Method and system for automatically configuring optical line terminals of different manufacturers based on artificial intelligence

Abstract

The invention relates to the technical field of optical line terminal configuration, in particular to a method and system for automatically configuring optical line terminals of different manufacturers based on artificial intelligence. The method comprises the steps of collecting multi-source heterogeneous data to train a deep learning model, constructing a Transform or RNN-based architecture of a TensorFlow framework, performing cross validation and model compression, then performing optimization through a TensorRT inference engine, developing an intelligent configuration application system, deploying the intelligent configuration application system to a control host, and providing algorithm support for automatic configuration. By constructing an automatic configuration system based on artificial intelligence, the problems of heterogeneous compatibility and intelligent configuration of OLT equipment of multiple manufacturers are systematically solved. In a configuration optimization link, a virtual simulation environment is constructed based on a real network topology, a configuration effect is evaluated through multi-dimensional indexes such as throughput and time delay, potential conflicts are detected in combination with an AI algorithm, an optimization scheme is generated, and the configuration error rate of manual operation is reduced.

Description

Technical Field

[0001] This invention relates to the field of optical line terminal configuration technology, specifically to a method and system for automatically configuring optical line terminals from different manufacturers based on artificial intelligence. Background Technology

[0002] With the large-scale deployment of Fiber to the Home (FTTH) networks, the configuration and management efficiency of Optical Line Terminals (OLTs), as aggregation devices, directly impacts network service quality and operating costs. Significant differences exist between OLT devices from different vendors in hardware architecture, software interfaces, configuration protocols, and command syntax. This necessitates network operators developing separate configuration schemes for each vendor's equipment, relying on manual operation to input commands one by one for each device—a cumbersome process prone to configuration conflicts due to human error.

[0003] Traditional solutions rely on manual input or single-protocol detection for device information collection, making it difficult to integrate multi-source heterogeneous data in real time. Configuration file generation lacks intelligent optimization mechanisms, failing to dynamically adapt to differences in network load and device capabilities. Furthermore, cross-vendor device compatibility management depends on experience accumulation, resulting in high configuration error rates and long debugging cycles, severely restricting the flexibility and reliability of network deployment. Especially in high-reliability scenarios such as 5G bearer networks and enterprise private lines, the rapid iteration of network requirements places higher demands on the rapid configuration, flexible adjustment, and unified management of OLT devices across multiple vendors. However, existing technologies have not yet formed a systematic intelligent configuration system. How to overcome the heterogeneous barriers of multi-vendor devices, build an automated configuration framework with self-learning capabilities, and achieve intelligent mapping and secure verification from user business needs to device-specific configuration commands has become a pressing technical problem to be solved in the intelligent management of optical networks. Summary of the Invention

[0004] This disclosure proposes a method and system for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, with the aim of overcoming at least one defect in the prior art.

[0005] To achieve the above objectives, the technical solution disclosed in this invention is as follows:

[0006] According to one aspect of this disclosure, a method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence is provided, the steps of which include:

[0007] Collect multi-source heterogeneous data to train deep learning models, build architectures based on Transformer or RNN within the TensorFlow framework, optimize them through the TensorRT inference engine after cross-validation and model compression, develop intelligent configuration application systems and deploy them to the control host to provide algorithmic support for automated configuration.

[0008] The control host is connected to the target OLT via a switch. SNMP and LLDP tools are used to automatically obtain the target OLT's model, manufacturer, protocol type, interface status, and terminal connection information, forming a data model of device capabilities and network environment.

[0009] Based on the configuration requirements input by the user, key information is extracted through natural language processing technology and combined with the device data model to generate a preliminary configuration file, realizing the conversion of user requirements into machine-readable instructions;

[0010] Import the initial configuration file into the virtual simulation environment to simulate multiple operating scenarios, detect configuration conflicts and performance issues, and generate optimization solutions to ensure that the configuration does not affect network stability.

[0011] The verified configuration file is sent to the target OLT through an encryption protocol. The configuration status is monitored in real time and changes are recorded. The deep learning model is optimized based on the running data of the target OLT. The model is iterated by importing the manufacturer's configuration data through a reserved interface.

[0012] Furthermore, the multi-source heterogeneous data includes device configuration XML / JSON files, performance metrics, and topological relationship data. Model compression employs pruning and quantization techniques, and the deployed control host hardware includes a processor supporting AVX-512 and an NVIDIA GPU to meet the requirements for efficient model inference.

[0013] Furthermore, the interface status includes optical power and port connectivity status, and the network environment data includes terminal SN, MAC and device identifier. The acquisition process supports multi-port concurrent detection to achieve automatic identification of OLT models, software versions and protocol types (GPON / EPON) from different manufacturers.

[0014] Furthermore, the natural language processing technology includes keyword extraction and intent classification. The machine-readable instructions are issued by a dynamic instruction translation engine, which has a built-in multi-vendor configuration syntax rule library. When generating the configuration file, it verifies device parameter constraints and network resource limitations to ensure that the machine-readable instructions conform to the device capability boundaries.

