Optical cable fiber core detail multi-dimensional management system

Abstract

The invention discloses an optical cable fiber core detail multi-dimensional management system, which relates to the technical field of communication and power resource management, and comprises a unified adaptation layer, an intelligent acquisition integration layer, an edge AI fusion layer and a safety linkage protection layer, the unified adaptation layer, the intelligent acquisition integration layer, the edge AI fusion layer and the safety linkage protection layer cooperate to realize standardized intercommunication, automatic acquisition, intelligent operation and maintenance and three-dimensional protection of optical cable fiber core resources. According to the method, the aspects of cross-platform collaboration, data quality, operation and maintenance efficiency, safety protection and the like are remarkably improved, the cross-platform data intercommunication capability is greatly enhanced, resource information between different manufacturers and new and old systems can be seamlessly circulated, and the problem of data islands is solved; the data accuracy and real-time performance are remarkably improved, the error rate is greatly reduced, the cross-department data synchronization delay is controlled at an extremely low level, a reliable basis is provided for management decision, and the intelligent operation and maintenance level is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of communication and power resource management technology, specifically to a multi-dimensional management system for optical fiber core details. Background Technology

[0002] To address the pain points of traditional optical fiber core management, such as data fragmentation, inefficient operation and maintenance, and insufficient resource utilization, and to meet the needs of industries such as communications and power for refined network management, the Optical Fiber Core Details Multi-Dimensional Management System has emerged as a core digital tool. By integrating multi-dimensional data such as geographic information, network topology, and business relationships, it constructs a "digital twin" model of fiber core resources, enabling full lifecycle management from planning and design, construction and handover to operation and maintenance scheduling and decommissioning. It can not only quickly locate faults and optimize resource allocation through visualization and intelligent analysis, but also break down cross-departmental data barriers and support rapid business activation, ultimately significantly improving network operation and maintenance efficiency, reducing operating costs, and providing solid resource support for the development of emerging businesses such as 5G and smart grids.

[0003] However, in the current technology, the industry lacks unified resource coding, data format and interface standards. There are technical barriers between different vendor systems and between new and old platforms, which makes data interoperability difficult. At the same time, data collection and integration technologies are lagging behind, manual input and OCR recognition are prone to errors, "dumb resources" lack automatic monitoring methods, cross-departmental data synchronization mechanisms are imperfect, and intelligent algorithms have insufficient adaptability. AI models have weak generalization ability and their decision-making logic is difficult to explain. In addition, the connection with on-site operation and maintenance scenarios is not tight, security protection design is lacking, data transmission encryption and access control are inadequate, and environmental risk linkage is not fully considered. As a result, the accuracy and real-time performance of system data are insufficient, fault location is slow in operation and maintenance, service activation efficiency is low, cross-platform and cross-departmental collaboration is hindered, and there are also hidden dangers such as sensitive information leakage and untimely equipment failure warnings. Ultimately, this not only increases manpower and deployment costs, but also restricts the rapid deployment of emerging services such as 5G and smart grids, and makes it difficult to meet the needs of refined and intelligent network management. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-dimensional management system for optical fiber core details to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-dimensional management system for optical fiber core details, comprising a unified adaptation layer, an intelligent acquisition and integration layer, an edge AI fusion layer, and a security linkage protection layer. These layers collaborate to achieve standardized interoperability, automated acquisition, intelligent operation and maintenance, and comprehensive protection of optical fiber core resources. The unified adaptation layer includes a multi-dimensional unified resource coding system, an adaptive data format conversion engine, and a standardized interface adaptation module to break down cross-platform and cross-departmental data interoperability barriers. The intelligent acquisition and integration layer includes a multi-modal dummy resource activation terminal, a cross-departmental real-time synchronization engine, and a full-link data verification module to improve data accuracy and real-time performance. The edge AI fusion layer includes an edge-end adaptive AI model, an AI decision interpretability module, and a real-time on-site operation and maintenance interaction module to enhance intelligent operation and maintenance adaptation capabilities. The security linkage protection layer includes a quantum key encryption transmission mechanism, an environmental risk linkage early warning system, and a full lifecycle security audit module to build a robust security barrier across all scenarios.

