Intelligent scheduling and management system for multiple communication devices

Abstract

The invention discloses an intelligent scheduling and management system for multiple communication devices, and the system comprises a multi-modal data fusion module, a deep learning intelligent analysis engine, an AR auxiliary real-time monitoring interface, a self-adaptive scheduling algorithm, a predictive maintenance module, and an intelligent energy management module. The multi-modal data fusion module is used for collecting and processing heterogeneous data from various data sources and executing data preprocessing operation, and the deep learning intelligent analysis engine is used for receiving the data preprocessed by the multi-modal data fusion module for mode recognition and anomaly detection and outputting an analysis result. The AR auxiliary real-time monitoring interface is used for three-dimensionally and visually displaying an analysis result of the deep learning intelligent analysis engine and receiving a user interaction instruction, and the like. The system supports real-time collection and processing of multiple data sources, ensures the integrity and consistency of the data, and can deeply mine mass data by using an advanced machine learning algorithm, so that the system has a wide application prospect. And complex modes and abnormal conditions are identified.

Description

Technical Field

[0001] The present invention relates to the technical field of communication device management, and specifically to an intelligent scheduling and management system for multiple communication devices. Background Art

[0002] With the rapid development of information technology, the scale and complexity of communication networks have been continuously increasing. Traditional communication device management and scheduling methods have gradually revealed many deficiencies. Traditional systems usually rely on manual operations and static rules, and it is difficult to cope with the dynamically changing network environment, resulting in problems such as slow response speed, low decision-making efficiency, high maintenance costs, and energy waste. In addition, in the face of the increasing data volume and diverse data sources, the data processing capabilities and analysis depth of existing systems also appear inadequate.

[0003] A large amount of heterogeneous data is generated by different types of communication devices. These data are scattered in each subsystem, forming "data islands", which are difficult to manage and analyze comprehensively. Traditional systems mainly rely on post-event alarm mechanisms and cannot detect and handle abnormal situations in real time, resulting in long fault response times and affecting service quality.

[0004] Most existing scheduling strategies are based on fixed rules and lack the ability to dynamically adjust according to real-time network status and user behavior, making it difficult to achieve optimal resource allocation.

[0005] At the same time, most existing analysis tools are rule-based systems and lack self-learning and adaptive capabilities, making it difficult to cope with complex network environments and changing user needs. Traditional monitoring interfaces have a single function and lack intuitiveness and interactivity, making it difficult for users to quickly obtain key information and make effective decisions. Summary of the Invention

[0006] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0007] Therefore, the purpose of the present invention is to provide an intelligent scheduling and management system for multiple communication devices, which supports real-time collection and processing of multiple data sources, ensures the integrity and consistency of data, and uses advanced machine learning algorithms. The system can deeply mine massive data, identify complex patterns and abnormal situations.

[0008] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided: An intelligent scheduling and management system for multiple communication devices, comprising: A multi-modal data fusion module for collecting and processing heterogeneous data from multiple data sources and performing data preprocessing operations; A deep learning intelligent analysis engine for receiving the data preprocessed by the multi-modal data fusion module to perform pattern recognition and anomaly detection and outputting analysis results; An AR-assisted real-time monitoring interface for three-dimensional visual display of the analysis results of the deep learning intelligent analysis engine and receiving user interaction instructions; An adaptive scheduling algorithm based on behavior prediction for dynamically adjusting the working state of communication devices according to the analysis results from the deep learning intelligent analysis engine and the user instructions of the AR-assisted real-time monitoring interface; A predictive maintenance module for receiving the device performance data output by the deep learning intelligent analysis engine, combining historical data for fault prediction and generating maintenance suggestions, and sending the maintenance suggestions to the intelligent energy management module and the user; An intelligent energy management module connected to the multi-modal data fusion module, the deep learning intelligent analysis engine and the predictive maintenance module for real-time monitoring of energy consumption and implementing energy efficiency optimization strategies.

[0009] As a preferred solution of a multi-device intelligent scheduling and management system according to the present invention, wherein the multi-modal data fusion module includes: An instant adaptation interface supporting multiple communication protocols for compatible data acquisition of different communication devices; An automatic error recovery mechanism for performing recovery operations and generating a recovery status report when data transmission is abnormal; A data preprocessing unit for performing operations including data cleaning, format conversion and outlier marking.

