5G portable WiFi fault diagnosis and recovery method and system

Abstract

The invention provides a 5G portable WiFi fault diagnosis and recovery method and system. The method comprises the following steps: collecting network connection data, system information and operation logs from a communication terminal connected to 5G portable WiFi equipment; analyzing the data, and generating a fault diagnosis report; selecting and executing corresponding recovery operation from a recovery scheme library according to the report; monitoring equipment state and network connection in real time, and judging whether recovery is successful or not; if the fault is not solved, an advanced solution is provided, and equipment information, program attributes, scheme library information, network connection data, system information and operation logs are sent to an edge server; the edge server obtains fault data and diagnosis reports of a plurality of similar devices as references, potential fault laws are mined through big data analysis, and reference analysis data are generated; and optimizing an intelligent diagnosis program and a recovery scheme library according to the reference analysis data and other information. The fault type can be accurately identified, the fault recovery time is remarkably shortened, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of 5G technology, and in particular to a 5G portable WiFi fault diagnosis and recovery method and system. Background Art

[0002] 5G smart portable WiFi devices have gradually become popular in recent years, providing users with a more convenient and high-speed network experience. 5G portable WiFi devices can provide faster data transmission speeds than traditional 4G devices, with a theoretical downlink rate of up to 10Gbps, and in actual use, it is generally several hundred Mbps or even higher, easily supporting high-definition video playback, 4K live broadcast and large file downloads; these devices are usually lightweight and easy to carry, without the need for installation and wiring, and provide WiFi signals anytime and anywhere; many 5G portable WiFi devices support large-volume packages, with monthly traffic of up to 1500G or more, meeting the user's long-term, large-volume usage needs; most devices can connect to multiple devices at the same time, usually around 32, to meet the needs of multiple people or multiple devices to access the Internet at the same time. However, due to structural limitations and changing usage environments, 5G portable WiFi devices are prone to failure; and the existing 5G portable WiFi device fault diagnosis methods are not accurate, efficient, or intelligent. Summary of the invention

[0003] Based on the above problems, the present invention proposes a 5G portable WiFi fault diagnosis and recovery method and system. Through the scheme of the present invention, the fault type can be identified more accurately, misjudgment can be reduced, and the reliability of diagnosis can be improved; the fault recovery time can be significantly shortened, and the user experience can be improved; the effect of the recovery operation can be judged in time, and the intelligence level of the system can be improved.

[0004] In view of this, one aspect of the present invention proposes a 5G portable WiFi fault diagnosis and recovery method, including: Collect network connection data from a communication terminal connected to the 5G portable WiFi device, as well as system information and operation logs of the communication terminal; Analyze the network connection data, the system information, and the operation log using a preset intelligent diagnostic program of the 5G portable WiFi device to obtain a first analysis result; Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; The edge server obtains fault data and diagnostic reports of multiple similar portable WiFi devices as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

[0005] Optionally, the step of analyzing the network connection data, the system information and the operation log by using a preset intelligent diagnostic program of the 5G portable WiFi device to obtain a first analysis result includes: The intelligent diagnosis program pre-processes the network connection data, specifically including: standardizing the signal strength data and converting the original dBm value into a percentage; performing time series analysis on the connection speed data and calculating the speed fluctuation rate and stability index; verifying the validity of the IP address and DNS configuration and checking whether there is a conflict or misconfiguration; calculating the time distribution characteristics of the packet loss rate and generating a packet loss pattern feature vector; calculating the mean, variance and peak value of the network delay and constructing a delay feature index; Extract features from the system information, specifically including: analyzing hardware resource status such as CPU usage, memory usage, storage space, etc.; detecting the compatibility of driver versions and firmware versions; extracting physical status parameters such as device temperature and power supply voltage; obtaining network protocol stack configuration parameters and operating status; Performing semantic analysis on the operation log, specifically including: extracting key event information using natural language processing technology; establishing an event time sequence association diagram to identify the fault trigger chain; counting the frequency and distribution characteristics of error codes; extracting the severity level of system warnings and error information; The data obtained after the above processing is input into the pre-trained multimodal fault recognition model, which specifically includes: using convolutional neural network to process time series feature data; using recurrent neural network to analyze event sequence data; using attention mechanism to fuse multi-source feature information; outputting the probability distribution of fault type; Decision reasoning is performed based on the model output results, specifically including: determining the main fault type according to the fault probability threshold; analyzing the correlation and causal relationship between fault types; evaluating the severity and urgency of the fault; and generating a first analysis result containing detailed fault information.