[0006] Preferably, the multi-dimensional unified resource coding system integrates five core dimensions: geographic coordinates, fiber core type, business affiliation, lifecycle status, and device number, to generate a unique resource identification code, and is compatible with existing mainstream manufacturer coding rules and supports custom extensions; The formula for generating a unique resource identifier is as follows:

[0007] In the formula: This represents the unique resource identifier of the optical fiber core. This refers to the SHA-256 hash function, used to map multi-dimensional parameters to unique codes. Represents geographic coordinate parameters. Indicates the fiber core type parameter, Indicates the business attribution parameter. Indicates lifecycle state parameters, This indicates the device number parameter.

[0008] Preferably, the adaptive data format conversion engine has built-in parsing modules for four data formats: XML, JSON, binary, and CSV. It automatically identifies the data source structure and establishes a field mapping relationship library through deep learning algorithms.

[0009] Preferably, the multimodal dumb resource activation terminal integrates IoT ZigBee technology and distributed optical fiber sensing technology, and integrates temperature sensors, vibration sensors, optical power sensors and connection status detectors to realize automatic acquisition and uploading of fiber core status data.

[0010] Preferably, the cross-departmental real-time synchronization engine uses a publish-subscribe model to build a distributed message queue, and automatically triggers data push to all associated platforms and terminals when the resource status changes.

[0011] Preferably, the edge-end adaptive AI model is based on a federated learning framework and uses a gradient aggregation algorithm to train the fault prediction model and the routing optimization model, which can dynamically adjust the model weights according to network topology and environmental parameters. The calculation formula for the gradient aggregation algorithm is as follows:

[0012] In the formula: This represents the global model weights after aggregation in round t+1. This represents the total number of edge nodes. Let represent the sample size of the i-th edge node. This represents the local model weight of the i-th edge node after the t-th round of training.

[0013] Preferably, the AI ​​decision interpretability module transforms the AI ​​reasoning process into a visualized link map and outputs concrete data such as fiber core loss curves, route matching scores, and fault impact range.

[0014] Preferably, the quantum key encryption transmission mechanism uses quantum key distribution technology to encrypt sensitive data end-to-end, and combines a 9-level hierarchical access control mechanism to allocate access and operation permissions.

[0015] Preferably, the environmental risk linkage early warning system integrates distributed fiber optic vibration sensing data and meteorological early warning data, identifies risk events through AI algorithms, and triggers graded early warnings by linking equipment protection mechanisms.

[0016] Preferably, the full lifecycle security audit module records all resource operations, data access, fault handling, and permission change behaviors in a complete log, supporting source tracing and analysis by time, role, and operation type. Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves significant improvements in cross-platform collaboration, data quality, operational efficiency, and security protection. Cross-platform data interoperability is greatly enhanced, allowing seamless transfer of resource information between different vendors and old and new systems, thus solving the "data silo" problem. Data accuracy and real-time performance are significantly improved, error rates are greatly reduced, and cross-departmental data synchronization latency is controlled at an extremely low level, providing a reliable basis for management decisions. Intelligent operation and maintenance levels are significantly improved, the AI ​​model's ability to predict and locate faults is enhanced, and the efficiency of business activation and fault handling is greatly improved. 2. Through visual decision support and on-site interactive tools, maintenance personnel have reduced workload, improved operational standardization, and a more comprehensive security protection system. The risk of sensitive data leakage has been effectively curbed, the accuracy of environmental risk warnings has been improved, and the entire process is traceable, ensuring the security and compliance of network operation. Overall, through the collaborative work of the four core layers, the system has significantly reduced operating costs, effectively supported the rapid implementation of emerging businesses, and fully met the needs for refined and intelligent management of optical fiber core resources. Attached Figure Description

[0017] Figure 1 This is a system block diagram of a multi-dimensional management system for optical fiber core details according to the present invention; Figure 2 This is a flowchart of the workflow of a multi-dimensional management system for optical fiber core details according to the present invention.