[0010] As a preferred solution of a multi-device intelligent scheduling and management system according to the present invention, wherein the deep learning intelligent analysis engine includes: An extensible learning framework supporting the import and fusion of knowledge graphs; A real-time update mechanism for online updating of model parameters without affecting system operation, so as to ensure a quick response to newly emerging communication modes and abnormal situations; A security module for encrypting and protecting training data and analysis results.

[0011] As a preferred solution of a multi-device intelligent scheduling and management system according to the present invention, wherein the AR-assisted real-time monitoring interface includes: A virtual reality integration unit providing an immersive device status inspection and remote operation environment; A multi-person collaboration interface allowing multiple users to synchronously view the scene and jointly formulate scheduling strategies; The natural user interaction unit supports gesture recognition and voice command input.

[0012] As a preferred solution of a multi-communication device intelligent scheduling and management system according to the present invention, wherein the adaptive scheduling algorithm realizes dynamic adjustment in the following manner: based on the historical behavior patterns and current network status provided by the deep learning intelligent analysis engine, the reinforcement learning mechanism is used to continuously optimize the resource allocation strategy, and through continuous learning and optimization, the working state of the communication device is dynamically adjusted according to the real-time changing situation to ensure that the system is always at the optimal performance level.

[0013] As a preferred solution of a multi-communication device intelligent scheduling and management system according to the present invention, it further includes a self-diagnosis tool. The self-diagnosis tool uses unsupervised learning algorithms to automatically identify and isolate problem areas without affecting services, and cooperates with the predictive maintenance module and the intelligent energy management module to deeply investigate the problem source through fault tree analysis and root cause analysis methods, generate detailed troubleshooting guides and automated repair scripts, and reduce the need for manual intervention.

[0014] As a preferred solution of a multi-communication device intelligent scheduling and management system according to the present invention, the self-diagnosis tool includes an automated repair subsystem. When the problems detected by the automated repair subsystem can be automatically solved, the automated repair subsystem immediately executes the repair program without waiting for manual confirmation. For complex problems, detailed troubleshooting guides are generated and the best maintenance action paths are recommended to reduce downtime and maintenance costs.

[0015] As a preferred solution of a multi-communication device intelligent scheduling and management system according to the present invention, the intelligent energy management module includes: An energy consumption dynamic adjustment unit, which automatically analyzes the energy usage of the device based on real-time energy consumption monitoring and machine learning algorithms, and coordinates with the predictive maintenance module to formulate an energy-saving plan; A carbon emission tracking unit, which tracks carbon emissions, generates carbon emission reports and pushes emission reduction strategies; A feedback optimization unit, which provides a scheduling plan under energy consumption constraints to the adaptive scheduling algorithm.

[0016] As a preferred solution of a multi-communication device intelligent scheduling and management system according to the present invention, the communication devices include base station devices supporting 5G / 6G, optical fiber transmission devices, and Internet of Things terminal devices, and all devices are connected to the multi-modal data fusion module through a unified interface protocol.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This intelligent scheduling and management system for multiple communication devices realizes the comprehensive intelligent management of communication devices by integrating multiple functional modules such as multi-modal data fusion, deep learning analysis, AR-assisted real-time monitoring, adaptive scheduling, predictive maintenance, and intelligent energy management. This system not only improves the data processing ability and analysis depth, enhances the real-time monitoring and user experience, optimizes resource scheduling and improves decision-making efficiency, but also reduces maintenance costs and downtime, achieving green communication and energy conservation and emission reduction. Generally speaking, the present invention provides strong technical support for the intelligent transformation of the communication industry, with broad application prospects and significant social and economic benefits.

[0018] 2. This intelligent scheduling and management system for multiple communication devices supports the real-time collection and processing of multiple data sources, breaks the "data island" phenomenon, and ensures the integrity and consistency of data. Through the automatic error recovery and data preprocessing functions, the quality of the input data is guaranteed, providing a reliable basis for subsequent advanced analysis. Using advanced machine learning algorithms, the system can deeply mine massive data, identify complex patterns and abnormal situations. Combining pre-trained models and online incremental learning, the system can continuously improve its understanding of industry-specific communication patterns and quickly respond to new communication trends and abnormal events. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in conjunction with the drawings and detailed embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them: Figure 1 It is a block diagram of the structure of an intelligent scheduling and management system for multiple communication devices of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below in conjunction with the drawings.

