Predictive maintenance model management method for communication information system

Abstract

The invention provides a predictive maintenance model management method for a communication information system, belongs to the field of artificial intelligence, and realizes predictive maintenance of equipment and network nodes by utilizing real-time data in a communication network and combining a PPO algorithm. The method is suitable for a dynamic environment of a large-scale communication network, integrates multi-source data through a data sharing platform, and optimizes the learning and prediction capabilities of the model. Comprising distributed data acquisition, dynamic model updating, maintenance strategy generation, model optimization and real-time data synchronization. The problem that traditional centralized model management cannot adapt to communication network complexity and dynamic change is solved. Through distributed data processing and adaptive optimization of the PPO algorithm, the prediction precision and response speed of the system are improved, and the operation efficiency and maintenance capability of the communication information system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a predictive maintenance model management method for a communication information system. Background Art

[0002] With the rapid development of big data and communication technology, especially driven by 5G communication technology, the scale and complexity of communication information systems are increasing. How to effectively maintain the system and predict faults has become the focus of the industry. The efficient operation and maintenance and fault prediction of communication networks are not only related to the stable operation of the network, but also directly affect the user experience and the service quality of operators. However, the current communication information system maintenance technology faces many challenges, including the complexity of data, the dynamic variability of the network, and the generalization ability of the prediction model.

[0003] At present, predictive maintenance of communication information systems mainly relies on traditional static models and empirical rules. These methods often show poor flexibility and adaptability in the face of ever-changing network environments. In addition, with the rapid growth of data scale in communication networks, a single centralized data processing and model training method has been unable to meet the requirements of real-time and high efficiency. Therefore, how to build a maintenance system that can dynamically adapt to network changes, update prediction models in real time, and make full use of distributed data resources has become an urgent problem to be solved in the industry.

[0004] At present, the predictive maintenance methods of communication information systems can be mainly summarized into the following four categories:

[0005] Static rules and model methods: Traditional static rules and model methods rely on historical data and manually defined rules to make fault predictions and maintenance decisions. This method is usually difficult to cope with complex and changing communication network environments and cannot respond to changes in network status in real time.

[0006] Centralized big data analysis method: Through centralized data collection and analysis, big data technology is used to monitor and maintain the communication network. Although it can process large-scale data, there are great challenges in terms of real-time performance and efficiency, especially the lack of flexibility in responding to dynamic changes in the network.

[0007] Distributed data processing method: Through distributed data collection and processing technology, data analysis tasks are distributed on different network nodes to improve the efficiency and real-time performance of data processing. However, this method has shortcomings in terms of model training and prediction accuracy, and it is difficult to form a unified prediction and decision-making system.

[0008] Federated learning method: Through federated learning technology, data resources distributed on different nodes are integrated for model training while ensuring data privacy. This method can effectively use cross-node data for model optimization, but its current application in communication information systems is mainly focused on data sharing and simple model integration, and has not yet fully realized real-time response and prediction of dynamic network environments.

[0009] The above methods have their own advantages and disadvantages, but they all have certain limitations. First, static rules and model methods cannot adapt to dynamically changing network environments, so their prediction accuracy and real-time performance are poor. Although centralized big data analysis methods can process massive amounts of data, they are not good at real-time performance, especially in terms of network latency and data transmission. Although distributed data processing methods improve data processing efficiency, they are not good at model uniformity and prediction accuracy. Although federated learning methods can integrate data and train models across nodes while ensuring data privacy, the adaptability and generalization capabilities of the model still need to be further improved when faced with complex and changing communication network environments. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a predictive maintenance model management method for a communication information system. The method aims to solve the problem that the model adaptability, fault prediction accuracy and real-time performance in a communication network environment are difficult to meet business needs at the same time. By adopting the PPO algorithm and distributed data processing technology, the present invention realizes accurate modeling and real-time prediction of a dynamic network environment, improves the maintenance efficiency and reliability of the system, and can meet the needs of the communication information system for efficient fault prediction and real-time decision-making.