[0018] In the diagram: 1. Unified Adaptation Layer; 2. Intelligent Data Acquisition and Integration Layer; 3. Edge AI Fusion Layer; 4. Security Linkage and Protection Layer. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Reference Figure 1 and Figure 2 As shown: A multi-dimensional management system for optical fiber core details includes a unified adaptation layer 1, an intelligent acquisition and integration layer 2, an edge AI fusion layer 3, and a security linkage protection layer 4. These layers work together to achieve standardized interoperability, automated acquisition, intelligent operation and maintenance, and comprehensive protection of optical fiber core resources. The unified adaptation layer 1 includes a multi-dimensional unified resource coding system, an adaptive data format conversion engine, and a standardized interface adaptation module to break down cross-platform and cross-departmental data interoperability barriers. The intelligent acquisition and integration layer 2 includes a multi-modal dummy resource activation terminal, a cross-departmental real-time synchronization engine, and a full-link data verification module to improve data accuracy and real-time performance. The edge AI fusion layer 3 includes an edge-end adaptive AI model, an AI decision interpretability module, and a real-time on-site operation and maintenance interaction module to enhance intelligent operation and maintenance adaptation capabilities. The security linkage protection layer 4 includes a quantum key encryption transmission mechanism, an environmental risk linkage early warning system, and a full lifecycle security audit module to build a robust security barrier across all scenarios. The multi-dimensional unified resource coding system integrates five core dimensions: geographic coordinates, fiber core type, business affiliation, lifecycle status, and device number, generating a unique resource identification code. It is compatible with existing mainstream manufacturer coding rules and supports custom extensions. The formula for generating a unique resource identifier is as follows:

[0021] In the formula: This represents the unique resource identifier of the optical fiber core. This refers to the SHA-256 hash function, used to map multi-dimensional parameters to unique codes. Represents geographic coordinate parameters. Indicates the fiber core type parameter, Indicates the business attribution parameter. Indicates lifecycle state parameters, Indicates the device number parameter; The adaptive data format conversion engine has built-in parsing modules for four data formats: XML, JSON, binary, and CSV. It automatically identifies the data source structure and establishes a field mapping relationship library through deep learning algorithms. The multimodal dummy resource activation terminal integrates IoT ZigBee technology and distributed optical fiber sensing technology, and integrates temperature sensors, vibration sensors, optical power sensors and connection status detectors to realize automatic collection and uploading of fiber core status data. The cross-department real-time synchronization engine adopts a publish-subscribe model to build a distributed message queue, and automatically triggers data push to all related platforms and terminals when the resource status changes. The edge-adaptive AI model is based on a federated learning framework and uses a gradient aggregation algorithm to train the fault prediction model and the routing optimization model. It can dynamically adjust the model weights according to network topology and environmental parameters. The calculation formula for the gradient aggregation algorithm is as follows:

[0022] In the formula: This represents the global model weights after aggregation in round t+1. This represents the total number of edge nodes. Let represent the sample size of the i-th edge node. The i-th edge node represents the local model weight after the t-th round of training. The AI ​​decision interpretability module transforms the AI ​​inference process into a visualized link graph and outputs concrete data such as fiber core loss curve, route matching score, and fault impact range. The quantum key encryption transmission mechanism uses quantum key distribution technology to encrypt sensitive data end-to-end. It combines a 9-level hierarchical access control mechanism to allocate access and operation permissions. The environmental risk linkage early warning system integrates distributed optical fiber vibration sensing data and meteorological early warning data. It identifies risk events through AI algorithms and triggers hierarchical early warnings by linking equipment protection mechanisms. The full lifecycle security audit module records resource operations, data access, fault handling, and permission change behaviors in a full log, and supports source tracing analysis by time, role, and operation type.