[0006] Optionally, the step of generating a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes a fault type, a fault severity, and a possible fault cause, comprises: Parsing the fault feature data in the first analysis result specifically includes: extracting the fault type probability distribution output by the fault identification model; obtaining the abnormality degree score of each performance indicator; reading the time series event chain related to the fault; and collecting system status and resource usage data; Determine the priority of fault types, including: sorting multiple possible fault types according to the fault probability value; analyzing the cause-effect relationship between fault types and building a fault propagation tree; identifying root faults and derivative faults; marking critical faults that need to be handled first; Evaluate the severity of the fault, including: calculating the impact of the fault on network performance; evaluating the negative impact of the fault on user experience; analyzing the duration and development trend of the fault; generating a fault severity level based on preset scoring rules; Analyze possible causes of failures, including: matching similar cases based on the historical failure database; deriving possible fault triggering conditions using the expert rule system; analyzing the correlation between environmental factors and failures; and evaluating the confidence of various possible causes; Generate a structured diagnostic report, including: organizing fault overview information, including device identification and timestamp; describing the specific manifestation and impact of the fault; listing possible fault causes and their probability weights; adding relevant diagnostic data and performance indicator charts; generating report views for different user roles.

[0007] Optionally, the step of selecting and executing a corresponding recovery operation from a pre-stored recovery solution library according to the fault diagnosis report includes: Performing feature mapping on the fault diagnosis report, specifically including: extracting a fault type identifier and a severity level; Analyze the priority of fault causes; obtain relevant performance indicator abnormal data; read the operating status parameters of the current equipment; Matching solutions in the pre-stored recovery solution library includes: building query conditions based on fault characteristics; retrieving historical cases with similar fault characteristics; evaluating the applicability scores of each candidate recovery solution; and sorting the recovery solutions according to the scores; Formulate recovery operation execution strategies, including: classifying candidate recovery plans into different execution levels; setting the execution order and triggering conditions of plans at each level; configuring the timeout and retry rules for plan execution; defining the rollback mechanism in case of execution failure; Execute the selected recovery operation, including: perform security checks on the execution environment; back up the current system configuration and key data; execute recovery instructions in sequence according to the preset strategy; record state changes during the execution process; Evaluate the effectiveness of the recovery operation, including: monitoring the trend of key performance indicators; verifying whether the fault symptoms have been alleviated; checking whether new abnormal conditions have occurred; and generating a recovery operation execution report.

[0008] Optionally, during the recovery operation, the step of monitoring the current device status and the current network connection status of the 5G portable WiFi device in real time, and judging whether the recovery operation successfully solves the fault according to the current device status and the current network connection status includes: Establish a real-time monitoring indicator system, including: setting equipment status monitoring indicators, including hardware parameters such as CPU usage, memory usage, temperature, and voltage; determining network connection monitoring indicators, including network parameters such as signal strength, transmission rate, packet loss rate, and delay; configuring the sampling period and threshold range of each indicator; and establishing association rules between indicators; Collect current device status data, including: reading real-time device parameters through hardware interfaces; obtaining system resource usage; detecting driver and service operation status; recording system logs and alarm information; and storing collected data in a temporary cache queue; Obtain current network connection data, including: measuring the current network signal quality; performing network speed tests; detecting the status of the network protocol stack; verifying the stability of data transmission; and recording network connection events; Conduct data analysis and status assessment, including: real-time filtering and preprocessing of the collected current device status and current network connection status; calculating trend changes of various indicators; detecting abnormal fluctuations and over-limit situations; and assessing the health score of the overall operating status; Determine the effectiveness of the recovery operation, specifically including: comparing the state changes before and after the fault occurs; verifying whether the fault symptoms are eliminated; evaluating whether the system stability is improved; checking whether new anomalies appear; and generating an evaluation conclusion of the recovery effect.

