Self-adaptive communication equipment fault prediction and maintenance system based on artificial intelligence

Abstract

The invention provides a self-adaptive communication equipment fault prediction and maintenance system based on artificial intelligence. According to the system, data analysis and machine learning technologies are utilized, multi-mode sensor data are analyzed in real time, and a fault prediction model is dynamically adjusted to adapt to equipment state changes. Performance evaluation, intelligent maintenance decision, predictive maintenance and fault diagnosis functions and self-healing capability are integrated, and timeliness and accuracy of maintenance are ensured. Through cloud edge cooperative computing, the system quickly responds to data processing, and the security is enhanced. Continuous updating of the knowledge base is maintained, and the system is optimized to adapt to the new technology. The system improves the reliability and maintenance efficiency of equipment, provides a personalized intelligent maintenance scheme for a user, and has wide application prospects and practical values.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, machine learning, communication equipment maintenance and predictive analysis technology, and in particular to an intelligent system that integrates multifunctional modules such as adaptive learning, real-time monitoring, intelligent decision support, self-healing control, user-defined model training, cloud-edge collaborative computing, security enhancement and automatic knowledge base update. Background Art

[0002] In the current field of communication equipment maintenance, traditional maintenance strategies, mainly including regular inspections and emergency repairs after a failure occurs, can no longer meet the growing demand for high efficiency and high reliability. These traditional methods have obvious limitations: they are often discovered only after the failure has occurred and caused damage, resulting in high maintenance costs and extended equipment downtime, which in turn affects service continuity and user satisfaction. In addition, with the continuous advancement of communication technology, communication equipment has become more complex, and the amount of data it generates is huge and diverse, including structured and unstructured data. These data have a high degree of time series characteristics, making it difficult for traditional data processing and analysis methods to cope with.

[0003] Although AI-based fault prediction technologies have begun to be applied to the maintenance of communication equipment in the hope of predicting potential faults by analyzing the historical and real-time data of the equipment, thereby achieving more timely maintenance, these technologies still face several challenges. First, the accuracy of the prediction model is affected by many factors, including the quality and integrity of the data, the generalization ability of the model, and the variability of the equipment operating environment. Second, the existing prediction models generally lack adaptive learning capabilities and are difficult to automatically adjust the prediction strategy according to the real-time changes in the equipment operating status, which limits the accuracy of the prediction and the response speed of the system. Third, different users have different specific needs for communication equipment, but existing systems often cannot provide sufficient customized support to meet the personalized needs of users. Finally, with the increase in system complexity, security issues in the maintenance process have become increasingly prominent, and existing security measures are difficult to resist the increasingly complex network attacks and data leakage risks.

[0004] In response to these challenges, the present invention proposes an adaptive communication equipment fault prediction and maintenance system based on artificial intelligence. The system adopts the latest deep learning algorithms, big data processing technology, adaptive learning mechanism, user-defined model training platform, cloud-edge collaborative computing architecture, and advanced security protection measures to provide a comprehensive, efficient, intelligent, safe and user-customized fault prediction and maintenance solution. The system can analyze and process multimodal data from communication equipment in real time, dynamically adjust the fault prediction model, automatically generate the optimal maintenance plan, realize rapid fault location and self-repair, and ensure the safety and reliability of the entire maintenance process, thereby significantly improving the maintenance efficiency and service quality of communication equipment.

[0005] Technical Solution

[0006] The technical solution of the present invention is a comprehensive artificial intelligence-based adaptive communication equipment fault prediction and maintenance system, which aims to achieve in-depth monitoring, intelligent analysis, predictive maintenance and automated management of communication equipment through a series of highly integrated modules and processes. The following are the specific technical solutions and processes:

[0007] The multi-source data acquisition module realizes real-time monitoring of key parameters of communication equipment by integrating various types of sensors and data receiving interfaces. These sensors include temperature sensors, vibration sensors, sound sensors, current sensors, pressure sensors and frequency sensors, which are used to monitor the temperature change, vibration state, operating noise, current load, pressure change and operating frequency of the equipment respectively. Each sensor has high precision and high sensitivity to ensure the accuracy and reliability of data acquisition; the data acquisition module is also equipped with multiple data receiving interfaces such as RS-232, RS-485, Ethernet and wireless communication to adapt to different models and types of communication equipment. These interfaces not only support traditional serial communication, but also realize high-speed data transmission and remote monitoring, which greatly enhances the compatibility and flexibility of the system; in order to improve the efficiency and accuracy of data processing, the module performs necessary preprocessing before data transmission, including filtering, denoising and normalization. These preprocessing steps help reduce noise and interference in the data and improve the accuracy of subsequent analysis. The module is also designed with an efficient power management system, which supports battery power supply or external power supply to ensure stable operation in various environments; in order to ensure the security of data during transmission, the module uses encryption technology to protect data to prevent data leakage or tampering. Through these designs, the multi-source data acquisition module can not only provide comprehensive and accurate equipment operation data, but also provide a solid foundation for system fault prediction and maintenance decisions.