[0023] In this invention, the system adopts a three-tier architecture of "cloud-edge-terminal" to achieve centralized management and distributed processing of optical fiber core resources across the entire domain. At the cloud level, a distributed server cluster is deployed to undertake unified adaptation, cross-departmental data collaboration, and global security auditing functions. The cluster adopts an elastic scaling design, which can dynamically adjust computing and storage capabilities according to resource scale to ensure efficient processing of massive amounts of fiber core data; at the same time, a master-slave backup mechanism ensures data reliability and avoids service interruptions caused by single points of failure. At the edge level, edge gateways are deployed at key network nodes (such as optical distribution boxes and data center aggregation points) to be responsible for localized data processing and intelligent analysis. The edge gateway has offline working capabilities, enabling it to independently complete data collection, AI inference, and basic operation and maintenance decisions when the network is interrupted. Once the network is restored, it automatically synchronizes data to the cloud, realizing an operation and maintenance mode of "local response + global collaboration". At the terminal level, multi-modal acquisition terminals are embedded in physical resource nodes such as optical distribution boxes, fiber optic splice trays, and splice packages. By integrating IoT and fiber optic sensing technologies, continuous perception of fiber core status and environmental parameters is achieved. At the same time, mobile terminals are provided for operation and maintenance personnel to support on-site operation interaction and data feedback, building an "end-to-end" field-cloud linkage channel. The core objective of Unified Adaptation Layer 1 is to break down data silos and achieve cross-system and cross-departmental resource information interoperability. The multi-dimensional unified resource coding system integrates core dimensions such as geographic coordinates, fiber core type, business affiliation, lifecycle status, and device number, generating a unique identifier through a hash algorithm. This coding is compatible with the coding rules of existing mainstream manufacturers and allows users to extend fields according to business needs, ensuring that resource information from new and old systems and devices from different manufacturers can be uniformly identified and associated. The adaptive data format conversion engine has built-in parsing modules for multiple mainstream data formats, automatically identifying the data source structure of different systems through deep learning algorithms and establishing a dynamic field mapping relationship library. Regardless of whether the original data is in XML, JSON, binary, or CSV format, it can be converted to the system's unified format, resolving data semantic conflicts caused by format differences. The standardized interface adaptation module is compatible with multiple communication protocols, providing a universal access interface for third-party devices such as OTDR testers and optical power meters. No additional adaptation programs need to be developed when devices connect; connection can be completed simply through interface configuration, significantly reducing the technical threshold for cross-device collaboration. The intelligent data acquisition integration layer 2 focuses on the accurate acquisition and real-time flow of data, providing high-quality basic data support for the system. The multi-modal acquisition terminal integrates various sensors such as temperature, vibration, optical power, and connection status, enabling real-time sensing of the fiber core's physical state (e.g., insertion / removal, loss) and environmental parameters (e.g., temperature changes, external vibration). The terminal has an intelligent triggering mechanism that automatically increases the sampling frequency when abnormal conditions are detected (e.g., sudden vibration, power drop), ensuring no critical information is lost. The cross-departmental real-time synchronization engine uses a publish-subscribe model to build a distributed message queue, enabling real-time push notifications of resource status changes. Whether the maintenance department updates the fiber core's usage status or the business department adds new business information, the changed data is instantly synchronized to all related platforms, ensuring consistency in resource information across departments. The end-to-end data verification module improves data accuracy through a cross-validation mechanism. It compares the acquired data with multi-source data such as GIS spatial information, business work orders, and historical maintenance records, automatically identifying and correcting errors entered manually or collected by equipment. The corrections take effect after manual confirmation, forming a dual guarantee of "machine verification + manual review."