[0011] The present invention realizes adaptive modeling of the network dynamic environment through the PPO algorithm, utilizes the data sharing platform to realize the real-time integration of multi-source data and the dynamic update of the model, provides efficient and accurate maintenance decisions, and provides strong technical support for the stable operation and efficient maintenance of the communication information system.

[0012] The technical solution of the present invention is:

[0013] A communication information system predictive maintenance model management method, comprising the following steps:

[0014] Data collection and upload: Collect real-time operation data from each node and device in the communication network, including network traffic logs, equipment status data, alarm information and historical fault records, and upload this data to the central data server through the data sharing platform.

[0015] Data preprocessing: Clean, denoise, standardize and convert the collected data to generate high-quality data sets suitable for model training and analysis.

[0016] Feature extraction: Extract key features from preprocessed data and use methods such as time series analysis and graph neural networks to generate feature sets for model training.

[0017] Model training: On the data sharing platform, the Proximal Policy Optimization (PPO) algorithm is used for initial model training. The PPO algorithm learns and optimizes data through the policy network and value network to generate an initial communication network prediction model.

[0018] Model distribution: Distribute the trained initial model to each edge node, and use local data on the edge node to fine-tune and optimize the model to generate a localized prediction model that adapts to different network environments.

[0019] Dynamic model update: Through the data sharing platform, incremental data is regularly collected from each edge node, uploaded to the central data server, and the PPO algorithm is used to dynamically update and optimize the model.

[0020] Real-time prediction and fault warning: On edge nodes and cloud servers, real-time data is used for model reasoning and prediction to generate fault warnings and maintenance recommendations for communication networks.

[0021] Furthermore,

[0022] The data collection and uploading steps include the following:

[0023] Data collection node setting: Deploy data collection devices on key nodes and equipment in the communication network to continuously monitor the network operation status and record relevant data.

[0024] Data transmission and storage: The collected data is transmitted to the data sharing platform through a secure network transmission protocol, and is stored and managed using distributed storage technology.

[0025] Furthermore,

[0026] The data preprocessing step includes the following:

[0027] Data cleaning: Detect and remove noise and outliers in the data to ensure data accuracy.

[0028] Data deduplication and filling: Remove redundant data, ensure data uniqueness, and use interpolation and machine learning filling techniques to complete data.

[0029] Data standardization: Unify data formats and units, and use normalization and standardization techniques to adjust data values ​​to a unified range.

[0030] Furthermore,

[0031] The feature extraction step includes the following contents:

[0032] Time Series Feature Extraction: Extract trend and periodic features from time series data.

[0033] Network feature extraction: Use graph neural networks to extract key features from network topology and device status data.

[0034] Furthermore,

[0035] The model training steps include the following:

[0036] Data batch processing: The data set is input into the PPO algorithm model in batches for training.

[0037] Policy network training: The PPO algorithm learns the failure modes in historical data through the policy network and generates maintenance strategies.

[0038] Value network evaluation: The value network evaluates the decisions of the strategy network and optimizes the decision parameters.

[0039] Furthermore,

[0040] The model distribution step includes the following:

[0041] Model fine-tuning: On each edge node, use local data to fine-tune and optimize the initial model sent from the cloud.

[0042] Localization optimization: Through transfer learning, adjust model parameters and generate a localized model that adapts to each edge node.

[0043] Furthermore,

[0044] The dynamic model updating step includes the following contents:

[0045] Incremental data collection: Collect incremental data from edge nodes and upload them to the central data server regularly.

[0046] Model parameter update: Use incremental data to update and optimize model parameters through the PPO algorithm.

[0047] Furthermore,

[0048] The real-time prediction and fault warning steps include the following:

[0049] Real-time data input: Input the real-time collected data into the PPO model for fault prediction.

[0050] Early warning strategy generation: Generate corresponding fault early warning strategies and maintenance recommendations based on the prediction results.