[0008] The adaptive learning engine uses deep learning algorithms, especially a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN), to process complex data collected by multi-source data acquisition modules. The engine minimizes the loss function where y i is the actual value, is the predicted value, N is the number of samples, and the model weights are continuously optimized. Using the back-propagation algorithm, combined with the gradient descent method, the engine automatically adjusts the network parameters to adapt to changes in the equipment's operating mode. Through time series analysis and feature extraction, the engine is able to identify abnormal patterns in equipment performance and thus predict potential failures. In addition, the introduction of long short-term memory network (LSTM) units enhances the model's understanding of time dependencies and improves the accuracy of predictions. The engine also integrates an online learning mechanism that allows the model to be updated instantly when new data arrives, without the need to retrain the entire network, thereby achieving rapid response to changes in equipment status and continuous optimization. With this approach, the adaptive learning engine not only improves the accuracy of fault predictions, but also enhances the flexibility and adaptability of the system.

[0009] The performance degradation assessment unit uses advanced data analysis technology to monitor key performance parameters of communication equipment in real time, such as temperature, vibration, and current. By establishing a normal operating baseline for equipment performance and using statistical analysis methods such as moving average or exponential smoothing to calculate the mean of performance indicators, the unit can identify abnormal changes in performance through baseline comparison. At the same time, integrated anomaly detection algorithms, such as isolation forests or single-class support vector machines, further identify performance indicators that deviate significantly from the baseline, thereby detecting anomalies. In addition, through time series analysis, such as the autoregressive integrated moving average (ARIMA) model, the trend of equipment performance indicators over time is evaluated, the future value of the performance indicator is predicted, and compared with the current value to identify the trend of performance degradation. When there is a significant deviation between the predicted value and the actual value of the performance indicator, or the performance indicator continues to deviate from the baseline, the system will mark it as a potential fault sign and trigger further analysis and maintenance recommendations. This real-time feedback mechanism ensures that the performance degradation assessment unit can not only monitor and evaluate equipment performance, but also provide real-time data support for the intelligent maintenance decision support system, thereby achieving scientific and timely maintenance plan generation.

[0010] The intelligent maintenance decision support system automatically generates and optimizes maintenance plans by receiving the fault prediction results provided by the adaptive learning engine and combining them with the availability of real-time maintenance resources. The system's built-in resource optimization algorithms, such as heuristic and linear programming methods, ensure the maximization of resource utilization and the minimization of maintenance costs. It not only lists the priority, required resources, estimated time and cost of maintenance tasks in detail, but also provides specific maintenance recommendations and risk assessments. In addition, the intelligent scheduling algorithm monitors resource status in real time and dynamically adjusts resource allocation to ensure the timely execution of maintenance tasks. The system also has a real-time feedback mechanism that can automatically adjust plans and reallocate resources based on on-site feedback to ensure the continuity and effectiveness of maintenance work. All decisions are based on data-driven analysis, and the system continuously improves the accuracy and efficiency of maintenance plans through continuous learning and optimization. Finally, the user interaction interface provided by the system enables the maintenance team to easily access and adjust maintenance plans, enhances the interactivity and flexibility of the system, and significantly improves the efficiency and effectiveness of communication equipment maintenance work.