[0021] Secondly, the present invention is described in detail in conjunction with the schematic diagrams. When detailing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples, and they should not limit the scope of protection of the present invention here. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0022] In order to make the purpose, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below in conjunction with the drawings.

[0023] The present invention provides an intelligent scheduling and management system for multiple communication devices, which supports real-time collection and processing of multiple data sources, ensures data integrity and consistency, and uses advanced machine learning algorithms to enable the system to deeply mine massive data and identify complex patterns and anomalies.

[0024] Figure 1 The following shows a block diagram of the intelligent scheduling and management system for multiple communication devices according to the present invention. As Figure 1 shown, the intelligent scheduling and management system for multiple communication devices includes a multimodal data fusion module, a deep learning intelligent analysis engine, an AR-assisted real-time monitoring interface, an adaptive scheduling algorithm based on behavior prediction, a predictive maintenance module, and an intelligent energy management module.

[0025] The multimodal data fusion module is used to collect and process multiple types of data (such as sensor data, log files, network traffic information data, etc.), and is connected to the deep learning intelligent analysis engine to provide high-quality data input; The deep learning intelligent analysis engine receives the data provided by the multimodal data fusion module for advanced data analysis and anomaly detection, and transmits the analysis results to the AR-assisted real-time monitoring interface and the adaptive scheduling algorithm based on behavior prediction; The AR-assisted real-time monitoring interface receives the analysis results from the deep learning intelligent analysis engine, provides an intuitive three-dimensional visualization operation environment, interacts with the user, and at the same time feeds back the user's scheduling instructions to the adaptive scheduling algorithm; The adaptive scheduling algorithm based on behavior prediction pre-adjusts the working state of the communication device according to the analysis results from the deep learning intelligent analysis engine and the user instructions of the AR-assisted real-time monitoring interface, improving the response speed and decision-making efficiency; The predictive maintenance module obtains the device performance metrics from the deep learning intelligent analysis engine, predicts potential failures in combination with historical data, and sends maintenance suggestions to the intelligent energy management module and the user; The intelligent energy management module is connected to the multimodal data fusion module, the deep learning intelligent analysis engine, and the predictive maintenance module, and is used to monitor and dynamically adjust the energy consumption of the communication device in real time, implement energy efficiency optimization strategies to reduce operating costs and support environmental protection.

[0026] In this embodiment, the intelligent scheduling and management system for the multi-communication device is designed as an integrated platform, which consists of multiple closely connected modules, and each module is responsible for a specific function. The multi-modal data fusion module serves as the data entry point of the system, collecting data from different sources and performing preliminary processing to ensure data quality. This data is then passed to the deep learning intelligent analysis engine, which uses advanced machine learning algorithms to deeply analyze the data, identify patterns and anomalies. The analysis results are not only used for the visual display of the AR-assisted real-time monitoring interface, but also provide a decision-making basis for the adaptive scheduling algorithm based on behavior prediction, enabling the communication device to optimize its operating state according to the predicted behavior pattern. In addition, the predictive maintenance module and the intelligent energy management module work together to ensure the efficient operation of the device and minimize energy consumption.

[0027] In some embodiments, the multi-modal data fusion module not only supports the immediate adaptation between multiple communication protocols, but also connects to the intelligent energy management module through an API interface to ensure seamless compatibility with the latest and traditional communication devices; Moreover, the multi-modal data fusion module also has an automatic error recovery function to ensure the continuity and reliability of data transmission, and reports the recovery status to the deep learning intelligent analysis engine.