[0015] Furthermore, the virtual simulation environment is built based on a real network topology, supports custom fault scenario parameters, evaluates the configuration effect through throughput, latency and packet loss rate indicators, and optimizes the scheme by updating the syntax rules of the instruction translation engine.

[0016] Furthermore, the encryption protocol uses AES-256 to protect the transmission of configuration files, device authentication includes digital certificate verification and dynamic token verification, and monitoring data is stored in high-speed NVMe storage according to time and device type.

[0017] Furthermore, during the continuous learning process of the deep learning model, the running data of the target OLT is collected in real time through SNMP Trap or log interface. The comparison and analysis module triggers the optimization of the deep learning model based on a preset success rate threshold, and incremental data training is used to avoid resource consumption.

[0018] Furthermore, the intelligent configuration application system's reserved data input interface supports graphical interface and API batch import, and is compatible with vendor business models in XML / JSON format. The vendor business model includes configuration syntax rules, parameter constraints, and functional module mapping relationships.

[0019] Furthermore, the switch is an industrial-grade multi-port device that connects the control host and multiple vendor OLTs via a 25G SFP28 port, providing an independent transmission channel to enable concurrent processing of data acquisition and configuration distribution.

[0020] According to another aspect of this disclosure, a system for automatically configuring optical line terminals from different manufacturers based on artificial intelligence is provided, for implementing the method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence as described above, comprising:

[0021] The control host is used to deploy trained and optimized deep learning models and intelligent configuration application systems, providing user interaction interfaces and data storage functions. Its hardware configuration supports model inference and multi-task processing, and is used to control the configuration process.

[0022] The intelligent configuration application system integrates a natural language processing module, a dynamic instruction translation engine, a virtual verification module, and a continuous learning module. It converts user requirements into manufacturer-specific configuration instructions, generates and optimizes configuration files, ensures configuration feasibility through simulation verification, and iterates model parameters based on runtime data.

[0023] A switch is used to connect a control host to OLTs from multiple vendors. It supports port expansion and high-speed data transmission, and is used for centralized management and command issuance of multiple devices from a single host, in order to be compatible with connecting heterogeneous devices.

[0024] The beneficial effects of this invention are:

[0025] This invention systematically solves the challenges of heterogeneous compatibility and intelligent configuration of multi-vendor OLT devices by constructing an AI-based automated configuration system. First, it utilizes a deep learning model trained with the TensorFlow framework, combined with Transformer or RNN architectures to process timing instructions. Through adaptive learning of multi-source heterogeneous data, it achieves deep analysis of the configuration syntax, parameter constraints, and functional modules of devices from different vendors. This constructs a dynamic instruction translation engine covering mainstream vendors, accurately translating user natural language requirements into device-specific configuration instructions, improving compatibility coverage and significantly reducing the management complexity of cross-vendor devices. Second, it automatically collects device model, protocol type, interface status, and terminal connection information using tools such as SNMP and LLDP, forming a real-time updated data model of device capabilities and network environment. This supports multi-port concurrent probing and data fusion, providing accurate device status input for configuration generation and avoiding the lag and error rate of manual data entry.

[0026] Furthermore, in the configuration optimization phase, a virtual simulation environment built based on the real network topology evaluates the configuration effect through multi-dimensional indicators such as throughput and latency. AI algorithms are used to detect potential conflicts and generate optimization solutions, reducing the configuration error rate of manual operations. Simultaneously, by dynamically adjusting GPON parameters, such as DBA templates and T-CONT binding, performance optimization effects of increased network throughput and reduced latency are achieved. During configuration distribution, AES-256 encryption protocol and digital certificate authentication mechanism are used to ensure data transmission security and device identity trustworthiness. Combined with high-speed NVMe storage, real-time auditing and fault tracing of configuration change data are achieved, improving the security and traceability of network management.

[0027] Furthermore, the system's reserved data input interface supports the import of vendor business models in XML / JSON format. Incremental data training enables continuous model iteration, allowing the model to adapt to new vendor equipment or configuration changes without retraining the entire model, significantly reducing resource consumption for algorithm updates. The hardware configuration of the control host and the high-speed port design of the industrial-grade switch ensure efficient execution of model inference and concurrent management of multiple devices, breaking through the efficiency bottlenecks and compatibility limitations of traditional manual configuration. It constructs an intelligent configuration system with self-learning capabilities, providing efficient and reliable technical support for the large-scale deployment and dynamic management of optical networks, and improving the intelligence level and resource utilization efficiency of network operation.

[0028] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the following describes the preferred embodiments of the present invention in detail with reference to the accompanying drawings. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, according to one embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram illustrating the principle architecture of a method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, according to one embodiment of the present invention.

