Intelligent fault prediction system based on deep learning

Abstract

The invention relates to an intelligent fault prediction system based on deep learning, and aims to improve the fault prediction capability and operation and maintenance efficiency of an IT system. The system comprises a data collection module, a preprocessing module, a deep learning model module, a fault prediction module and an alarm feedback module. The system collects data, performs cleaning, normalization and feature extraction on the data, and performs training and prediction by using deep learning algorithms such as a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The core technology is to analyze operation data in real time by using a deep learning model and generate a fault prediction result and early warning information. The fault prediction module analyzes data in real time, and the alarm and feedback module provides feedback for system optimization. According to the system, the accuracy and the real-time performance of fault prediction are remarkably improved, manual intervention is reduced, the maintenance cost is reduced, and the system stability and the operation and maintenance efficiency are improved. The method is widely applicable to the fields of data centers, network equipment and the like, and has remarkable technical and commercial values.

Description

Technical Field

[0001] The present invention relates to the field of fault management of information technology (IT) systems, and specifically to an intelligent fault prediction system based on deep learning technology. The system is applied to IT operation and maintenance management, and aims to improve the system's fault detection and early warning capabilities by analyzing and predicting system operation data using a deep learning model. More specifically, the present invention is applicable to data center servers, enterprise application software, network equipment, and other IT systems that require fault prediction and monitoring. By deeply analyzing system logs, performance indicators, user behavior and other data in a big data environment, the present invention can achieve efficient and accurate fault prediction, thereby improving the stability and operation and maintenance efficiency of the IT system. Background Art

[0002] In the modern information technology (IT) environment, system reliability and stability are crucial, especially in key areas such as data centers, enterprise application software, and network equipment. As the complexity of IT systems continues to increase, traditional fault management and operation and maintenance methods have been unable to meet the needs of efficient and real-time fault prediction. Current fault management methods mainly include:

[0003] Static data analysis: Many systems rely on static data analysis tools that analyze historical data to identify failure modes. However, this approach is usually weak for real-time failure prediction because it cannot respond to changes in system status and new failure modes in a timely manner.

[0004] Rule-based monitoring: Traditional fault detection relies on preset rules and thresholds to monitor system status. Although this approach can detect some known fault modes, it often cannot cope with complex and unknown fault modes. In addition, the setting of rules requires a lot of experience and manual adjustments, and it is difficult to adapt to the dynamic changes of the system environment.

[0005] Manual intervention: In the current fault management system, operation and maintenance personnel usually need to rely on manual monitoring and analysis to identify and handle faults. This method is inefficient, prone to missing potential problems, and slow to respond when a fault occurs, which may lead to extended system downtime and business losses.

[0006] Although existing technologies can detect and manage system failures to a certain extent, they have the following major drawbacks:

[0007] Slow response: Lack of real-time monitoring and automated early warning functions, fault detection and handling rely on manual intervention, resulting in slow response.

[0008] High maintenance cost: A lot of manual configuration and rule adjustment is required, which increases operation and maintenance costs and complexity.

[0009] Difficult to adapt to changes: The system environment and failure modes are constantly changing, and traditional methods are difficult to dynamically adjust and optimize.

[0010] Insufficient prediction accuracy: Traditional methods are mainly based on historical data and preset rules, which are difficult to deal with new and complex failure modes. Both accuracy and timeliness are limited.

[0011] To overcome these problems, deep learning technology, as an advanced artificial intelligence method, provides a new solution. Deep learning models can automatically learn and analyze large amounts of data to discover complex patterns and potential problems in the data, thereby achieving more efficient and accurate fault prediction and early warning.

[0012] The intelligent fault prediction system of the present invention uses deep learning technology to comprehensively analyze multiple data sources of IT systems, and can monitor system status in real time, predict potential faults, and automatically generate early warning information. This method not only improves the accuracy and timeliness of fault prediction, but also reduces manual intervention and maintenance costs, significantly improving the stability and operation and maintenance efficiency of IT systems. Summary of the invention

[0013] The present invention provides an intelligent fault prediction system based on deep learning, which aims to improve the fault prediction and management capabilities of IT systems through advanced artificial intelligence technology. The system can accurately predict potential faults and generate early warning information by analyzing IT system operation data in real time, thereby improving the stability and operation and maintenance efficiency of the system. Specifically, the technical solution of the present invention includes the following key aspects:

[0014] 1. The intelligent fault prediction system of the present invention mainly includes the following modules:

[0015] Data collection module: used to collect various data from IT systems, including system logs, performance indicators, configuration data, and user behavior data. Data sources cover all aspects of the system to ensure comprehensive capture of system status and potential problems.