[0051] Furthermore,

[0052] The data sharing platform adopts a distributed architecture, can achieve real-time synchronization and unified management of multi-source data, and provide efficient distributed data processing capabilities.

[0053] Furthermore,

[0054] The PPO algorithm optimizes the fault prediction strategy of network nodes by combining the strategy network and the value network, thereby improving the prediction accuracy and decision-making ability of the model.

[0055] The beneficial effects of the present invention are

[0056] The present invention solves the problems of existing predictive maintenance technology in communication information systems being difficult to adapt to dynamic changes in the network, insufficient prediction accuracy, and poor real-time performance by introducing the Proximal Policy Optimization (PPO) algorithm and data sharing platform. Specifically, the main beneficial effects of the present invention are as follows:

[0057] Improve prediction accuracy: Through the reinforcement learning mechanism of the PPO algorithm, the model can effectively learn and identify complex failure modes of communication networks from large-scale data. Compared with traditional methods, this invention significantly improves the accuracy of fault prediction, can accurately predict possible equipment failures and network anomalies, and reduces maintenance costs and downtime.

[0058] Enhanced model adaptability: The present invention uses a data sharing platform to achieve distributed management and processing of data and models. Each network node synchronizes data and updates models through the shared platform, ensuring that the model can adapt to the dynamic changes of the network in a timely manner, providing stable and reliable fault prediction capabilities, and improving the generalization performance of the model in different network environments.

[0059] Improve real-time performance and response speed: Through real-time monitoring and dynamic model update mechanism, the PPO algorithm can quickly respond to changes in network status, adjust prediction model parameters in time, and provide real-time fault warnings and maintenance suggestions. Compared with traditional centralized data processing methods, this invention significantly improves the real-time performance and response speed of the system, and can quickly identify and solve network problems.

[0060] Optimize resource utilization: Through distributed data processing and model management, the present invention fully utilizes the computing resources and data resources of each node, reducing the burden of data transmission and centralized processing. By optimizing the training and updating process of the model, the overall resource utilization efficiency of the system is improved, and efficient data management and predictive maintenance are achieved.

[0061] Improve system stability and robustness: The PPO algorithm combined with the distributed architecture of the data sharing platform enhances the stability and robustness of the system. Each node performs collaborative optimization and adjustment of the model through the shared platform to ensure that the system can operate stably in the face of network fluctuations and equipment failures, and provide reliable prediction and maintenance services.

[0062] Reduce maintenance costs: Through accurate fault prediction and real-time maintenance suggestions, the present invention effectively reduces the unplanned downtime and maintenance costs of equipment. By identifying potential problems in advance and optimizing maintenance plans, operating costs are reduced and the economic benefits of the communication network are improved.

[0063] In summary, the present invention provides an efficient, real-time and accurate predictive maintenance method for communication information systems based on the PPO algorithm and data sharing platform, which overcomes the limitations of traditional methods and provides strong technical support for the intelligent management and efficient maintenance of communication networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] The present invention provides a predictive maintenance model management method for a communication information system based on Proximal Policy Optimization (PPO) and a data sharing platform. The method realizes dynamic fault prediction and real-time maintenance of the communication network by building a distributed data collection and processing architecture and combining the reinforcement learning mechanism of the PPO algorithm.

[0067] The specific steps are as follows:

[0068] 1. Data collection and transmission

[0069] Collect real-time operation data from each node and device in the communication network, including network traffic logs, device status data, alarm information, and historical fault records. Through the data sharing platform, each node uploads the data to the central data server in real time to ensure the integrity and real-time nature of the data. The data collection module includes the following steps:

[0070] 1.1 Data collection node setting: Deploy data collection devices on key nodes and devices in the communication network to continuously monitor the network operation status and record relevant data.

[0071] 1.2 Data transmission and storage: The collected data is transmitted to the data sharing platform through a secure network transmission protocol. The platform uses distributed storage technology to uniformly store and manage data to ensure data availability and consistency.

[0072] 1.3 Data formatting: Convert and standardize the uploaded data to generate a data set suitable for model training and analysis.