[0028] The multi-modal data fusion module is one of the core components of the system. It supports the immediate adaptation between multiple communication protocols, ensuring seamless compatibility between new and old communication devices. Through the API interface, this module is connected to the intelligent energy management module to ensure that the energy usage of all devices is monitored in real time. To ensure the continuity and reliability of data transmission, an automatic error recovery mechanism is embedded in the module, which can quickly resume normal operation when problems occur and report the recovery status to the deep learning intelligent analysis engine for subsequent analysis and improvement. At the same time, this module has powerful data preprocessing capabilities, including data cleaning, format conversion, outlier detection, etc., to ensure that the data input into the deep learning intelligent analysis engine reaches the best quality Optionally, the deep learning intelligent analysis engine includes an extensible learning framework that allows the import of knowledge graphs and protects the security of training data through a security module; The deep learning intelligent analysis engine combines pre-trained models and online incremental learning to continuously improve its understanding of industry-specific communication patterns and anomaly recognition capabilities, and at the same time shares the analysis results with the predictive maintenance module to facilitate the advance planning of maintenance activities.

[0029] The deep learning intelligent analysis engine is the brain of the system. It adopts an extensible learning framework that allows the import of knowledge graphs to enhance the understanding of industry-specific communication patterns. The security module safeguards the security of training data and prevents the leakage of sensitive information. By combining pre-trained models and online incremental learning, the engine can continuously improve its ability to identify anomalies and response speed. Through sharing analysis results, it closely cooperates with the predictive maintenance module to plan maintenance activities in advance and reduce the occurrence of unexpected failures. Additionally, the engine has a real-time update mechanism that can adjust model parameters online without affecting the normal operation of the system, thus quickly adapting to new communication patterns and anomalies.

[0030] In some embodiments, the AR-assisted real-time monitoring interface is not limited to static information display. It is also connected to the cloud deployment mode through the network, supports interactive simulation and rehearsal of different scheduling strategies, and provides natural user interface technologies (such as gesture recognition, voice commands) to enhance the user experience. The AR-assisted real-time monitoring interface can generate detailed reports and suggestions to help users make more informed decisions and send these decision instructions to the adaptive scheduling algorithm.

[0031] The AR-assisted real-time monitoring interface provides users with an intuitive three-dimensional visualization environment through which users can interact with the system and issue scheduling instructions. The interface is not limited to static information display. It also supports interactive simulation and rehearsal of different scheduling strategies to help users evaluate the effects of various solutions. Through natural user interface technologies such as gesture recognition and voice commands, the user experience is enhanced. The interface can also generate detailed reports and suggestions to assist users in making more informed decisions and send these decision instructions to the adaptive scheduling algorithm for execution.

[0032] In some embodiments, the adaptive scheduling algorithm based on behavior prediction combines historical behavior patterns from the deep learning intelligent analysis engine and the current network state, and adopts a reinforcement learning mechanism to continuously optimize the resource allocation strategy to ensure that critical tasks are given priority.

[0033] The adaptive scheduling algorithm is based on the historical behavior patterns and current network state provided by the deep learning intelligent analysis engine, and uses a reinforcement learning mechanism to continuously optimize the resource allocation strategy. This ensures that critical tasks are given priority, improves the system's response speed and decision-making efficiency. Through continuous learning and optimization, the algorithm can dynamically adjust the working state of communication devices according to real-time changes, ensuring that the system always operates at the optimal performance level.

[0034] Furthermore, the multi-modal data fusion module also supports data preprocessing functions, including but not limited to data cleaning, format conversion, and outlier detection, to ensure the quality of the data input into the deep learning intelligent analysis engine. This module can automatically identify and mark suspicious data for subsequent analysis or manual review.

[0035] The predictive maintenance module obtains device performance metrics from the deep learning intelligent analysis engine and combines historical data to predict potential failures. By collaborating with the intelligent energy management module, it can propose specific maintenance suggestions to guide users to take preventive measures and avoid failures. This module also supports an automated repair function that can automatically solve problems whenever possible, reducing the need for manual intervention, lowering maintenance costs, and shortening downtime.

[0036] The deep learning intelligent analysis engine further includes a real-time update mechanism that can update model parameters online without affecting system operation, thus ensuring a rapid response to newly emerging communication patterns and anomalies. This mechanism is implemented through an incremental learning algorithm, and only a small amount of new data is required to adjust the model performance.