[0031] Figure 3 This is an environmental diagram of a system based on artificial intelligence that automatically configures optical line terminals from different manufacturers, according to one embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0033] The present invention provides the following preferred embodiments:

[0034] Example 1: To address the issues of high management complexity and frequent manual intervention caused by heterogeneity in the configuration of multi-vendor optical line terminals (OLTs), such as... Figure 1 and Figure 2 As shown, this embodiment provides a method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, collaboratively building a standardized operating procedure:

[0035] S100: Collects multi-source heterogeneous data to train deep learning models, builds an architecture based on Transformer or RNN within the TensorFlow framework, optimizes it through the TensorRT inference engine after cross-validation and model compression, develops intelligent configuration application systems, and deploys them to the control host to provide algorithmic support for automated configuration.

[0036] S200: Connects the control host and the target OLT through a switch, and automatically obtains the target OLT's model, manufacturer, protocol type, interface status and terminal connection information using SNMP and LLDP tools to form a data model of device capabilities and network environment.

[0037] S300: Based on user-input configuration requirements, it extracts key information through natural language processing technology, combines it with the device data model to generate a preliminary configuration file, and realizes the conversion of user requirements into machine-readable instructions.

[0038] S400: Imports the initial configuration file into the virtual simulation environment, simulates multiple operating scenarios, detects configuration conflicts and performance issues, and generates optimization solutions to ensure that the configuration does not affect network stability.

[0039] S500: The verified configuration file is sent to the target OLT through an encryption protocol, the configuration status is monitored in real time and changes are recorded, the deep learning model is optimized based on the target OLT's running data, and the model is iterated by importing the manufacturer's configuration data through the reserved interface.

[0040] Furthermore, in the deep learning model training phase, multi-source heterogeneous data is collected from device APIs, network management systems (such as Huawei eSight), and log systems (such as Splunk). This includes device configuration XML / JSON files, performance metrics such as CPU utilization and optical power, and network topology data. A Transformer-based time-series processing architecture is built using the TensorFlow framework, and k-fold cross-validation is used to optimize the model's generalization ability. After model training, pruning and quantization techniques are applied for compression, reducing the number of parameters by more than 40%. Then, the TensorRT inference engine is used to optimize FP16 low-precision computation, improving the model inference speed by 3 times. The developed intelligent configuration application system is deployed on the control host, which is equipped with an Intel Xeon Platinum 8368 processor (supporting the AVX-512 instruction set) and dual NVIDIA A100 GPUs, providing hardware support for efficient model operation.

[0041] Furthermore, the control host connects to the target OLT via an industrial-grade multi-port switch, utilizing SNMP v3 and LLDP protocols to automate the acquisition of device information. SNMP is used to obtain parameters such as device model, software version, GPON / EPON protocol type, interface optical power, and connectivity status. LLDP is used to identify the serial number (SN), MAC address, and device identifier of the terminal devices connected to the port. The acquisition process supports concurrent multi-port probing, with the time for acquiring full device information in a single instance controlled within 30 seconds. This forms a dynamic data model containing device capability parameters and network environment status, providing real-time data support for subsequent configuration generation.

[0042] Furthermore, in the user requirement processing phase, the intelligent configuration application system receives configuration requirements input by the user through a graphical interface. It then uses keyword extraction and intent classification algorithms to parse natural language instructions. For example, from the requirement of "configuring the DBA template for the GPON port," it extracts key information such as "GPON" and "DBA template." The system calls device capability parameters from the data model, such as the maximum number of supported T-CONTs and bandwidth allocation mode, to generate a preliminary configuration file that conforms to the device's syntax rules, thus achieving semantic conversion from business requirements to machine-readable instructions.

[0043] Furthermore, the system imports the initially generated configuration file into a virtual simulation environment built on a real network topology. This environment supports customizable parameter configurations for various scenarios, such as load pressure and port failures. By simulating the network's operational status after configuration, the system monitors metrics such as throughput, latency, and packet loss rate in real time, automatically identifying potential problems such as port bandwidth conflicts and protocol version incompatibility. Based on the conflict type, the system generates targeted optimization solutions, such as adjusting DBA template priorities or correcting command syntax formats, ensuring that the configuration solution undergoes logical verification and performance pre-assessment before actual deployment.

[0044] Furthermore, after successful configuration verification, the system uses the AES-256 encryption protocol to send the configuration file to the target OLT via SSH / Telnet. The transmission process includes a dual authentication mechanism of digital certificate verification and dynamic token to ensure the security of command transmission. Simultaneously, the system collects OLT configuration logs and performance data in real time, categorizing them by timestamp and device type and storing them in high-speed NVMe storage for subsequent model optimization. When the accumulated successful configuration data reaches a preset threshold, the system automatically triggers an incremental learning process, updating model parameters through a reserved data import interface (supporting XML / JSON formats). This enables adaptive expansion of configuration rules for new vendor devices, forming a "training-verification-deployment-iteration" optimization mechanism.