[0011] The fault prediction and diagnosis integrated module analyzes the equipment operation data through a deep learning model to achieve rapid prediction and location of faults. The module can not only predict the time and type of potential faults, but also quickly determine the specific location and components of the fault by analyzing the changes in equipment parameters and historical fault data. Using technologies such as decision trees, association rule mining and cluster analysis, the module deeply analyzes the cause of the fault and automatically generates maintenance recommendations including recommended maintenance operations, required tools and spare parts. At the same time, the module evaluates the impact of the fault on the equipment operation and system performance, determines the severity of the fault, and automatically determines the priority of the maintenance task based on these evaluation results and the availability of maintenance resources. With real-time update function, the module can dynamically adjust the prediction and diagnosis results according to the latest equipment operation data and maintenance feedback to ensure the timeliness and effectiveness of maintenance work. In addition, the module provides an intuitive user interaction interface, which enables maintenance personnel to easily access and operate, enhancing the interactivity and flexibility of the system. Through these comprehensive functions, the fault prediction and diagnosis integrated module significantly improves the efficiency and accuracy of communication equipment fault management.

[0012] The self-healing control mechanism uses preset thresholds or anomaly detection algorithms to identify minor faults or abnormal conditions by monitoring the key performance indicators of the equipment in real time, such as temperature, vibration, and current. Once a fault is detected, the system automatically executes a predefined parameter adjustment strategy, such as increasing the flow of the cooling system or adjusting the fan speed, or restarting related components such as network interface cards, software processes, or the entire device to restore its normal function. The system's built-in recovery strategy library contains self-healing strategies for different types of faults, and can automatically select the most appropriate recovery operation based on historical fault data and maintenance experience. The self-healing control system with a real-time feedback mechanism can self-learn and optimize according to the results of fault handling. If the automatic recovery operation fails, the system will record the cause of the failure and adjust the strategy. This mechanism significantly reduces the downtime of the equipment and improves the availability of the equipment, while notifying maintenance personnel to conduct more in-depth analysis and intervention when necessary to ensure the full recovery of the system. All automatically executed operations undergo strict safety checks to prevent possible safety risks, thereby providing users with a more intelligent and automated maintenance solution.

[0013] The user-defined model training platform provides a user-friendly custom model training platform that allows users to upload and process data sets, select and configure a variety of pre-trained models such as decision trees, random forests, support vector machines, and neural networks according to their specific needs and application scenarios. The platform supports feature engineering operations such as data cleaning, feature selection, feature extraction, and conversion to ensure data optimization to improve model performance. The integrated high-performance computing environment supports GPU acceleration, enabling users to efficiently train models and perform detailed performance evaluation using tools such as cross-validation, confusion matrix, and ROC curve. After training, the system automatically generates a detailed performance evaluation report and allows users to export the trained model to a standard format for easy deployment on other platforms or applications. The intuitive user interface supports drag-and-drop operations and one-click functions, making the model training and evaluation process simple and easy. In addition, the platform also provides a detailed user manual and online help documents, as well as professional technical support to ensure that users can get timely help and guidance during use. Through these comprehensive functions, the user-defined model training platform significantly improves the flexibility and personalization of model training while ensuring the quality and applicability of the model.

[0014] The cloud-edge collaborative computing architecture achieves rapid data collection, preliminary processing and real-time analysis by deploying edge computing nodes on the communication device side and cloud processing centers in the data center. The edge computing nodes are responsible for collecting device data and performing preliminary data processing and fault prediction, reducing data transmission delays and improving system response speed. The cloud processing center processes large-scale data sets, performs complex data analysis tasks, and stores analysis results. The two work closely together through efficient data synchronization mechanisms and intelligent task allocation strategies to optimize computing resource management and load balancing to ensure rapid response to critical tasks. At the same time, the system has designed load balancing technology to automatically allocate computing tasks to avoid overloading of a single node, thereby enhancing the stability and reliability of the system. In terms of security, all data transmission is carried out through encrypted channels, and security monitoring systems are deployed on both edge nodes and the cloud to prevent unauthorized access and data leakage. In addition, the scalable design of the architecture allows edge nodes to be added or cloud resources to be expanded as needed to adapt to changing computing needs. Users can also monitor computing resource usage through an interactive interface and adjust task allocation as needed to improve system transparency and user control capabilities. This cloud-edge collaborative computing architecture significantly improves data processing efficiency and system response speed while ensuring system flexibility and security.

[0015] The security enhancement module integrates an advanced intrusion detection system and uses machine learning algorithms to analyze network traffic and system logs to monitor and identify potential attack patterns and abnormal behaviors in real time, such as DDoS attacks, SQL injections, and cross-site scripting attacks. At the same time, the Transport Layer Security (TLS) and Secure Sockets Layer (SSL) protocols are implemented to ensure encryption and integrity during data transmission and prevent data interception or tampering. The system also provides role-based access control policies to limit unauthorized access, and automatically performs incident identification, assessment, isolation, repair, and recovery measures through a detailed security incident response process. In addition, critical data is backed up regularly to ensure rapid recovery when data is damaged or lost, and all user operations and system activities are recorded through a comprehensive security audit function to support the investigation of security incidents. The system also provides user education and training resources to enhance user security awareness, and conducts regular compliance checks to ensure that operations comply with security standards and regulatory requirements such as ISO 27001 and GDPR, thereby comprehensively improving the security and reliability of the system.

