service_level='core') into memory based on historical statistics.

[0051] Furthermore, in S4, constructing the current communication network as a weighted graph model includes: Extract communication site data, optical cable segment data, and real-time status data from the network resource topology database.

[0052] Each communication site is mapped to a node in the graph, with the node ID directly corresponding to the unique identifier of the communication site. Each optical cable is transformed into an undirected edge connecting two nodes. Each edge is assigned multi-dimensional weights, including physical length weight, bandwidth weight, quality weight, and economic weight. Physical length weight: The number of kilometers of the optical cable, directly affecting signal attenuation. Bandwidth weight: The reciprocal of the currently available bandwidth (ensuring high-bandwidth paths are prioritized). Quality weight: A transmission quality score calculated based on the signal-to-noise ratio. Economic weight: A cost coefficient calculated considering factors such as length and bandwidth premium. The weight calculation method is dynamically adjusted according to the service type: Core service paths: Reliability is prioritized (70% quality weight + 30% length weight). Ordinary data services: Economic efficiency is prioritized (60% cost weight + 40% bandwidth weight).

[0053] For the target fiber optic cable to be relocated (such as G01), the system performs the following operations: edge removal method (recommended solution). Completely delete all edges corresponding to the fiber optic cable in the graph. For example, if fiber optic cable G01 connects stations A and B, then permanently remove edges (A,B) and (B,A). Advantages: Completely avoids the algorithm selecting ineffective paths, resulting in higher computational efficiency. Weight penalty method (alternative): Sets all weights of the edges corresponding to the target fiber optic cable to maximum values ​​(e.g., 10^6). Applicable scenarios: Used when the complete topology structure needs to be preserved for auxiliary analysis.

[0054] The graph model is checked for splitting into multiple isolated subgraphs due to the removal of the fiber optic cable using a depth-first search. If a split occurs, an alert is issued immediately and manual intervention is required. All weight values ​​are ensured to be valid positive numbers, and abnormal cases with zero length or negative bandwidth are excluded.

[0055] Furthermore, in S5, the multi-strategy optical path dynamic rerouting algorithm aims to find the optimal migration path for each service. The inputs are the source and destination sites of the service, and the output is several migration paths, including: The system automatically selects or manually specifies optimization strategies based on business level and type, calculates the weight value corresponding to the strategy for each edge in the graph model, and verifies the connectivity between the source and destination sites.

[0056] Based on the weights of the selected strategy, Dijkstra's algorithm is used to calculate the first shortest path. For each deviation point of the found path, the path segment is temporarily removed, and the shortest path from the deviation point to the destination is calculated. These segments are then merged to form new candidate paths, and so on, until the second to the Kth shortest paths are calculated. The bandwidth is checked to ensure it meets business requirements, the hop count is verified to be within tolerance limits, and paths involving fiber optic cable relocation are excluded to verify path validity.

[0057] Remove duplicate or highly similar paths and sort the paths in ascending order according to their policy weight values.

[0058] The multi-strategy optical path dynamic rerouting algorithm employs a strategy pattern to implement a scalable multi-strategy framework. Its core architecture is as follows: a strategy abstraction layer defines a unified path calculation interface, including weight calculation methods and termination conditions, allowing dynamic loading and switching of different optimization strategies; a core computing engine is based on an enhanced implementation of classic graph theory algorithms (such as Yen'sK shortest path algorithm), supporting path enumeration and sorting under constraints; and a result synthesizer performs deduplication, sorting, and comprehensive evaluation of the results from multiple strategies.

[0059] Different strategies are implemented by adjusting the way edge weights are calculated: 1. Shortest path strategy Weighting function: weight = physical length of optical cable Optimization objective: Minimize total transmission distance and reduce signal attenuation. Applicable scenarios: Businesses sensitive to transmission latency (such as financial transactions); 2. Maximum bandwidth strategy Weighting function: weight = 1 / current available bandwidth (maximizing bandwidth) Optimization goal: Maximize path bottleneck bandwidth Applicable scenarios: High bandwidth demand services (such as video transmission and data backup); 3. Lowest cost strategy Weighting function: weight = base cost coefficient × length + bandwidth premium cost Optimization objective: Minimize the total cost of resource usage. Applicable scenarios: ordinary data services and cost-sensitive applications.

[0060] Furthermore, in S6, the calculation of feasibility indicators, economic indicators, and risk indicators for each migration path includes: Based on the length field in the optical cable attribute table, sum the physical lengths of all optical cable segments in the path to obtain the total path length. Count the number of communication stations the path passes through to obtain the path hop count. Estimate the total optical attenuation: Total optical attenuation = Σ(optical cable length × attenuation coefficient) + Σ(station hop count × insertion loss).