[0016] Data preprocessing module: preprocesses the collected data, including data cleaning, data normalization and feature extraction. Data cleaning is used to remove duplicate and erroneous records, data normalization ensures that the data meets the input requirements of the deep learning model, and feature extraction is used to extract valuable information from the raw data.

[0017] Deep learning model module: Use deep learning algorithms to train and predict preprocessed data. This module includes convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The former is used to process local features extracted from system logs, and the latter is used to process time dependencies in time series data.

[0018] Fault prediction module: Analyzes input system data in real time, generates fault prediction results, and generates warning information based on the prediction results. This module can evaluate whether the system has a fault risk based on the output of the deep learning model and generate corresponding warnings.

[0019] Alarm and feedback module: Processes the generated warning information and notifies the operation and maintenance personnel. The warning information is conveyed through email, SMS or system notification, and the actual fault occurrence and model prediction results are recorded for subsequent model optimization.

[0020] 2. Data collection and preprocessing:

[0021] Data collection: The system obtains real-time operation data from IT systems through API interfaces, log collection tools, etc. The data includes system logs (such as operation logs, error logs), performance indicators (such as CPU usage, memory usage), configuration data, and user behavior data.

[0022] Data preprocessing: Data preprocessing includes cleaning, normalization, and feature extraction. Cleaning removes invalid data, normalization ensures that the data meets the model requirements, and feature extraction converts the data into a format that is helpful for model training.

[0023] 3. Training and prediction of deep learning models:

[0024] Model training: Use historical data to train deep learning models. The data set is divided into training set, validation set and test set. The model parameters are adjusted through optimization algorithms to minimize the loss function and improve the prediction accuracy of the model.

[0025] Model prediction: Input real-time data into the trained model for prediction. The model outputs the prediction results and calculates the probability of failure.

[0026] 4. Fault prediction and early warning:

[0027] Real-time analysis: The system analyzes input data in real time to detect potential failure risks. The prediction results generate warnings based on the set thresholds to alert operation and maintenance personnel of possible failures.

[0028] Generate warning: When the prediction result exceeds the set threshold, the system generates warning information and provides detailed fault type, impact range and recommended treatment measures.

[0029] 5. Alarm and feedback mechanism:

[0030] Alarm processing: The warning information is notified to the operation and maintenance personnel through various means (such as email and SMS) to help them respond quickly and handle potential faults.

[0031] Feedback records: Record the actual handling of each warning, including the time of the fault, the scope of impact, the handling results, etc. These records are used to optimize the deep learning model and improve the overall prediction ability of the system.

[0032] Innovation

[0033] Application of deep learning model: The present invention uses deep learning technology to perform fault prediction. Compared with the traditional rule-based method, it can automatically learn and extract complex features in the data, thereby improving the accuracy and flexibility of fault prediction.

[0034] Integrated real-time data analysis: The present invention can analyze data in IT systems in real time, promptly identify and predict potential failures, thereby shortening failure response time and improving system reliability and operation and maintenance efficiency.

[0035] Adaptive prediction capability: By continuously recording and feeding back prediction results, the system can continuously optimize and adjust the deep learning model to adapt to changes in the system environment and new failure modes, maintaining efficient prediction performance.

[0036] Comprehensive data processing: The system uses multiple modules including data collection, preprocessing, model training, prediction and feedback to form a complete fault prediction solution covering all aspects of data processing. Example Example 1: Application of Deep Learning-Based Intelligent Fault Prediction System in Data Center Servers

[0037] 1. Background information: In data centers, server stability is critical. Server failure may lead to service interruption, data loss, or business loss. Therefore, early prediction and handling of server failures is key to maintaining efficient operation of data centers.

[0038] 2. System implementation steps:

[0039] (1) Data collection:

[0040] Server data source: collects operation data from multiple servers in the data center, including system logs, performance indicators (such as CPU usage, memory usage), network traffic, etc.

[0041] Data collection tools: Use open source tools (such as Logstash) and commercial solutions (such as Splunk) for real-time data collection and centralized storage.

[0042] (2) Data preprocessing:

[0043] Data cleaning: remove duplicate logs, fix data format errors, and fill in missing values.

[0044] Data normalization: Standardize the performance indicators to make the data suitable for the input requirements of the deep learning model.

[0045] Feature extraction: Extract statistical features (such as mean, variance) and periodic features (such as daily load changes) from time series data.