[0009] Optionally, if the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server, including: Generate advanced solutions, including: analyzing the reasons why the initial recovery operation failed; matching higher-level solutions based on the persistent characteristics of the failure; generating user guidance processes that include hardware inspection steps; and developing alternative solutions for network repair; Arranging the device information of the 5G portable WiFi device, specifically including: collecting the hardware configuration information of the 5G portable WiFi device; extracting the firmware version and driver information; obtaining the unique identification code of the device; recording the usage time and operating environment parameters of the device; and packaging the warranty status information of the device; Summarize the data related to program attribute information and solution library information, including: extracting the configuration parameters of the intelligent diagnosis program; collecting the execution records of the diagnosis algorithm; sorting out the version information of the recovery solution library; statistically analyzing the frequency and success rate of solution use; and recording the detailed process of solution execution; Processing the network connection data, the system information and the operation log, specifically including: compressing the network connection data; extracting key system event logs; screening abnormal status records; collating performance monitoring data; and summarizing fault-related alarm information; The data that has gone through the above processing is encrypted and transmitted, including: desensitizing sensitive data; using encryption algorithms to protect data security; establishing a secure connection with the edge server; transmitting large amounts of data in blocks; and verifying the integrity of data transmission.

[0010] Optionally, the step of the edge server acquiring the fault data and diagnostic reports of multiple similar portable WiFi devices as reference data includes: Determine the matching range of similar devices to obtain the similar portable WiFi devices, specifically including: establishing a product family classification system based on the device model of the 5G portable WiFi device; identifying the similarity of hardware architecture; comparing the compatibility of software versions; evaluating the similarity of functional characteristics; and establishing a device grouping index; Construct a distributed data collection network, including: deploying data collection agents on edge servers; establishing a device node registration mechanism; configuring data collection priority policies; setting collection frequency and data volume thresholds; and implementing adaptive load balancing; Acquiring the fault data of the same type of portable WiFi device, specifically including: receiving the fault information actively reported by the same type of portable WiFi device; regularly synchronizing the operating status data of the same type of portable WiFi device; acquiring the on-site data when the fault of the same type of portable WiFi device occurs; collecting the process record of fault handling; and saving the result data of fault resolution; Collecting diagnostic reports of similar portable WiFi devices, specifically including: obtaining analysis results of corresponding intelligent diagnostic programs on similar portable WiFi devices; collecting expert opinions of manual diagnosis; recording the determination process of the cause of the fault; saving the execution record of the repair plan; and statistically evaluating the effect of fault handling; Data preprocessing is performed on the fault data and the diagnostic report to obtain reference data, specifically including: format conversion of original data; cleaning of abnormal and redundant data; supplementation of missing data items; standardization of data representation; and establishment of data association indexes.

[0011] Optionally, the step of analyzing the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data includes: Classifying and preprocessing the reference data, specifically including: establishing data subsets according to fault types; extracting time series feature data; constructing device behavior feature vectors; labeling key attribute labels of the data; and processing missing values ​​and outliers in the data; Perform multi-dimensional correlation analysis, including: calculating the time correlation of fault occurrence; analyzing the correlation between faults and environmental factors; evaluating the propagation relationship between faults; identifying the combination of triggering conditions for faults; and building a fault correlation network model; Apply machine learning algorithms for pattern recognition, including: using clustering algorithms to discover fault patterns; using classification algorithms to predict fault types; predicting fault trends through regression analysis; using anomaly detection to identify new faults; and establishing fault prediction models; Conduct time series feature mining, including: analyzing the periodicity of faults; identifying the seasonal characteristics of faults; extracting the evolutionary pattern of fault development; predicting potential fault inflection points; and evaluating the persistence characteristics of faults; Generate reference analysis data, including: summarizing the statistical characteristics of fault patterns; outputting the probability model of fault prediction; generating a decision tree for fault diagnosis; forming the best practices for fault handling; and building a fault knowledge graph.