[0037] The intelligent energy management module focuses on real-time monitoring of the energy consumption of communication devices and uses machine learning algorithms to analyze energy usage. It works in collaboration with the predictive maintenance module to develop energy-saving solutions, such as automatically switching to a power-saving mode or adjusting the operating frequency during low-load periods to achieve the effect of peak shaving and valley filling. In addition, this module also supports carbon emission tracking and reporting functions to help enterprises meet environmental regulations while ensuring that service quality is not affected. Through a feedback mechanism, it provides energy consumption optimization suggestions to users and the adaptive scheduling algorithm, promoting the development of green communication.

[0038] In some embodiments, the system further includes a self-diagnosis tool that uses unsupervised learning algorithms to automatically identify and isolate problem areas without affecting services, and collaborates with the predictive maintenance module and the intelligent energy management module to deeply investigate the source of problems through methods such as fault tree analysis and root cause analysis, generating detailed troubleshooting guides and automated repair scripts to reduce the need for manual intervention. The self-diagnosis tool also includes an automated repair subsystem that immediately executes the repair program when the detected problem can be automatically solved without waiting for manual confirmation; for complex problems, it generates detailed troubleshooting guides and recommends the best maintenance action path to reduce downtime and maintenance costs.

[0039] The self-diagnosis tool utilizes unsupervised learning algorithms to automatically identify and isolate problem areas without disrupting the service, and deeply investigates the source of problems through methods such as fault tree analysis and root cause analysis. For simple problems, the automated repair subsystem immediately executes the repair procedure without waiting for manual confirmation; for complex problems, it generates detailed troubleshooting guides and recommends the best maintenance action paths to reduce downtime and maintenance costs. This feature greatly improves the reliability and availability of the system and reduces the need for manual intervention.

[0040] In some embodiments, the AR-assisted real-time monitoring interface integrates virtual reality technology, allowing users to perform device status checks and remote operations in a fully immersive environment; The AR-assisted real-time monitoring interface supports a multi-person collaboration mode, where multiple users can synchronously view the same scene over the network and jointly formulate scheduling strategies to improve team work efficiency.

[0041] The AR-assisted real-time monitoring interface further integrates virtual reality technology, enabling users to check device status and perform remote operations in a fully immersive environment. The multi-person collaboration mode allows multiple users to synchronously view the same scene over the network and jointly formulate scheduling strategies, significantly enhancing team work efficiency. This immersive experience makes users feel as if they are on the scene, greatly enhancing the users' sense of participation and control.

[0042] In some embodiments, the intelligent energy management module automatically analyzes the energy usage of devices based on real-time energy consumption monitoring and machine learning algorithms, and coordinates with the predictive maintenance module to formulate energy-saving solutions, such as automatically switching to a power-saving mode or adjusting the working frequency during low-load periods to achieve the effect of peak shaving and valley filling; The intelligent energy management module also supports carbon emission tracking and reporting functions, helping enterprises meet environmental protection regulatory requirements, while ensuring that the service quality is not affected, and providing energy consumption optimization suggestions to users and the adaptive scheduling algorithm through a feedback mechanism.

[0043] The intelligent energy management module not only focuses on the energy consumption management of devices but also is committed to environmental protection. Through the carbon emission tracking and reporting functions, enterprises can better understand their carbon footprint and take corresponding emission reduction measures. The energy-saving solutions formulated by the module promote sustainable development while ensuring service quality, reflecting the enterprise's responsibility and commitment in green environmental protection.

[0044] The patented intelligent scheduling and management system for multiple communication equipment realizes comprehensive intelligent management of communication equipment by integrating multiple functional modules such as multimodal data fusion, deep learning analysis, AR-assisted real-time monitoring, adaptive scheduling, predictive maintenance and intelligent energy management. The system not only improves data processing capabilities and analysis depth, enhances real-time monitoring and user experience, optimizes resource scheduling and improves decision-making efficiency, but also reduces maintenance costs and downtime, and realizes green communication and energy conservation and emission reduction. In general, the present invention provides strong technical support for the intelligent transformation of the communication industry, and has broad application prospects and significant social and economic benefits.