[0045] The advantage of this embodiment is that, through a standardized data acquisition process, an intelligent model inference architecture, and a closed-loop verification mechanism, it achieves full automation of the entire process from requirement analysis to configuration and deployment of multi-vendor OLT devices, significantly reducing the complexity and error probability of manual configuration, and providing a feasible technical solution for the efficient management of optical networks.

[0046] Example 2: To address the efficiency and accuracy balance issue of deep learning models in processing multi-source heterogeneous data, this example further refines the specific implementation methods for data processing and hardware deployment. During data acquisition, device configuration files (XML / JSON format, containing structured data such as VLAN configuration and QoS policies), performance metrics (CPU utilization, memory usage, PON port optical power fluctuation curves), and topology data (ONU device hierarchy relationships and physical link routing information) are synchronously acquired from device management interfaces (such as Huawei OLT's iManager northbound API and ZTE's NetNumen N31 interface), the log system (Splunk Enterprise), and the network monitoring platform (Zabbix). When building the model using the TensorFlow framework, temporal feature enhancement is performed on the multi-head attention mechanism of the Transformer architecture, and invalid data noise is filtered out using dynamic masking technology. In the model compression stage, structured pruning based on sensitivity analysis (preserving key weight connections) and 8-bit fixed-point quantization technology are applied sequentially to reduce the model parameter size by more than 60%. At the same time, through FP16 precision optimization of the TensorRT inference engine, the inference latency of a single device configuration is reduced to less than 20ms.

[0047] Furthermore, the control host hardware configuration adheres to the model's high-efficiency operation requirements, employing a server platform equipped with an Intel Xeon Platinum 8368 processor and dual NVIDIA A100 GPUs, coupled with 4TB PCIe 4.0 NVMe SSDs for storing raw training data, ensuring high IO throughput during large-scale data input. The system deployment utilizes Docker containerization technology, managed through a Kubernetes cluster to achieve elastic scaling of the model service, meeting the computational resource scheduling needs of concurrent multi-OLT configurations. It is important to understand that the collaborative design of the aforementioned hardware architecture and data processing flow provides underlying support for real-time inference of the model in complex network environments, improving the efficiency of feature extraction and pattern recognition from heterogeneous data.

[0048] The advantage of this embodiment is that it constructs an efficient processing link from data input to model inference through standardized data acquisition interfaces, targeted model compression strategies, and high-performance hardware deployment, ensuring the feasibility of engineering implementation of deep learning models in multi-vendor OLT configuration scenarios.

[0049] Example 3: To achieve automated and accurate collection of information from OLT devices from different manufacturers, this example further optimizes the technical details of data detection and type identification. The control host connects to the target OLT through an industrial-grade Layer 2 switch and periodically polls for basic device information using the SNMP v3 protocol.

[0050] Obtain the device identifier via OID 1.3.6.1.2.1.1.5.0;

[0051] The software version number is determined by resolving the digits 1.3.6.1.4.1.2011.6.128.1.2.1.1.3.0.

[0052] Identify the PON interface type (GPON or EPON) using 1.3.6.1.2.1.105.1.2.1.1.2.

[0053] Simultaneously, the LLDP protocol (Link Layer Discovery Protocol) is enabled to collect port connection information. By parsing the system name TLV in the LLDPDU, the terminal device SN, MAC address, and device identifier, such as the manufacturer code of the ONU, are obtained. Combined with the port optical power and connectivity status collected by SNMP, a real-time updated data model of device capabilities and network environment is constructed.

[0054] The data acquisition process employs a multi-threaded concurrent mechanism, with a single control host supporting simultaneous probing of 32 OLT devices. The probing interval for each device port is dynamically adjusted to avoid network congestion. For newly connected OLT devices, the system automatically matches and resolves rules using a device identifier database, achieving unattended identification of GPON / EPON protocol type and software version. It should be noted that the aforementioned multi-protocol fusion probing technology and dynamic concurrency strategy effectively solve the compatibility issues of heterogeneous device information acquisition, providing accurate basic data support for subsequent configuration generation.

[0055] The advantage of this embodiment is that, through standardized protocol combinations and intelligent detection mechanisms, it achieves full-dimensional data collection from basic device parameters to network connection status, providing accurate data model support for the differentiated configuration of OLTs from multiple vendors.

[0056] Example 4: To improve the accuracy of converting user configuration requirements into machine instructions and enhance device compatibility, this example further refines the technical implementation of natural language processing and instruction generation. The user interface of the intelligent configuration application system supports both text input and speech-to-text. The input content is processed by the NLP module: First, a bidirectional long short-term memory network (Bi-LSTM) combined with a conditional random field (CRF) is used for keyword extraction. For example, key elements such as "GPON," "port 1," "DBA template," "uplink bandwidth," and "1Gbps" are identified from the instruction "Configure a DBA template on GPON port 1, with uplink bandwidth limited to 1Gbps." Then, a multilayer perceptron (MLP) classifier is used for intent classification to determine the configuration operation type (such as template creation, port parameter adjustment, service instance binding, etc.).