[0073] 2. Data preprocessing and feature extraction

[0074] Preprocess and extract features of the collected data to provide a high-quality data foundation for subsequent model training. The data preprocessing module mainly includes the following steps:

[0075] 2.1 Data cleaning: Ensure data accuracy by detecting and removing noise and outliers in the data. Use statistical analysis methods and machine learning algorithms to identify and eliminate invalid data.

[0076] 2.2 Data deduplication and filling: Remove redundant data to ensure data uniqueness. At the same time, interpolation and machine learning filling technology are used to fill in missing data to ensure data integrity.

[0077] 2.3 Data standardization: Standardize data from different sources, unify data formats and units, and facilitate subsequent analysis. Use normalization and standardization techniques to adjust data values ​​to a unified range.

[0078] 2.4 Feature extraction: Use time series analysis and graph neural network methods to extract key features from the original data and generate feature sets for model training. Features include device operating status, network traffic change trends, fault frequency, etc.

[0079] 3. Model training and optimization

[0080] On the data sharing platform, the PPO algorithm is used for initial training and optimization of the model. The model training module mainly includes the following steps:

[0081] 3.1 Data batching and processing: The preprocessed data set is input into the PPO algorithm model in batches for data processing and training. Each batch of data contains time series features, network status features, and device status features.

[0082] 3.2 Policy network training: The PPO algorithm learns and optimizes the input data through the policy network. The policy network analyzes historical data to identify potential failure modes in the network and generates corresponding maintenance strategies.

[0083] 3.3 Value Network Evaluation: The value network of the PPO algorithm evaluates and provides feedback on the decisions of the policy network, calculates the value function of each decision, and adjusts and optimizes the policy. The value network optimizes the decision-making ability of the model by evaluating changes in network status and device status.

[0084] 3.4 Model iteration and optimization: Through multiple rounds of iterative training, the parameters of the policy network and the value network are optimized to improve the prediction accuracy and decision-making ability of the model. After each iteration, the prediction results of the model are evaluated and the optimization direction is adjusted to ensure the effectiveness of the model.

[0085] 4. Dynamic model update

[0086] After the model training is completed, the dynamic optimization mechanism of the PPO algorithm is combined to update and adjust the model in real time to ensure that the model can adapt to changes in the communication network. The dynamic model update module includes the following steps:

[0087] 4.1 Real-time data monitoring: Through the data sharing platform, continuously monitor the real-time data in the network and obtain the latest device status and network traffic information.

[0088] 4.2 Model parameter update: Use real-time data to dynamically update the parameters of the PPO algorithm model. Use online learning methods to adjust the parameters of the policy network and value network according to the latest data to ensure that the model can respond to network changes in a timely manner.

[0089] 4.3 Model retraining: Regularly retrain the model, integrate historical data and the latest data, and optimize the model's decision-making ability. Use the reinforcement learning mechanism of the PPO algorithm to comprehensively optimize and update the model to improve the model's long-term prediction ability.

[0090] 4.4 Model performance evaluation: Regularly evaluate the model's prediction performance and maintenance effect through the data sharing platform. Comprehensively evaluate the model's prediction accuracy, fault detection rate, and maintenance efficiency to ensure the reliability and stability of the model.

[0091] 5. Real-time prediction and fault warning

[0092] Deploy the trained PPO algorithm model in the communication network to perform real-time prediction of network status and fault warning. The real-time prediction module mainly includes the following steps:

[0093] 5.1 Real-time data input: Input the real-time data of network nodes and devices into the PPO algorithm model for real-time analysis and prediction.

[0094] 5.2 Fault mode identification: Use the PPO algorithm model to identify potential fault modes and abnormal conditions in the network. The model predicts the possible time and location of faults by analyzing real-time data.

[0095] 5.3 Early warning strategy generation: Generate corresponding fault early warning strategies and maintenance suggestions based on the prediction results. The model combines historical data and real-time data to provide accurate maintenance plans and optimization suggestions.