[0024] Edge AI Fusion Layer 3 enhances operational efficiency through intelligent technologies, shifting from "passive response" to "proactive prediction." The edge-end adaptive AI model, trained on a federated learning framework, trains fault prediction and routing optimization models. Deployed on edge gateways, these models dynamically adjust parameters based on network topology and environmental characteristics, improving adaptability across different scenarios. By aggregating local training results from various edge nodes, the global model performance is continuously optimized, ensuring the accuracy and generalization of intelligent decisions. The AI ​​decision interpretability module transforms complex AI reasoning processes into intuitive visualizations. Through concrete forms such as link graphs, loss curves, and routing scores, it clearly presents the basis for fault location, optimal route selection logic, and impact assessment, enabling operations personnel to understand and trust AI decisions, lowering the application threshold for intelligent technologies. The on-site real-time interaction module enables bidirectional "cloud-on-site" interaction via a mobile app. After receiving a work order, the app automatically loads navigation paths and operation instructions. After completing the operation, operations personnel perform a quality self-check by capturing on-site images. The system automatically reviews and updates resource status through image recognition, achieving a digital closed loop for the entire operations process.

[0025] The security linkage protection layer 4 constructs a full-scenario security barrier to ensure the security of resource data and operation and maintenance processes. The quantum key encryption transmission mechanism provides end-to-end encryption protection for sensitive data (such as core routing and resource configuration). Combined with a multi-level permission control mechanism, data access and operation permissions are assigned according to roles to prevent unauthorized access and information leakage. The environmental risk linkage early warning system integrates fiber optic vibration sensing data and meteorological information. It uses AI algorithms to identify risk events such as construction damage and extreme weather. It triggers graded early warnings according to the risk level and links with equipment protection mechanisms (such as activating waterproof devices and adjusting loss compensation) to achieve early intervention in risks. The full life cycle security audit module records all operations within the system (such as data modification, work order processing, and permission changes). The logs contain key information such as the operation subject, time, and content, supporting multi-dimensional traceability analysis to ensure that every operation is traceable and to provide a basis for security management and responsibility determination.

[0026] The system workflow revolves around a closed-loop logic of "data acquisition - processing - analysis - execution - optimization," with each stage working in tandem to ensure management efficiency and reliability. In the data acquisition phase, multi-modal terminals collect fiber core status and environmental data in real time, automatically increasing the sampling frequency in abnormal situations. Maintenance personnel supplement on-site information via mobile terminals, and the data is encrypted before being uploaded to the edge gateway or cloud. In the data processing phase, the unified adaptation layer 1 performs format conversion and encoding identification on the raw data, and the end-to-end verification module corrects errors. The cross-departmental synchronization engine pushes standardized data to various related platforms to ensure real-time data interoperability. The intelligent analysis phase... The edge gateway's AI model performs fault prediction and route planning based on standardized data, and the decision interpretability module generates a visual diagnostic report. The system automatically generates maintenance work orders, which are then pushed to the corresponding personnel after being linked to navigation information. During the on-site execution phase, maintenance personnel complete the operation according to the APP guidance, perform quality self-inspection through image recognition, and submit the results. The system updates resource status in real time. During the security and optimization phase, data security is ensured throughout the process through encryption and access control. The risk warning system monitors and intervenes in potential risks in real time, and the audit module records the entire process. The system regularly analyzes operational data, optimizes AI model parameters and collection rules, and continuously improves performance.

[0027] After implementation, this system has achieved significant improvements in cross-platform collaboration, data quality, operational efficiency, and security. Cross-platform data interoperability has been greatly enhanced, enabling seamless transfer of resource information between different vendors and between new and old systems, resolving the "data silo" problem. Data accuracy and real-time performance have been significantly improved, error rates have been drastically reduced, and cross-departmental data synchronization latency has been kept to an extremely low level, providing a reliable basis for management decisions. Intelligent operation and maintenance capabilities have been significantly improved, with enhanced AI models' ability to predict and locate faults, and greatly increased efficiency in service activation and fault handling. Operation and maintenance personnel have experienced reduced workload and improved operational standardization through visual decision support and on-site interactive tools. The security protection system has been further improved, effectively curbing the risk of sensitive data leakage, increasing the accuracy of environmental risk warnings, and ensuring traceability of the entire operation process, guaranteeing the security and compliance of network operation. Overall, through the collaborative work of the four core layers, the system has significantly reduced operating costs, effectively supported the rapid deployment of emerging businesses, and fully met the needs for refined and intelligent management of optical fiber core resources.