[0046] (3) Deep learning model:

[0047] Model selection: Use Long Short-Term Memory (LSTM) networks to process time series data to capture the temporal dependencies in the data.

[0048] Model training: Use historical failure data to train the LSTM model and optimize model parameters to improve prediction accuracy.

[0049] Model Validation: Use cross-validation and test sets to evaluate model performance to ensure it can accurately predict server failures.

[0050] (4) Fault prediction and early warning:

[0051] Real-time data analysis: Real-time data is input into the trained LSTM model for fault prediction.

[0052] Generate warning: When the prediction results indicate that the server may fail, the system generates a warning message to notify the operation and maintenance personnel.

[0053] (5) Alarm and feedback:

[0054] Alarm processing: Send warning information to operation and maintenance personnel via email, SMS or system notification, and provide detailed fault information and processing suggestions.

[0055] Feedback records: record actual fault occurrences and model prediction results for subsequent model optimization and improvement.

[0056] 3. Implementation effect: By implementing the intelligent fault prediction system in this example, the data center can monitor the server status in real time and detect potential faults in a timely manner, thereby reducing the frequency of faults and improving the stability and operation and maintenance efficiency of the system. Example 2: Application of Deep Learning-Based Intelligent Fault Prediction System in Enterprise Application Software

[0057] 1. Background information: In enterprise application software, system failures may affect business continuity and user experience. Predicting and preventing failures is of great significance to ensuring the stable operation of the system and improving user satisfaction.

[0058] 2. System implementation steps:

[0059] (1) Data collection:

[0060] Application data source: collect application logs, performance indicators (such as response time, error rate), user behavior data, etc.

[0061] Data collection tools: Use application performance management tools (such as NewRelic, AppDynamics) and log analysis tools (such as ElasticStack) to collect and store data.

[0062] (2) Data preprocessing:

[0063] Data cleaning: filter out irrelevant data, correct data errors, and handle outliers in logs.

[0064] Data normalization: Normalize application performance indicators to meet the requirements of deep learning models.

[0065] Feature extraction: Extract user behavior patterns, anomaly detection features, and time series features to assist model learning and prediction.

[0066] (3) Deep learning model:

[0067] Model selection: Use convolutional neural networks (CNN) to process local features in log data and recurrent neural networks (RNN) to process time series data.

[0068] Model training: Use historical failure data and normal operation data to train the hybrid model and optimize the model to improve prediction accuracy.

[0069] Model validation: Evaluate model performance using different datasets and tune the model to improve predictions.

[0070] (4) Fault prediction and early warning:

[0071] Real-time data analysis: Real-time application data is input into the trained hybrid model for fault prediction.

[0072] Generate warning: Generate warning information based on the prediction results, and provide detailed fault description and recommended measures.

[0073] (5) Alarm and feedback:

[0074] Alarm processing: Send warning information to relevant personnel through system notification, email or SMS to help quickly respond to potential faults.

[0075] Feedback records: record the actual processing results and fault conditions of the early warning, and provide data support for model retraining and optimization.

[0076] 3. Implementation effect: By implementing the intelligent fault prediction system in this example, enterprises can monitor the operating status of applications in real time, promptly warn of potential faults, reduce system failure rates, and improve business continuity and user satisfaction. Summarize

[0077] This paper proposes an intelligent fault prediction system based on deep learning, which aims to improve the fault prediction ability of IT systems through advanced artificial intelligence technology, thereby significantly improving the stability and operation and maintenance efficiency of the system. The core advantage of this system is that it comprehensively applies deep learning technology to perform real-time analysis and prediction of the operation data of IT systems, and has the following main features and advantages:

[0078] 1. Data-driven prediction capability: The system builds a comprehensive data-driven model by collecting and processing multiple types of data sources, including system logs, performance indicators, configuration data, and user behavior data. Deep learning algorithms (such as LSTM, CNN, etc.) can deeply analyze this data, identify potential failure modes, and achieve high-precision failure prediction.

[0079] 2. Real-time monitoring and early warning: The system can monitor the operating status of the IT system in real time, and input real-time data into the trained model for prediction through the data stream processing framework (such as Apache Kafka, Apache Flink). Once the risk of failure is predicted, the system will immediately generate early warning information to notify the operation and maintenance personnel to take measures to reduce the probability and impact of the failure.

[0080] 3. High degree of automation and intelligence: The system of the present invention reduces the need for manual monitoring and improves the accuracy and response speed of fault detection through automated data processing and intelligent model prediction. The deep learning model continuously learns and optimizes, can adapt to changes in different system environments, and achieve dynamic adjustment and self-improvement.