[0016] The maintenance knowledge base update system uses machine learning algorithms to automatically analyze new fault cases, extract key information and patterns, and integrate this new knowledge into the maintenance knowledge base. Through continuous learning and verification, the system ensures the accuracy and timeliness of the knowledge base content, and uses natural language processing technology to parse technical documents and user feedback to further enrich the knowledge base. In addition, the system has also designed a verification mechanism to verify the accuracy and applicability of the information in the knowledge base by comparing it with actual maintenance cases, ensuring that the guidance and suggestions provided to maintenance personnel are reliable and effective. This automatic update and verification mechanism not only improves the practicality of the knowledge base, but also reduces the need for manual updates, thereby improving the efficiency and quality of maintenance work.

[0017] The system implementation process:

[0018] Start the data acquisition module to collect multimodal operation data of communication equipment in real time.

[0019] The adaptive learning engine is used to process data and dynamically adjust the fault prediction model.

[0020] The performance degradation assessment unit monitors equipment performance and assesses performance degradation trends.

[0021] Use intelligent maintenance decision support system to generate maintenance plans and provide maintenance recommendations.

[0022] The integrated fault prediction and diagnosis module can be used to quickly locate faults and analyze causes.

[0023] Activate self-healing control mechanisms to automatically adjust device parameters or restart components.

[0024] Allow users to train and validate personalized models through a custom model training platform.

[0025] Use cloud-edge collaborative computing architecture to optimize system response time and data processing speed.

[0026] Ensure safety of maintenance operations with the Safety Enhancement Module.

[0027] Continuously update and validate the knowledge base using the maintenance knowledge base update system.

[0028] The above technical solution can bring the following three key technical effects:

[0029] 1. Significantly improve the accuracy of fault prediction: Through the adaptive learning engine, the system can analyze the multimodal data of communication equipment in real time and dynamically adjust the fault prediction model. For example, in the embodiment, a deep learning model is used to process historical fault data. After 50 rounds of iterative training, the accuracy of the model on the validation set reaches more than 90%. This adaptive capability enables the system to predict equipment failures more accurately, so that maintenance measures can be taken in advance and the risk of unexpected downtime can be reduced.

[0030] 2. Optimize the utilization efficiency of maintenance resources: The intelligent maintenance decision support system automatically generates a maintenance plan based on the fault prediction results and the real-time status of maintenance resources. In the embodiment, the system automatically matches technicians and spare parts through a resource optimization algorithm, generates a maintenance task list, and optimizes the allocation of maintenance resources. This automated resource scheduling not only improves the response speed of maintenance work, but also reduces maintenance delays caused by improper resource allocation, significantly improving the utilization efficiency of maintenance resources.

[0031] 3. Enhance the self-healing ability and security of the system: The self-healing control mechanism enables the system to automatically perform recovery operations, such as adjusting signal gain or restarting a subsystem, when a minor fault is detected. In the embodiment, when the system detects a self-healing fault type, the average recovery time is less than 1 minute, significantly reducing the need for manual intervention. At the same time, the security enhancement module successfully defended against multiple simulated network attacks through real-time monitoring and protective measures, ensuring the safety of system operation. This enhancement of self-healing ability and security not only improves the reliability of the equipment, but also ensures the safety of maintenance operations and reduces the risk of service interruption due to faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the system module of the present invention

[0033] Figure 2 Flow chart of the operation steps of the system of the present invention DETAILED DESCRIPTION

[0034] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The specific implementation of the present invention describes in detail the construction and operation process of the adaptive communication equipment fault prediction and maintenance system based on artificial intelligence:

[0036] Implementation steps:

[0037] Data acquisition module startup: Start the multi-source data acquisition module, and the sensors deployed at the key nodes of the communication equipment collect parameters such as temperature, vibration, sound, current, pressure and frequency in real time. The data acquisition frequency is set to 1kHz to ensure that every subtle change in the operation of the equipment is captured.