[0012] Optionally, the step of optimizing the intelligent diagnosis program and the recovery solution library according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log includes: Evaluate the performance indicators of existing systems, including: statistically analyzing the accuracy and recall rate of diagnostic procedures; calculating the success rate and efficiency of recovery plans; analyzing system response time and resource usage; evaluating user feedback and satisfaction data; and generating performance evaluation reports; Optimize the intelligent diagnosis program, including: update the fault feature model based on reference analysis data; adjust the weight parameters of fault identification; add newly discovered fault modes; optimize the decision logic of the diagnosis process; update the fault rule knowledge base; Upgrade the recovery solution library, including: screening efficient and reliable recovery solutions; removing inefficient or outdated solutions; adding new solutions; optimizing the execution order of solutions; and updating the applicable conditions of solutions; Perform adaptive optimization, including: adjusting diagnostic parameters according to device characteristics; optimizing execution strategies based on network environment; adjusting resource allocation according to system load; customizing solutions based on user scenarios; and achieving dynamic performance tuning; Verify the optimization effect, including: conduct offline test verification; perform small-scale field tests; collect optimized performance data; compare the effects before and after optimization; and generate an optimization effect report.

[0013] Another aspect of the present invention provides a 5G portable WiFi fault diagnosis and recovery system for executing a 5G portable WiFi fault diagnosis and recovery method, including: a 5G portable WiFi device, a communication terminal connected to the 5G portable WiFi device, and an edge server; The 5G portable WiFi device is configured as: Collecting network connection data and system information and operation logs of the communication terminal from the communication terminal; Analyzing the network connection data, the system information and the operation log using a preset intelligent diagnostic program to obtain a first analysis result; Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; The edge server is configured as: Obtain the fault data and diagnostic reports of multiple similar portable WiFi devices as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

[0014] By adopting the technical solution of the present invention, the network connection data, system information and operation log are analyzed by artificial intelligence algorithms, so that the fault type can be identified more accurately; this analysis method based on historical data and fault modes can reduce misjudgment and improve the reliability of diagnosis; the system can automatically select and execute recovery operations according to the fault diagnosis report, such as restarting the device or updating the firmware. This automated process significantly shortens the fault recovery time and improves the user experience; during the recovery operation, the device status and network connection status are monitored in real time, and the effect of the recovery operation can be judged in time. This dynamic feedback mechanism ensures the timeliness and effectiveness of fault handling; when the initial recovery operation fails to solve the fault, the system can provide advanced solutions, such as guiding the user to perform hardware inspection or contact after-sales service. This multi-level solution can more comprehensively deal with various fault situations; by collecting and analyzing the fault data of multiple similar devices, the system can explore potential fault patterns and trends, thereby optimizing the intelligent diagnosis program and recovery solution library; this data-driven optimization process can continuously improve the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a 5G portable WiFi fault diagnosis and recovery method provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of a 5G portable WiFi fault diagnosis and recovery system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Refer to the following Figure 1 to Figure 2 To describe a 5G portable WiFi fault diagnosis and recovery method and system provided according to some embodiments of the present invention.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a 5G portable WiFi fault diagnosis and recovery method, including: Collect network connection data (including signal strength, connection speed, IP address, DNS configuration, packet loss rate, latency data) from communication terminals connected to the 5G portable WiFi device, as well as system information and operation logs of the communication terminals; It can be understood that in this step, it is ensured that the communication terminal (such as a smartphone, tablet or laptop) is successfully connected to the 5G portable WiFi device; once the connection is successful, the communication terminal sends a request to the 5G portable WiFi device through an API or a network protocol (such as HTTP or MQTT) to collect the following network connection data: signal strength (obtaining the current signal strength through the wireless interface of the device), connection speed (measuring the current upload and download speeds), IP address (obtaining the IP address assigned to the communication terminal), DNS configuration (collecting the currently used DNS server address), packet loss rate (calculating the packet loss rate through the statistics of sending and receiving data packets), delay data (measuring the time from sending to receiving the data packet); the communication terminal accesses its system information (such as operating system version, device model, etc.) and operation log (such as network connection history, error log, etc.) through the system API; this information is packaged together with the network connection data to form a complete data set; the collected network connection data, system information and operation log are sent to the intelligent diagnosis program of the 5G portable WiFi device through a secure communication protocol (such as HTTPS) for analysis; after receiving the data, the 5G portable WiFi device stores it in a local database and prepares for subsequent intelligent analysis. Through this step, various data related to the network connection can be comprehensively collected to ensure that the basic information for fault diagnosis is sufficient; the collected data such as signal strength and connection speed provides an important basis for subsequent fault analysis and can help identify the root cause of network problems; through continuous data collection and transmission, real-time monitoring of network status can be achieved, and potential faults can be discovered and responded to in a timely manner; through accurate fault diagnosis and rapid recovery operations, the user experience when using 5G portable WiFi devices can be significantly improved, reducing usage interruptions caused by network problems.