[0096] 5.4 Maintenance execution and feedback: According to the maintenance strategy generated by the model, the corresponding maintenance operations are executed and the execution results are fed back in real time. The maintenance results are uploaded to the central server through the data sharing platform as reference data for subsequent model optimization.

[0097] Through the above detailed technical scheme, the present invention realizes the intelligent predictive maintenance of the communication information system, solves the problems of low data processing efficiency, poor model adaptability and inaccurate fault prediction in the prior art, and provides comprehensive technical support for the stable operation and efficient maintenance of the communication network.

[0098] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A communication information system predictive maintenance model management method, characterized in that: The following steps are involved: 1) Data collection and upload: Collect real-time operation data from each node and device in the communication network, and upload this data to the central data server through the data sharing platform; 2) Data preprocessing: Clean, denoise, standardize and convert the collected data to generate a data set suitable for model training and analysis; 3) Feature extraction: Extract key features from the preprocessed data and use time series analysis and graph neural networks to generate feature sets for model training; 4) Model training: On the data sharing platform, use the Proximal Policy Optimization (PPO) algorithm for initial model training; 5) Model distribution: Distribute the trained initial model to each edge node, and use local data on the edge node to fine-tune and optimize the model to generate a localized prediction model that adapts to different network environments; 6) Dynamic model update: Through the data sharing platform, incremental data is regularly collected from each edge node, uploaded to the central data server, and the PPO algorithm is used to dynamically update and optimize the model; 7) Real-time prediction and fault warning: On edge nodes and cloud servers, real-time data is used for model reasoning and prediction to generate fault warnings and maintenance recommendations for the communication network.

2. The method according to claim 1, characterized in that The step 1) includes the following contents: Data collection node setting: deploy data collection devices on key nodes and devices of the communication network to continuously monitor the network operation status and record relevant data; Data transmission and storage: The collected data is transmitted to the data sharing platform through a secure network transmission protocol, and is stored and managed using distributed storage technology.

3. The method according to claim 1, characterized in that The step 2) includes the following contents: Data cleaning: Detect and remove noise and outliers in the data to ensure data accuracy; Data deduplication and filling: Remove redundant data, ensure data uniqueness, and use interpolation and machine learning filling techniques to complete data; Data standardization: Unify data formats and units, and use normalization and standardization techniques to adjust data values ​​to a unified range.

4. The method according to claim 1, characterized in that: The step 3) includes the following contents: Time series feature extraction: extract trend and periodic features from time series data; Network feature extraction: Use graph neural networks to extract key features from network topology and device status data.

5. The method according to claim 1, characterized in that The step 4) includes the following contents: Data batch processing: input the data set into the PPO algorithm model in batches for training; Policy network training: The PPO algorithm learns the failure modes in historical data through the policy network and generates maintenance strategies; Value network evaluation: The value network evaluates the decisions of the strategy network and optimizes the decision parameters.

6. The method according to claim 1, characterized in that The step 5) includes the following contents: Model fine-tuning: On each edge node, use local data to fine-tune and optimize the initial model sent from the cloud; Localization optimization: Through transfer learning, adjust model parameters and generate a localized model that adapts to each edge node.

7. The method according to claim 1, characterized in that The step 6) includes the following contents: Incremental data collection: Collect incremental data from edge nodes and upload them to the central data server regularly; Model parameter update: Use incremental data to update and optimize model parameters through the PPO algorithm.

8. The method according to claim 1, characterized in that: The step 7) includes the following contents: Real-time data input: input the real-time collected data into the PPO model for fault prediction; Early warning strategy generation: Generate corresponding fault early warning strategies and maintenance recommendations based on the prediction results.

9. The method according to claim 1, characterized in that: The data sharing platform adopts a distributed architecture to achieve real-time synchronization and unified management of multi-source data.

10. The method according to claim 1, characterized in that The PPO algorithm optimizes the fault prediction strategy of network nodes by combining the strategy network and the value network.

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