[0081] 4. Powerful data preprocessing capabilities: The data preprocessing module cleans, normalizes and extracts features from the collected data to ensure the quality of input data and the prediction effect of the model. By processing missing values, removing noise and selecting features, the performance of the model and the reliability of prediction are improved.

[0082] 5. Flexible application scenarios: The system has a wide range of applicability and can be applied to multiple fields such as server monitoring in data centers, fault prediction of enterprise application software, and status analysis of network equipment. According to different application scenarios, the system can be customized to meet the prediction needs in specific environments.

[0083] 6. Continuous optimization and feedback mechanism: By recording and analyzing the actual failure situation and the model prediction results, the system can be continuously optimized and improved. The feedback mechanism ensures the continuous improvement of the system in practical applications, improves the prediction accuracy of the model and the overall performance of the system.

[0084] 7. Economic benefits and business value: The present invention can effectively reduce the frequency of system failures, reduce the time and cost of fault repair, and improve system stability and business continuity. For enterprises, it can significantly reduce the economic losses caused by system failures, improve operational efficiency and user satisfaction.

[0085] In summary, the intelligent fault prediction system based on deep learning of the present invention provides an innovative and effective solution for the operation and maintenance management of IT systems with its advanced technology, real-time monitoring capabilities and efficient prediction functions. It not only improves the accuracy and timeliness of fault prediction, but also promotes the development of automation and intelligence of operation and maintenance, and provides a strong guarantee for the stable operation of modern IT systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is the architecture diagram of the intelligent fault prediction system based on deep learning.

Claims

1. An intelligent fault prediction system based on deep learning, It is characterized in that The system comprises: Data collection module, which is used to collect various types of data from IT systems, including system logs, performance indicators, configuration data, and user behavior data; Data preprocessing module, which is used to clean, normalize and extract features from the collected data to prepare input data suitable for deep learning models; A deep learning model module, used to train and predict the preprocessed data using a deep learning algorithm, wherein the deep learning model module includes a convolutional neural network (CNN), a long short-term memory network (LSTM), or a recurrent neural network (RNN); Fault prediction module, used to analyze real-time data and generate fault prediction results and warning information; The alarm and feedback module is used to process the warning information generated by the system, send notifications to operation and maintenance personnel, and record actual fault conditions and predicted results.

2. The intelligent fault prediction system according to claim 1, It is characterized in that The data collection module includes: System log collection unit, used to collect operation logs, error logs and event logs in IT systems in real time; Performance indicator monitoring unit, used to monitor and collect system CPU usage, memory usage, disk I / O, and network traffic; Configuration data collection unit, used to obtain system hardware configuration, software version and system update information; User behavior data collection unit is used to record user login records and operation behaviors.

3. The intelligent fault prediction system according to claim 1, It is characterized in that The data preprocessing module comprises: Data cleaning unit, used to remove duplicate records in data, fix data format errors and handle missing values; A data normalization unit, used to perform standardization processing on performance indicator data; The feature extraction unit is used to extract statistical features and periodic features from time series data.

4. The intelligent fault prediction system according to claim 1, It is characterized in that The deep learning model module includes: Convolutional Neural Network (CNN) unit to process local features extracted from system logs; Long Short-Term Memory (LSTM) units are used to handle temporal dependencies in time series data.

5. The intelligent fault prediction system according to claim 1, It is characterized in that The fault prediction module is used for: Real-time data analysis unit, which inputs real-time data into the trained deep learning model for fault prediction; The warning generation unit generates fault prediction results and warning information, and sets warning thresholds based on the prediction probability.

6. The intelligent fault prediction system according to claim 1, It is characterized in that The alarm and feedback module includes: Alarm notification unit, used to send warning information to operation and maintenance personnel via email, SMS or system notification; Feedback recording unit, used to record actual fault conditions and prediction results, and provide data support for model retraining and optimization.

7. The intelligent fault prediction system according to claim 1, It is characterized in that The deep learning model module further includes: Model training unit, used to divide historical fault data into training set, validation set and test set, train deep learning model and optimize its parameters; The model validation unit is used to evaluate the model performance and ensure that it has good generalization ability and prediction accuracy.

8. The intelligent fault prediction system according to claim 1, It is characterized in that The system can adapt to different application scenarios and support application needs such as data center server monitoring, enterprise application software fault prediction and network equipment status analysis by adjusting data collection and processing modules.

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