[0045] In this embodiment, the intelligent dispatching and management system of multiple communication devices is specifically applied as follows: S1. Data collection and preprocessing: As the front end of the system, the multimodal data fusion module is responsible for collecting raw data from various sources (such as sensors, log files, network traffic, etc.) and performing necessary preprocessing steps, including data cleaning, format conversion, and outlier detection. This step ensures the quality of data entering the system and lays a solid foundation for subsequent advanced analysis.

[0046] The module also supports instant adaptation between multiple communication protocols and is connected to the intelligent energy management module through an API interface to ensure seamless compatibility between new and old communication equipment and to guarantee the continuity and reliability of data transmission.

[0047] S2. Advanced data analysis and anomaly detection: The Deep Learning Intelligent Analysis Engine receives high-quality data from the Multimodal Data Fusion Module and uses advanced machine learning algorithms to conduct in-depth analysis to identify patterns and anomalies. It combines pre-trained models and online incremental learning to continuously improve the understanding of industry-specific communication patterns and anomaly identification capabilities.

[0048] The engine's security module protects the security of training data and prevents sensitive information from being leaked. At the same time, it updates model parameters in real time, quickly responds to new communication patterns and abnormal situations, and ensures the flexibility and adaptability of the system.

[0049] S3, AR-assisted real-time monitoring and user interaction: The AR-assisted real-time monitoring interface provides users with an intuitive 3D visual operating environment that not only displays static information, but also supports interactive simulation and preview of different scheduling strategies. Users can enhance the user experience through natural user interface technologies such as gesture recognition and voice commands.

[0050] The interface can generate detailed reports and suggestions to help users make more informed decisions and send these decision instructions to the adaptive scheduling algorithm for execution. In addition, the interface integrates virtual reality technology, allowing multiple users to view the same scene synchronously and jointly formulate scheduling strategies, thereby improving team work efficiency.

[0051] S4. Adaptive Scheduling and Resource Optimization: The adaptive scheduling algorithm based on behavior prediction, according to the historical behavior patterns and current network status provided by the deep learning intelligent analysis engine, uses a reinforcement learning mechanism to continuously optimize the resource allocation strategy. It pre-adjusts the working state of communication devices to ensure that critical tasks are given priority, improving the system's response speed and decision-making efficiency.

[0052] Through continuous learning and optimization, the algorithm dynamically adjusts the working state of communication devices to ensure that the system always operates at the optimal performance level, thereby improving the overall operation efficiency and service quality.

[0053] S5. Predictive Maintenance and Fault Prevention: The predictive maintenance module obtains device performance metrics from the deep learning intelligent analysis engine and combines historical data to predict potential faults. It collaborates with the intelligent energy management module to propose specific maintenance suggestions to guide users to take preventive measures to avoid faults.

[0054] For simple problems, the automated repair subsystem will immediately execute the repair program without waiting for manual confirmation; for complex problems, it will generate detailed troubleshooting guides and recommend the best maintenance action path to reduce downtime and maintenance costs.

[0055] S6. Intelligent Energy Management and Environmental Protection and Energy Conservation: The intelligent energy management module focuses on real-time monitoring of the energy consumption of communication devices and uses machine learning algorithms to analyze the energy usage situation. It works in collaboration with the predictive maintenance module to formulate energy-saving solutions, such as automatically switching to the power-saving mode or adjusting the working frequency during low-load periods to achieve the effect of peak shaving and valley filling.

[0056] This module also supports carbon emission tracking and reporting functions to help enterprises meet environmental protection regulations requirements while ensuring that the service quality is not affected. Through the feedback mechanism, it provides energy consumption optimization suggestions to users and the adaptive scheduling algorithm to promote the development of green communication.

[0057] S7. Self-Diagnosis and Automated Repair: The self-diagnosis tool utilizes unsupervised learning algorithms to automatically identify and isolate problem areas without affecting the service, and deeply investigates the source of the problem through methods such as fault tree analysis and root cause analysis. For simple problems, the automated repair subsystem immediately executes the repair program without waiting for manual confirmation; while for complex problems, it generates detailed troubleshooting guides and recommends the best maintenance action paths, reducing downtime and maintenance costs. This feature greatly improves the reliability and availability of the system, reduces the need for manual intervention, and ensures the stable operation of the system.