[0038] Adaptive learning engine training: A historical fault data set containing 1.2 million records was used to train the deep learning model. The model training used convolutional neural networks (CNN) and recurrent neural networks (RNN), with a batch size of 64 and a learning rate of 0.001. After 50 iterations, the model achieved an accuracy of 90% on the validation set.

[0039] Performance degradation assessment: Set the performance threshold of the performance degradation assessment unit. When the equipment performance parameter is lower than the threshold for five consecutive sampling cycles, the system automatically records and analyzes the degradation trend.

[0040] Intelligent maintenance decision support system: Maintenance resources include 10 technicians and 5 sets of spare parts. The system automatically allocates maintenance tasks and resources based on fault prediction results and optimizes maintenance plans.

[0041] Fault prediction and diagnosis integrated module: The integrated module can identify five common fault modes, shortening the average fault diagnosis time to 30 minutes.

[0042] Implementation of self-healing control mechanism: When a minor fault is detected, the system automatically adjusts the equipment parameters, such as restarting the communication module, and it takes an average of 5 seconds to restore service.

[0043] User-defined model training platform: Users upload new training data sets containing 60,000 records through the user-defined model training platform. The training time is an average of 2 hours, and the model accuracy is improved to 92%.

[0044] Cloud-edge collaborative computing architecture: The cloud processing center receives data from 200 edge nodes, and the data processing delay is reduced to 50 milliseconds.

[0045] Security Enhancement Module: The module successfully defended against 1,500 simulated network attacks without any security incidents occurring.

[0046] Maintain the knowledge base update system: the knowledge base is updated daily, 300 new fault cases are added, and the knowledge base size is expanded to 100,000 records.

[0047] System feedback and optimization: The system automatically adjusts the prediction model parameters based on maintenance execution results and user feedback, optimizes the maintenance process, and forms a closed-loop optimization.

[0048] Parameters and results:

[0049] Data acquisition frequency: 1kHz

[0050] Model training accuracy: 90% (validation set)

[0051] Performance degradation threshold: below the baseline for 5 consecutive sampling periods

[0052] Maintenance resources: 10 technicians, 5 sets of spare parts

[0053] Fault identification mode: 5 common faults

[0054] Self-healing control recovery time: 5 seconds on average

[0055] Custom model training data set: 60,000 records, 92% accuracy

[0056] Cloud edge computing latency: 50 milliseconds

[0057] Security attack defense times: 1500 times

[0058] Knowledge base update cases: 300 new cases added every day

[0059] Through the above implementation steps and parameter settings, the system successfully achieved comprehensive monitoring and intelligent maintenance of communication equipment. The accuracy of fault prediction is stable at more than 90%, the maintenance response time is shortened by 60%, and the application of self-healing mechanism reduces the need for manual intervention by 30%, which significantly improves the reliability of communication equipment and the efficiency of maintenance work. At the same time, the operational safety of the system has been fully verified, and no safety accidents have occurred. Through continuous self-learning and knowledge base updates, the system has demonstrated good adaptability and continuous evolution capabilities.