[0057] Furthermore, the dynamic command translation engine incorporates a multi-vendor configuration syntax rule library. This library stores command templates for devices from various vendors in JSON format and associates them with device capability constraints. When generating configuration files, the system first checks the validity of user input by calling device parameters in the data model, such as supported PON protocol types and port quantity limits. If out-of-bounds behavior such as "configuring GPON-specific parameters for EPON devices" is detected, an error message is triggered immediately. For valid input, a command set conforming to the target OLT syntax specification is generated through rule library mapping, and device-specific configuration prefixes are automatically added. It is important to understand that this dual mechanism based on semantic parsing and syntax mapping ensures the compatibility and effectiveness of configuration commands across different vendor devices.

[0058] The advantage of this embodiment is that, through a layered NLP processing architecture and a dynamic syntax mapping mechanism, it achieves accurate conversion of user natural language requirements into device-specific instructions, effectively reducing the risk of configuration errors caused by human understanding of device differences.

[0059] Example 5: To ensure the reliability verification of the configuration scheme before actual deployment, this example further refines the construction and evaluation mechanism of the virtual simulation environment. The simulation environment is built based on the OpenDaily ight controller. A digital twin system is constructed by importing real network topology data, supporting customized load pressure scenarios such as sudden traffic surges, periodic port failures, and protocol compatibility tests such as interactive verification of different versions of the OMCI protocol. After the configuration file is imported, the system starts a multi-dimensional evaluation process: First, it performs syntax compliance verification, checking whether the instructions contain parameters that the target OLT does not support, such as issuing dynamic DBA instructions to devices that only support static bandwidth allocation; then, it simulates 1Gbps uplink / downlink traffic load using traffic generation tools such as Iperf3, monitoring the network throughput, latency, and packet loss rate indicators after configuration. If the throughput drops beyond a preset threshold or continuous packet loss occurs, the configuration conflict point is automatically marked.

[0060] Furthermore, for detected issues, the system generates optimization solutions based on the conflict type: if it's a syntax error, the correct syntax format is directly retrieved from the instruction translation engine's rule base for correction; if it's a resource allocation conflict, such as the total bandwidth of multiple ports exceeding the device's uplink capacity, resource quotas are reallocated using a linear programming algorithm. The optimized solution synchronously updates the instruction translation engine's rule base, forming a closed-loop mechanism of "detection-optimization-learning." It should be noted that the simulation environment supports synchronization with the image configuration of the real OLT, updating the simulation model by periodically capturing device runtime snapshots to ensure the real-time performance and authenticity of the test scenario.

[0061] The advantage of this embodiment is that the highly realistic simulation environment built through digital twin technology enables full-scenario pre-verification of the configuration scheme, effectively avoiding network stability risks that may occur in actual deployment and improving the reliability of automated configuration.

[0062] Example 6: To ensure secure configuration file transmission and traceable operational data, this example further enhances the technical implementation of data transmission and monitoring storage. Configuration file distribution uses the SSH 2.0 protocol channel, and the transmitted content is encrypted using the AES-256-GCM algorithm. The key is generated by the hardware security module HSM and automatically rotates every 24 hours. A two-factor authentication mechanism is also introduced: the target OLT pre-installs the control host's RSA digital certificate, and the public key is stored in the device trust list. Upon establishing a connection, certificate verification is performed first; upon successful verification, a one-time dynamic token is generated to complete identity authentication and prevent unauthorized access. The transmission process employs a segmented verification mechanism, with each 1KB data segment accompanied by an SHA-256 checksum. If the receiving end detects a verification failure, it automatically requests a retransmission to ensure instruction integrity.

[0063] Furthermore, the configuration status monitoring module collects OLT syslog logs and SNMP Trap alarms in real time, categorizing them by device type, timestamp, and configuration operation type and storing them in a high-speed NVMe storage array using a RAID 10 architecture to ensure data redundancy. The storage system supports second-level retrieval response, facilitating subsequent auditing, tracing, and fault location. It's important to understand that the above security mechanisms and storage architecture design not only meet the stringent requirements of the communications industry for data transmission security but also provide comprehensive log support for the long-term stable operation of the system.

[0064] The advantage of this embodiment is that, through a multi-layered security protection system and an efficient data storage architecture, it ensures the security and traceability of the configuration process, and meets the reliability standards of a telecommunications-grade network management system.