[0058] Although the present invention has been described above with reference to the embodiments, various improvements can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the disclosed embodiments of the present invention can be combined with each other in any manner, and the exhaustive description of these combinations is omitted in this specification only for the sake of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An intelligent dispatching and management system for multiple communication devices, characterized in that: include: Multimodal data fusion module, used to collect and process heterogeneous data from multiple data sources and perform data preprocessing operations; Deep learning intelligent analysis engine, which is used to receive the data pre-processed by the multimodal data fusion module for pattern recognition and anomaly detection, and output the analysis results; AR-assisted real-time monitoring interface, used to visualize the analysis results of the deep learning intelligent analysis engine in three dimensions and receive user interaction instructions; An adaptive scheduling algorithm based on behavior prediction dynamically adjusts the working status of communication equipment according to the analysis results from the deep learning intelligent analysis engine and user instructions from the AR-assisted real-time monitoring interface; A predictive maintenance module receives the equipment performance data output by the deep learning intelligent analysis engine, performs fault prediction and generates maintenance suggestions in combination with historical data, and sends the maintenance suggestions to the intelligent energy management module and the user; The intelligent energy management module is connected with the multimodal data fusion module, the deep learning intelligent analysis engine and the predictive maintenance module to monitor energy consumption in real time and execute energy efficiency optimization strategies.

2. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The multimodal data fusion module includes: Instant adaption interface, supporting multiple communication protocols, for data collection compatible with different communication devices; Automatic error recovery mechanism, used to perform recovery operations and generate recovery status reports when data transmission is abnormal; The data preprocessing unit performs operations including data cleaning, format conversion, and outlier marking.

3. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The deep learning intelligent analysis engine includes: Extensible learning framework, supporting the import and integration of knowledge graphs; Real-time update mechanism, which updates model parameters online without affecting system operation, thus ensuring rapid response to emerging communication patterns and abnormal situations; Security module, encrypts and protects training data and analysis results.

4. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The AR-assisted real-time monitoring interface includes: Virtual reality integrated unit, providing immersive equipment status inspection and remote operation environment; Multi-user collaboration interface, allowing multiple users to view scenes simultaneously and jointly develop scheduling strategies; Natural user interaction unit that supports gesture recognition and voice command input.

5. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The adaptive scheduling algorithm achieves dynamic adjustment in the following ways: based on the historical behavior patterns and current network status provided by the deep learning intelligent analysis engine, the resource allocation strategy is continuously optimized using the reinforcement learning mechanism, and through continuous learning and optimization, the working status of the communication equipment is dynamically adjusted according to the real-time changes, ensuring that the system is always at the optimal performance level.

6. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: It also includes a self-diagnosis tool that uses an unsupervised learning algorithm to automatically identify and isolate problem areas without affecting service, and collaborates with the predictive maintenance module and the intelligent energy management module to deeply investigate the source of the problem through fault tree analysis and root cause analysis methods, generate detailed troubleshooting guides and automated repair scripts, and reduce the need for manual intervention.

7. The intelligent dispatching and management system for multiple communication devices according to claim 6, characterized in that: The self-diagnosis tool includes an automated repair subsystem. When a problem detected by the automated repair subsystem can be automatically solved, the automated repair subsystem immediately executes the repair procedure without waiting for manual confirmation. For complex problems, a detailed troubleshooting guide is generated and the optimal maintenance action path is recommended to reduce downtime and maintenance costs.

8. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The intelligent energy management module comprises: The energy consumption dynamic adjustment unit automatically analyzes the energy usage of the equipment based on real-time energy consumption monitoring and machine learning algorithms, and coordinates with the predictive maintenance module to develop energy-saving plans; Carbon emission tracking unit, which tracks carbon emissions, generates carbon emission reports and pushes emission reduction strategies; The feedback optimization unit provides the scheduling scheme under energy consumption constraints to the adaptive scheduling algorithm.

9. The intelligent dispatching and management system for multiple communication devices according to claim 1, characterized in that: The communication equipment includes base station equipment, optical fiber transmission equipment and Internet of Things terminal equipment supporting 5G / 6G, and all devices are connected to the multimodal data fusion module through a unified interface protocol.

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