[0028] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-dimensional management system for optical fiber core details, characterized in that, It includes a unified adaptation layer (1), an intelligent acquisition integration layer (2), an edge AI fusion layer (3), and a security linkage protection layer (4). The unified adaptation layer (1), the intelligent acquisition integration layer (2), the edge AI fusion layer (3), and the security linkage protection layer (4) work together to achieve standardized interoperability, automated acquisition, intelligent operation and maintenance, and three-dimensional protection of optical fiber core resources. The unified adaptation layer (1) includes a multi-dimensional unified resource coding system, an adaptive data format conversion engine, and a standardized interface adaptation module, which are used to break down the barriers to cross-platform and cross-departmental data interoperability. The intelligent data acquisition integration layer (2) includes a multimodal dumb resource activation terminal, a cross-departmental real-time synchronization engine, and a full-link data verification module, which are used to improve data accuracy and real-time performance. The edge AI fusion layer (3) includes an edge-end adaptive AI model, an AI decision interpretability module, and a real-time on-site operation and maintenance interaction module, which are used to enhance the intelligent operation and maintenance adaptation capabilities. The security linkage protection layer (4) includes a quantum key encryption transmission mechanism, an environmental risk linkage early warning system, and a full life cycle security audit module, which are used to build a solid security barrier for all scenarios.

2. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The multi-dimensional unified resource coding system integrates five core dimensions: geographic coordinates, fiber core type, business affiliation, lifecycle status, and device number, generating a unique resource identification code. It is compatible with existing mainstream manufacturer coding rules and supports custom extensions. The formula for generating a unique resource identifier is as follows: ， In the formula: This represents the unique resource identifier of the optical fiber core. This refers to the SHA-256 hash function, used to map multi-dimensional parameters to unique codes. Represents geographic coordinate parameters. Indicates the fiber core type parameter, Indicates the business attribution parameter. Indicates lifecycle state parameters, This indicates the device number parameter.

3. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The adaptive data format conversion engine has built-in parsing modules for four data formats: XML, JSON, binary, and CSV. It automatically identifies the data source structure and establishes a field mapping relationship library through deep learning algorithms.

4. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The multimodal dummy resource activation terminal integrates IoT ZigBee technology and distributed optical fiber sensing technology, and integrates temperature sensors, vibration sensors, optical power sensors and connection status detectors to realize automatic acquisition and uploading of fiber core status data.

5. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The cross-departmental real-time synchronization engine uses a publish-subscribe model to build a distributed message queue, which automatically triggers data push to all associated platforms and terminals when resource status changes.

6. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The edge-end adaptive AI model is based on a federated learning framework and uses a gradient aggregation algorithm to train a fault prediction model and a routing optimization model. It can dynamically adjust the model weights according to network topology and environmental parameters. The calculation formula for the gradient aggregation algorithm is as follows: ， In the formula: This represents the global model weights after aggregation in round t+1. This represents the total number of edge nodes. Let represent the sample size of the i-th edge node. This represents the local model weight of the i-th edge node after the t-th round of training.

7. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The AI ​​decision interpretability module transforms the AI ​​reasoning process into a visualized link map and outputs concrete data such as fiber core loss curves, route matching scores, and fault impact range.

8. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The quantum key encryption transmission mechanism uses quantum key distribution technology to encrypt sensitive data end-to-end, and combines a 9-level hierarchical access control mechanism to allocate access and operation permissions.

9. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The environmental risk linkage early warning system integrates distributed fiber optic vibration sensing data and meteorological early warning data, and uses AI algorithms to identify risk events and trigger graded early warnings by linking equipment protection mechanisms.

10. The multi-dimensional management system for optical fiber core details according to claim 1, characterized in that, The full lifecycle security audit module records resource operations, data access, fault handling, and permission change behaviors in a complete log, and supports source tracing analysis by time, role, and operation type.

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