[0065] Example 7: To achieve continuous adaptive optimization of the deep learning model, this example further clarifies the technical path and data processing mechanism for model iteration. The target OLT's operational data is collected in real time via SNMP Trap (subscribing to custom events such as OID 1.3.6.1.4.1.2011.6.128.1.3.1.1.1) and log interfaces such as Huawei's diagnostic logs and ZTE's operation logs. The data includes configured port utilization, service traffic curves, and fault alarm records. The comparative analysis module presets a configuration success rate threshold. When the success rate of a certain type of configuration operation falls below the threshold for 24 consecutive hours, the model optimization process is automatically triggered: First, similar failure cases are screened from historical data, and key features, such as device model, configuration parameter combinations, and failure error codes, are extracted to generate an incremental training dataset. Then, transfer learning technology is used to fine-tune only the last two fully connected layers of the model, avoiding the resource consumption caused by retraining the entire model.

[0066] Furthermore, during incremental training, the system prioritizes data samples from high-frequency failure scenarios, using a focal loss function to improve the model's ability to identify difficult examples. After training, the newly generated model parameters gradually replace the old version through a canary release mechanism, first validating on 10% of OLT devices, and then deploying fully after confirming there are no anomalies. It should be noted that this strategy based on dynamic threshold triggering and incremental learning effectively reduces computational resource consumption while ensuring model optimization efficiency, making it suitable for long-term online learning scenarios.

[0067] The advantage of this embodiment is that, through a real-time data-driven closed-loop optimization mechanism, the deep learning model can continuously adapt to new device characteristics and complex network environments, gradually improving the intelligence level of the configuration strategy.

[0068] Example 8: To support the continuous input and model expansion of configuration knowledge from multiple vendors, this example further improves the data feeding mechanism of the intelligent configuration application system. The reserved data feeding interface provides two import methods: a graphical interface and an API interface. The graphical interface supports uploading vendor business model files in XML / JSON format via an interactive form; the system automatically parses the configuration syntax rules, parameter constraints, and functional module mapping relationships in the file. The API interface supports batch import operations, submitting standardized data formats via HTTP POST requests, suitable for rapid access to large-scale vendor data.

[0069] Furthermore, the vendor's business model files adhere to a unified schema definition, including basic device information, instruction syntax libraries, and parameter validation rules. During data import, the system first performs format validation and conflict detection. If syntax differences are found between different versions of the same device model, a version compatibility mapping table is automatically generated. After successful validation, the new model data is integrated into the instruction translation engine's rule base, triggering a small-scale validation of the model to ensure the effectiveness of the newly added knowledge. It's important to understand that this open interface design, compatible with multiple import methods, provides flexible expansion capabilities for the system to support more vendor devices in the future.

[0070] The advantage of this embodiment is that, through a standardized data feeding interface and model integration mechanism, it enables efficient input of configuration knowledge from multiple vendors and continuous iteration of system capabilities, ensuring that the intelligent configuration system can adapt to constantly updated device models and technical standards.

[0071] Example 9: To meet the concurrent management requirements of multiple vendor OLTs, this example further clarifies the hardware design and data transmission mechanism of the network connection layer. The system uses an industrial-grade multi-port switch as the connection hub between the control host and the target OLT. Each SFP28 port is independently connected to an OLT device via single-mode fiber, forming a physically isolated transmission channel to avoid traffic congestion during data interaction between multiple devices. The switch uses port aggregation technology (LACP protocol) to bind two 25G ports to the control host, achieving a bidirectional bandwidth throughput of 40Gbps to meet the high bandwidth requirements of large-scale data acquisition and configuration distribution.

[0072] Furthermore, the data transmission process employs priority queue management (802.1p QoS), marking configuration issuance commands as the highest priority (queue 7) to ensure real-time requirements; data acquisition traffic is marked as medium priority (queue 4), automatically yielding to configuration services when the network is busy. The switch's built-in ACL access control list strictly restricts data flow, allowing only designated IP addresses of the control host (192.168.1.100 / 32) to access the OLT's management ports (SNMP port 161, Telnet port 23, SSH port 22). Connection requests from other IP addresses are rejected, strengthening system security at the network layer. It should be noted that this independent channel design and traffic management strategy based on industrial-grade hardware provides stable network infrastructure support for the concurrent automated configuration of multi-vendor OLTs.

[0073] The advantage of this embodiment is that, through a highly reliable network connection architecture and a fine-grained traffic control mechanism, it achieves efficient concurrent processing of data acquisition and configuration distribution, ensuring the stability and response speed of the system in large-scale device management scenarios.

[0074] Example 10: To solve the engineering integration problem of automated configuration systems for multi-vendor OLT equipment, such as... Figure 3 As shown, this embodiment provides a system for automatically configuring optical line terminals from different manufacturers based on artificial intelligence. It refines the collaborative implementation of the system's hardware architecture and software modules, constructing a complete technical solution including a control host, an intelligent configuration application system, and a high-speed switch. This system architecture aims to achieve full-process automation from user requirement input to device configuration distribution through the rational allocation of hardware resources and modular design of software functions, while simultaneously meeting the practical application requirements of heterogeneous device compatibility, large-scale concurrent processing, and continuous model optimization.