[0061] Calculate resource occupancy cost: Resource occupancy cost = Basic rental fee + Bandwidth usage fee + Distance surcharge. Check if the path requires activation of new ports or devices, and convert port or device requirements into equivalent costs based on equipment purchase and installation costs.

[0062] Calculate the bottleneck bandwidth utilization rate: Bottleneck bandwidth utilization rate = bandwidth required by the service / available bandwidth of the narrowest segment in the path. Query the network resource database to identify the number of core services carried by each optical cable, and calculate the proportion of optical cable segments shared with core services in the path to obtain the sharing degree of important services.

[0063] Furthermore, in S7, the weighted scoring method for ranking each migration path by comprehensive score includes: converting indicators of different dimensions into a unified scale, allocating weights to each indicator using a weight allocation strategy, calculating the comprehensive score of each migration path using a weighted scoring method, and ranking each migration path by comprehensive score.

[0064] Weight determination methods include: 1. Subjective empowerment method (expert scoring) Organizational experts scored the importance of each indicator. The weight vector is calculated using the Analytic Hierarchy Process (AHP). Perform a consistency test (CR < 0.1); 2. Objective weighting method (entropy weighting method) Weights are automatically calculated based on the degree of variation in indicator data. The greater the degree of variation, the higher the weight. Formula: Weight = (1 - Information Entropy) / Σ(1 - Information Entropy); 3. Combination weighting method A weighted average is calculated by combining subjective and objective weights. Ensure that the weights reflect both expert experience and data characteristics.

[0065] In S7, a multi-attribute decision model is used to rank each migration path based on its comprehensive score. This includes: constructing a decision matrix, where rows represent candidate paths and columns represent various evaluation indicators; calculating positive and negative ideal solutions, where positive ideal solutions represent the set of optimal values ​​for each indicator and negative ideal solutions represent the set of worst values ​​for each indicator; calculating the relative proximity score, where relative proximity = distance to negative ideal solution / (distance to positive ideal solution + distance to negative ideal solution); and using the relative proximity score as the comprehensive score to rank each migration path.

[0066] The following is an application scenario for the above method: Due to municipal construction, the backbone optical cable G01 connecting "Beijing Site A" and "Tianjin Site B" will be interrupted for 6 hours.

[0067] Input: G01, 2023-10-26 00:00-06:00.

[0068] The system discovered the core business "Financial Transaction Data_Business 01" hosted on G01.

[0069] Read its original path: Beijing A--[G01]--> Tianjin B--[G23]--> Shanghai C.

[0070] After network modeling, optical cable G01 is set to invalid.

[0071] Calculate a detour path from Beijing A to Shanghai C for the business "Financial Transaction Data_Business 01" (avoiding G01).

[0072] The algorithm outputs two alternative paths: Option P1: Beijing A -- [G45] -- Jinan D -- [G67] -- Shanghai C. (Shorter path, fewer hops) Option P2: Beijing A--[G12]--> Shijiazhuang E--[G34]--> Tianjin B--[G23]--> Shanghai C. (Long path, but the entire route is a high-level backbone link) Assessment P1: Length 800km, hop count 2. However, the current bottleneck bandwidth utilization of fiber optic cable G45 has reached 85%, posing a high risk. Overall score: 75.

[0073] Evaluation P2: Length 1200km, hop count 3. However, the utilization rate of all fiber optic cables along the route is below 40%, indicating extremely high reliability. Overall score: 92.

[0074] Decision: Given that this business is a core financial business, the system sets the "reliability" weight to the highest level, and therefore recommends solution P2.

[0075] Output: The report clearly shows the topology of the original path and the recommended path P2, and explains why P1 is not recommended because of its potential congestion risk.

[0076] This application also provides an intelligent migration path generation system for optical cable relocation services, the system comprising: The information acquisition module is used to acquire information about optical cable relocation events. The information includes at least the unique identifier of the target optical cable to be relocated, the relocation time window, and the geographical location of the breakpoint.

[0077] The impact analysis module is used to query and locate all service circuits passing through the optical cable in the network resource topology database, using the target relocated optical cable identifier as an index, generate a list of affected services containing service identifiers and service levels, and read the original path of each service.

[0078] The path calculation module is used to construct the current communication network as a weighted graph model, and temporarily remove the edge corresponding to the target optical cable from the model or set its weight to infinity based on the migration event. This module adopts a multi-strategy optical path dynamic rerouting algorithm to calculate one or more migration paths from the source site to the destination site for each service in the affected service list.

[0079] The solution decision module is used to calculate feasibility, economic and risk indicators for each migration path, and to comprehensively evaluate and rank the paths based on these indicators using a weighted scoring or multi-attribute decision model, and output the recommended solution and the advantages and disadvantages analysis of other alternative paths.