[0060] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An adaptive communication equipment fault prediction and maintenance system based on artificial intelligence, characterized in that: include: Multi-source data acquisition module, used to collect multi-modal operation data of communication equipment in real time, including but not limited to parameters such as temperature, vibration, sound, current, pressure and frequency; Adaptive learning engine, which uses deep learning algorithm, can dynamically adjust the fault prediction model according to the collected data, has self-learning ability, and can autonomously optimize the prediction algorithm according to the changes in the equipment operation status; The performance degradation assessment unit uses advanced data analysis technology to monitor equipment performance indicators in real time, evaluate the degradation trend of equipment performance, and identify potential faults through threshold setting; Intelligent maintenance decision support system, integrating resource optimization algorithm, automatically generates optimal maintenance plan according to the availability of maintenance resources and fault prediction results, and provides maintenance suggestions; fault prediction and diagnosis integrated module, realizes rapid fault location, cause analysis and maintenance suggestions, and provides one-stop fault management service; self-healing control mechanism, can automatically adjust equipment parameters or restart related components when minor faults are detected, realize self-recovery of faults; user-defined model training platform, provides graphical user interface and tool set, enables users to customize training data sets and model parameters according to specific needs and scenarios; cloud-edge collaborative computing architecture, including cloud data processing center and edge computing nodes, is used to realize distributed collaboration of data processing and model training, optimize system response time and data processing speed; The security enhancement module, including intrusion detection system and security protocol, ensures the security of maintenance operations and prevents security risks during the maintenance process; the maintenance knowledge base update system, which adopts machine learning algorithm, can automatically extract knowledge from new fault cases and update the knowledge base to adapt to the development and changes of communication equipment technology. The multi-source data acquisition module collects multi-modal operating data such as temperature, vibration, sound, current, pressure and frequency of communication equipment in real time; these data are transmitted to the adaptive learning engine, which uses deep learning algorithms to analyze data and dynamically adjust the fault prediction model, and the autonomous optimization prediction algorithm is used to adapt to changes in equipment status; the performance degradation assessment unit monitors equipment performance through advanced data analysis technology, evaluates degradation trends, and identifies potential faults through threshold setting; the intelligent maintenance decision support system automatically generates maintenance plans and provides maintenance suggestions based on fault prediction and resource status; the fault prediction and diagnosis integration module realizes rapid fault location and cause analysis, and provides one-stop fault management; the self-healing control mechanism automatically adjusts equipment parameters or restarts components when a minor fault is detected to achieve self-recovery; the user-defined model training platform allows users to customize training data sets and model parameters according to specific needs, thereby enhancing system flexibility; the cloud-edge collaborative computing architecture optimizes data processing and model training through the collaborative work of cloud and edge computing nodes, thereby improving system response speed; The security enhancement module ensures the security of maintenance operations through intrusion detection and security protocols; the maintenance knowledge base update system automatically extracts knowledge from new fault cases and updates the knowledge base to adapt the system to technological development. The close integration and synergy of these modules enable the entire system to achieve comprehensive prediction, rapid response and intelligent maintenance of communication equipment failures, greatly improving maintenance efficiency and equipment reliability.

2. The system as claimed in claim 1, characterized in that The performance degradation assessment unit further includes a performance baseline comparison and anomaly detection algorithm for more accurately identifying performance degradation and failure symptoms.

3. The system as claimed in claim 1, characterized in that The intelligent maintenance decision support system further includes a maintenance resource scheduling algorithm for optimizing the allocation of maintenance teams, tools and spare parts.

4. The system as claimed in claim 1, characterized in that The fault prediction and diagnosis integrated module further includes fault impact assessment and prioritization functions to support rapid response in emergency situations.

5. The system as claimed in claim 1, characterized in that The self-healing control mechanism further includes equipment status monitoring and automatic repair strategies for taking preventive measures before a failure occurs.

6. The system as claimed in claim 1, characterized in that The user-defined model training platform further provides model validation and performance evaluation tools to ensure the effectiveness and accuracy of the customized model.

7. The system as claimed in claim 1, characterized in that The cloud-edge collaborative computing architecture further includes data synchronization and task allocation mechanisms to achieve efficient computing resource management and load balancing.

8. The system as claimed in claim 1, characterized in that The security enhancement module further includes security incident response and recovery processes to deal with potential security threats and attacks.

9. The system as claimed in claim 1, characterized in that The maintenance knowledge base update system further includes knowledge verification and conflict resolution mechanisms to ensure the consistency and reliability of the knowledge base.

10. A method of using the system according to any one of claims 1 to 9, comprising the following steps: s1, start the multi-source data acquisition module to collect multi-modal operation data of communication equipment in real time; s2, uses an adaptive learning engine to process the collected data and dynamically adjust the fault prediction model according to changes in the equipment's operating status; s3, monitor equipment performance indicators through the performance degradation assessment unit, evaluate performance degradation trends, and identify potential failures; s4, using intelligent maintenance decision support system to generate maintenance plans and provide maintenance recommendations based on failure prediction results and the availability of maintenance resources; s5, realize rapid fault location, cause analysis and maintenance suggestions through the integrated fault prediction and diagnosis module; s6, when a minor fault is detected, the self-healing control mechanism is activated to automatically adjust device parameters or restart related components to achieve self-recovery of the fault; s7, allows users to train and verify personalized models through a user-defined model training platform; s8, using cloud-edge collaborative computing architecture to optimize data processing speed and system response time, and achieve efficient computing resource management and load balancing; s9, ensures the security of maintenance operations through security enhancement modules, responds to security incidents and performs recovery processes; s10, use the maintenance knowledge base update system to continuously update and verify the knowledge base based on new failure cases and data to adapt to technological development.

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