[0075] Specifically, the control host, as the core computing node of the system, employs supercomputing-grade hardware to support complex model inference and multi-task parallel processing. At the hardware level, it utilizes dual Intel Xeon Platinum 8368 processors, whose vector computing capabilities accelerate matrix operations in deep learning models. Combined with dual NVIDIA A10080GB GPUs, it constructs a heterogeneous computing platform supporting asynchronous parallel execution of model inference and data preprocessing. The storage system is configured with four 4TB PCIe 4.0 NVMe SSDs in a RAID 50 architecture, ensuring sequential read / write bandwidth of over 12GB / s while providing data redundancy protection, suitable for storing large-scale training data and real-time monitoring data. The network interface integrates two 25G SFP28 Ethernet ports for connecting to switches for OLT management communication and one InfiniBand HDR interface for cluster data interaction during model training. Management traffic and computing traffic are queued and isolated through Linux kernel traffic control tools to prevent resource contention from affecting real-time configuration. The software deployment adopts containerization technology, which encapsulates the intelligent configuration application system into Docker containers and uses Kubernetes cluster management to achieve dynamic allocation of computing resources. It supports running more than 32 configuration task instances at the same time, meeting the concurrent processing needs of OLTs from multiple vendors.

[0076] Furthermore, the intelligent configuration application adopts a microservice architecture to decouple modules, comprising four core components: The Natural Language Processing (NLP) module, built on the Flask framework, provides both a web interface and a Python API for interaction. User-inputted configuration requirements are first analyzed using the spaCy word segmentation tool, combined with a pre-trained BERT model for intent recognition, outputting structured configuration parameters; The Dynamic Command Translation Engine maintains an independent grammar rule database, stored in PostgreSQL, supporting distributed expansion. After receiving parameters from the NLP module, it retrieves the corresponding command template based on the target OLT's manufacturer and model, automatically generating a configuration script conforming to the device's CLI specifications, embedding parameter constraint verification during the process; The Virtual Verification Module builds an OLT device simulation environment based on QEMU-KVM technology, imports a configuration parser image of the real device, performs grammar compliance verification and resource consumption simulation on the generated configuration script, injects simulated traffic by integrating the Iperf3 tool, and monitors whether the network throughput and latency metrics after configuration meet expectations; The Continuous Learning Module monitors the OLT device's SNMP in real time. Trap alerts and syslog logs automatically extract the context data of the failure case when a configuration failure event is detected, update the deep learning model parameters through incremental learning algorithms, and automatically synchronize the updated model to the production environment after gray-scale testing and verification.

[0077] Furthermore, the industrial-grade switches at the connectivity layer utilize 25G SFP28 devices with port expansion capabilities. Each optical port is independently connected to a target OLT via LC single-mode fiber, achieving physical layer device isolation and avoiding signal interference caused by differences in communication protocols between multiple vendors. The switches employ Dynamic Link Aggregation (LACP) technology, binding two 25G ports to the control host to form a 50Gbps bidirectional transmission channel, meeting the bandwidth requirements for large-scale configuration file distribution. The port management policy adopts the 802.1p QoS mechanism, marking configuration command data streams as the highest priority (queue 7) to ensure real-time requirements; data acquisition traffic is marked as medium priority (queue 4), automatically adjusting the transmission rate during network congestion. Simultaneously, the switches are configured with source IP-based ACL access rules, allowing only specified IP addresses of the control host (e.g., 192.168.100.1) to access the OLT's management ports (SSH 22 / Telnet 23 / SNMP 161), discarding connection requests from other IPs, thus building a security barrier at the network layer. It is important to understand that this hierarchical hardware isolation and flow control design not only ensures the efficiency of concurrent management of multiple devices, but also avoids protocol compatibility issues in heterogeneous network environments.

[0078] During system operation, users input configuration requirements in natural language through the graphical interface of the intelligent configuration application system. For example, enabling dynamic bandwidth allocation for a GPON type OLT on port 2 and setting the uplink peak to 2Gbps. After parsing, the NLP module generates a parameter set. The dynamic command translation engine, based on previously detected device information, calls the corresponding syntax rules to generate device-specific commands such as "interface epon-olt_0 / 2" and "dba-profile add dynamic2048". After the virtual verification module confirms that there are no syntax errors and that the resource allocation is compliant, the commands are sent to the target OLT through the switch's independent channel. After configuration, the continuous learning module monitors device operation data. If it detects an abnormal packet loss rate on the port under high load, it automatically triggers the model optimization process, adjusts the DBA parameter configuration strategy, and synchronizes the optimized rules to the command translation engine, forming a closed-loop control of "requirement input - intelligent processing - verification and distribution - feedback optimization".