[0080] The report generation module is used to automatically generate standardized relocation plan reports based on the output of the plan decision module.

[0081] It should be noted that the terms "first," "second," and similar terms used in this application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, "a" or "one," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. "A plurality" or "several" indicates at least two. Unless otherwise stated, terms such as "front," "back," "left," "right," "lower," and / or "upper" are for illustrative purposes only and are not limited to a location or spatial orientation. Terms such as "comprising" or "including" indicate that the elements or objects preceding "comprising" encompass the elements or objects listed following "comprising" or "including" and their equivalents, and do not exclude other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0082] The singular forms “a,” “the,” and “the” used in this application specification and appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0083] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for generating intelligent migration paths for optical cable relocation services, characterized in that, The method includes: S1 receives user input or automatically obtains optical cable relocation event information from an external work order system. The optical cable relocation event information includes at least: the unique identifier of the target optical cable to be relocated, the relocation time window, and the geographical location of the breakpoint. S2 uses the target optical cable relocation identifier as the key index to perform a reverse query in the network resource topology database to locate all service circuits passing through the target optical cable relocation and generate a list of affected services. Each service in the service list includes a service identifier and a service level. S3 reads the original path of each business from the business history configuration or performance management database for each business in the business list. S4. Construct the current communication network as a weighted graph model. The graph model includes a set of nodes, a set of edges, and a set of weights. The set of nodes represents all communication stations in the network, the set of edges represents the optical cable segments connecting the stations, and the set of weights assigns one or more weight values ​​to each edge. The key constraint of the graph model is to temporarily remove the edge corresponding to the optical cable being relocated from the graph model or set the weight to infinity. S5 employs a multi-strategy optical path dynamic rerouting algorithm, aiming to find the optimal migration path for each service. The input is the source site and destination site of the service, and the output is several migration paths. The multi-strategy optical path dynamic rerouting algorithm supports the shortest path strategy, the maximum bandwidth strategy, and the lowest cost strategy. S6 calculates feasibility, economic and risk indicators for each migration path. Feasibility indicators include total path length, path hop count and estimated total optical attenuation. Economic indicators include resource usage cost and required new equipment. Risk indicators include bottleneck bandwidth utilization and sharing with important services. S7. Based on the feasibility, economic and risk indicators of each migration path, a weighted scoring method or multi-attribute decision model is used to rank each migration path by comprehensive score. The migration path with the highest comprehensive score is marked as the recommended solution, and other migration paths and their advantages and disadvantages are listed. S8 generates a standardized migration plan report based on the recommended scheme and other migration paths and their advantages and disadvantages.

2. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S1, receiving user input or automatically obtaining optical cable relocation event information from an external work order system includes: When an external system creates a work order of type "fiber optic cable relocation" and enters the "pending planning" state, it sends a standardized event notification through a predefined RESTful API or message middleware. Upon receiving the notification, it actively calls the query interface provided by the external system based on the unique work order number to obtain complete fiber optic cable relocation event information. A data adapter is used to parse and map the fiber optic cable relocation event information, converting it into internally defined standardized data. A task creation web interface is built for manual input by operations and maintenance personnel. The task creation web interface includes a fiber optic cable identifier input box, a time window selector, a geographic location input, and the upload of associated information. The task creation web interface checks whether the input fiber optic cable identifier exists in the resource database, ensures that the end time is later than the start time, and checks the validity of the geographic coordinates.

3. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S2, the process of using the target relocated optical cable identifier as a key index to perform a reverse query in the network resource topology database, locating all service circuits passing through the target relocated optical cable, and generating a list of affected services includes: A network resource topology database is constructed. The network resource topology database uses a graph database to store topology data. B+ tree indexes are created for the optical cable identifier and service identifier fields, and bitmap indexes are added for core services. Upon receiving the target optical cable identifier, perform a direct query using SQL or Cypher's graph database query language to obtain the query results. If the service spans multiple optical cables, ensure that the service is included in the service list only if the target relocated optical cable is a necessary segment. Convert the query results into a JSON format list of affected services.

4. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S3, reading the original routing path of each service in the service list from the service history configuration or performance management database includes: For each business identifier, the original path is obtained according to the following priority: query the real-time business path table to obtain the latest reported path; obtain a snapshot of the valid path within the current time window from the historical paths; and read the initial path from the static configuration of the CMDB system. The acquired raw path is converted into a unified structured object. If there is a failed optical cable in the path, an alarm is triggered and the path is marked as abnormal. If the path is incomplete, the path is automatically completed through topology reasoning. For all business identifiers in the business list, use the IN statement or batch API request to query them all at once; cache frequently accessed business paths in Redis, set the TTL to 5 minutes, and balance real-time performance and performance; preload the paths of core businesses into memory based on historical statistics.

5. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S4, constructing the current communication network as a weighted graph model includes: Extract communication site data, optical cable segment data, and real-time status data from the network resource topology database; Each communication site is mapped to a node in the graph, and the node ID directly corresponds to the unique identifier of the communication site; each optical cable is transformed into an undirected edge connecting two nodes; each edge is assigned a multi-dimensional weight, including physical length weight, bandwidth weight, quality weight, and economic weight. The graph model is checked for splitting into multiple isolated subgraphs due to the removal of the fiber optic cable using a depth-first search. If a split occurs, an alert is issued immediately and manual intervention is required. All weight values ​​are ensured to be valid positive numbers, and abnormal cases with zero length or negative bandwidth are excluded.

6. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S5, the multi-strategy optical path dynamic rerouting algorithm aims to find the optimal migration path for each service. The inputs are the source and destination sites of the service, and the output is several migration paths, including: The system automatically selects or manually specifies optimization strategies based on business level and type, calculates the weight value corresponding to the strategy for each edge in the graph model, and verifies the connectivity between the source site and the destination site. Based on the weights of the selected strategy, the Dijkstra algorithm is used to calculate the first shortest path; for each deviation point of the found path, the path segment is temporarily removed, the shortest path from the deviation point to the destination is calculated, and the segments are merged to form new candidate paths, thereby calculating the second to the Kth shortest paths in sequence; check whether the bandwidth meets the business requirements, verify whether the number of hops is within the tolerance range, and exclude paths that include relocation of optical cables to verify the effectiveness of the path. Remove duplicate or highly similar paths and sort the paths in ascending order according to their policy weight values.

7. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S6, the calculation of feasibility indicators, economic indicators, and risk indicators for each migration path includes: Based on the length field in the optical cable attribute table, sum the physical lengths of all optical cable segments in the path to obtain the total path length; count the number of communication stations the path passes through to obtain the path hop count; estimate the total optical attenuation, total optical attenuation = Σ(optical cable length × attenuation coefficient) + Σ(station hop count × insertion loss); Calculate resource occupancy cost: resource occupancy cost = basic rental fee + bandwidth usage fee + distance surcharge; check if the path requires activation of new ports or devices, and convert port or device requirements into equivalent costs based on equipment purchase and installation costs; Calculate the bottleneck bandwidth utilization rate: bottleneck bandwidth utilization rate = bandwidth required by the service / available bandwidth of the narrowest segment in the path; query the network resource database to identify the number of core services carried by each optical cable, and calculate the proportion of optical cable segments in the path that are shared with core services to obtain the sharing degree of important services.

8. The intelligent migration path generation method for optical cable relocation services according to claim 1, characterized in that, In S7, the weighted scoring method is used to rank each migration path by comprehensive score, which includes: converting indicators with different dimensions into a unified scale, allocating the weight of each indicator using a weight allocation strategy, calculating the comprehensive score of each migration path using a weighted scoring method, and ranking each migration path by comprehensive score. In S7, a multi-attribute decision model is used to rank each migration path by comprehensive score, including: constructing a decision matrix, where the rows of the decision matrix represent each candidate path and the columns represent each evaluation indicator; calculating positive and negative ideal solutions, where the positive ideal solution is the set of optimal values ​​for each indicator and the negative ideal solution is the set of worst values ​​for each indicator; calculating the relative proximity, where relative proximity = distance to the negative ideal solution / (distance to the positive ideal solution + distance to the negative ideal solution); and using the relative proximity as the comprehensive score to rank each migration path by comprehensive score.

9. An intelligent migration path generation system for optical cable relocation services, characterized in that: The system includes: The information acquisition module is used to acquire information about optical cable relocation events. The information includes at least the unique identifier of the target optical cable to be relocated, the relocation time window, and the geographical location of the breakpoint. The impact analysis module is used to query and locate all service circuits passing through the optical cable in the network resource topology database using the target relocated optical cable identifier as an index, generate a list of affected services containing service identifiers and service levels, and read the original path of each service. The path calculation module is used to construct the current communication network into a weighted graph model, and temporarily remove the edge corresponding to the target optical cable from the model or set its weight to infinity according to the migration event; the module adopts a multi-strategy optical path dynamic rerouting algorithm to calculate one or more migration paths from the source site to the destination site for each service in the list of affected services; The solution decision module is used to calculate feasibility, economic and risk indicators for each migration path, and to comprehensively evaluate and rank the paths based on these indicators using weighted scoring or multi-attribute decision models, and output the recommended solution and the advantages and disadvantages analysis of other alternative paths. The report generation module is used to automatically generate standardized relocation plan reports based on the output of the plan decision module.