[0079] The benefits of this embodiment lie in the deep collaboration of a high-performance hardware platform, modular software architecture, and intelligent network connectivity, which constructs an automated configuration system adapted to heterogeneous environments of multi-vendor OLTs, realizing the full-process technology implementation from user interaction to device management. The heterogeneous computing capabilities of the control host ensure efficient inference of complex models, the layered processing mechanism of the intelligent configuration application system solves the cross-vendor mapping problem from natural language to device commands, and the port expansion and traffic management technology of the high-speed switch supports the concurrent processing of large-scale devices. The organic combination of these three elements provides an engineering solution for the intelligent operation and maintenance of telecom operator networks, effectively reducing the technical threshold and labor costs of multi-vendor device management.

[0080] Although the present invention has been specifically described above with reference to preferred embodiments, it should be understood that the present invention is not limited to the embodiments described above. Various modifications and variations can be made by those skilled in the art without departing from the spirit of the present invention, and such modifications and variations should fall within the scope defined by the appended claims and their equivalents.

Claims

1. A method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, characterized by the following steps: include: Collect multi-source heterogeneous data to train deep learning models, build architectures based on Transformer or RNN within the TensorFlow framework, optimize them through the TensorRT inference engine after cross-validation and model compression, develop intelligent configuration application systems and deploy them to the control host to provide algorithmic support for automated configuration. The control host is connected to the target OLT via a switch. SNMP and LLDP tools are used to automatically obtain the target OLT's model, manufacturer, protocol type, interface status, and terminal connection information, forming a data model of device capabilities and network environment. Based on the configuration requirements input by the user, key information is extracted through natural language processing technology and combined with the device data model to generate a preliminary configuration file, realizing the conversion of user requirements into machine-readable instructions; Import the initial configuration file into the virtual simulation environment to simulate multiple operating scenarios, detect configuration conflicts and performance issues, and generate optimization solutions to ensure that the configuration does not affect network stability. The verified configuration file is sent to the target OLT through an encryption protocol. The configuration status is monitored in real time and changes are recorded. The deep learning model is optimized based on the running data of the target OLT. The model is iterated by importing the manufacturer's configuration data through a reserved interface.

2. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The multi-source heterogeneous data includes device configuration XML / JSON files, performance indicators, and topological relationship data. Model compression employs pruning and quantization techniques. The deployed control host hardware includes a processor supporting AVX-512 and an NVIDIA GPU to meet the requirements of efficient model inference.

3. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The interface status includes optical power and port connectivity status. The network environment data includes terminal SN, MAC and device identifier. The acquisition process supports multi-port concurrent detection to achieve automatic identification of OLT models, software versions and protocol types (GPON / EPON) from different manufacturers.

4. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The natural language processing technology includes keyword extraction and intent classification. The machine-readable instructions are issued by a dynamic instruction translation engine. The dynamic instruction translation engine has a built-in multi-vendor configuration syntax rule library. When generating the configuration file, it verifies device parameter constraints and network resource limitations to ensure that the machine-readable instructions conform to the device capability boundaries.

5. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The virtual simulation environment is built on a real network topology, supports custom fault scenario parameters, evaluates the configuration effect through throughput, latency and packet loss rate indicators, and optimizes the scheme by updating the syntax rules of the instruction translation engine.

6. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The encryption protocol uses AES-256 to protect the transmission of configuration files. Device authentication includes digital certificate verification and dynamic token verification. Monitoring data is stored in high-speed NVMe storage according to time and device type.

7. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, During the continuous learning process of the deep learning model, the running data of the target OLT is collected in real time through SNMP Trap or log interface. The comparison and analysis module triggers the optimization of the deep learning model based on a preset success rate threshold, and incremental data training is used to avoid resource consumption.

8. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The intelligent configuration application system has a reserved data input interface that supports graphical interface and API batch import, and is compatible with the vendor business model in XML / JSON format. The vendor business model includes configuration syntax rules, parameter constraints and functional module mapping relationships.

9. The method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence according to claim 1, characterized in that, The switch is an industrial-grade multi-port device that connects the control host and multiple vendor OLTs via a 25G SFP28 port, providing an independent transmission channel to enable concurrent processing of data acquisition and configuration distribution.

10. A system for automatically configuring optical line terminals from different manufacturers based on artificial intelligence, used to implement the method for automatically configuring optical line terminals from different manufacturers based on artificial intelligence as described in any one of claims 1-9, characterized in that, include: The control host is used to deploy trained and optimized deep learning models and intelligent configuration application systems, providing user interaction interfaces and data storage functions. Its hardware configuration supports model inference and multi-task processing, and is used to control the configuration process. The intelligent configuration application system integrates a natural language processing module, a dynamic instruction translation engine, a virtual verification module, and a continuous learning module. It converts user requirements into manufacturer-specific configuration instructions, generates and optimizes configuration files, ensures configuration feasibility through simulation verification, and iterates model parameters based on runtime data. A switch is used to connect a control host to OLTs from multiple vendors. It supports port expansion and high-speed data transmission, and is used for centralized management and command issuance of multiple devices from a single host, in order to be compatible with connecting heterogeneous devices.

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