[0022] Analyze the network connection data, the system information, and the operation log using a preset intelligent diagnostic program of the 5G portable WiFi device to obtain a first analysis result; It can be understood that the intelligent diagnostic program is based on an artificial intelligence algorithm and can identify the types of faults that may currently exist based on historical fault data and preset fault patterns. The types of faults include but are not limited to equipment hardware failures, software failures, network signal problems, configuration errors, and external interference.

[0023] Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; It is understood that the recovery operation includes but is not limited to automatically restarting the device, updating firmware, adjusting network configuration parameters, switching network frequency bands, and clearing cached data; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; It is understandable that the advanced solution includes guiding the user to perform hardware inspection, contact after-sales maintenance service, or perform network repair operations.

[0024] The edge server obtains fault data and diagnostic reports of multiple similar portable WiFi devices (the same or similar products as the 5G portable WiFi device) as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

[0025] It will be appreciated that the computing resources of the edge server may be utilized for more in-depth analysis and diagnosis, including high-performance computing capabilities based on quantum computing to accelerate the fault diagnosis process.

[0026] In this embodiment, the type of fault can be more accurately identified by analyzing network connection data, system information and operation logs through artificial intelligence algorithms; this analysis method based on historical data and fault modes can reduce misjudgment and improve the reliability of diagnosis; the system can automatically select and execute recovery operations according to the fault diagnosis report, such as restarting the device or updating the firmware. This automated process significantly shortens the time for fault recovery and improves the user experience; during the recovery operation, the device status and network connection status are monitored in real time, and the effect of the recovery operation can be judged in time. This dynamic feedback mechanism ensures the timeliness and effectiveness of fault handling; when the initial recovery operation fails to solve the fault, the system can provide advanced solutions, such as guiding the user to perform hardware inspections or contact after-sales service. This multi-level solution can more comprehensively deal with various fault situations; by collecting and analyzing the fault data of multiple similar devices, the system can explore potential fault patterns and trends, thereby optimizing the intelligent diagnosis program and recovery solution library. This data-driven optimization process can continuously improve the intelligence level of the system.

[0027] In order to provide more accurate, safer and more personalized services, some possible implementations of the present invention may further include the following steps: Provide users with a remote diagnostic service interface, so that technical support personnel can remotely access the information of faulty equipment and perform remote assistance operations through the terminal, including but not limited to remotely controlling the terminal to execute diagnostic commands, view equipment settings, modify network configurations, etc.; During data collection, transmission and storage, encryption technology is used to encrypt device information, user data and diagnostic results to prevent data leakage; Monitor the system in real time to prevent malicious attacks and intrusions, use blockchain technology to ensure data integrity and immutability, and record all diagnostic and recovery operation logs for traceability and auditing; Displays fault diagnosis results, recovery operation progress and status information to users through intuitive graphical interfaces or voice prompts; Receive user feedback and operation instructions, guide users to perform necessary manual operations (such as plugging and unplugging SIM cards, replacing power adapters, etc.), and feed back user operation results to the corresponding fault diagnosis module and fault recovery module for further analysis and processing.

[0028] In some possible implementations of the present invention, the step of analyzing the network connection data, the system information, and the operation log using the intelligent diagnostic program preset by the 5G portable WiFi device to obtain a first analysis result includes: The intelligent diagnosis program pre-processes the network connection data, specifically including: standardizing the signal strength data and converting the original dBm value into a percentage; performing time series analysis on the connection speed data and calculating the speed fluctuation rate and stability index; verifying the validity of the IP address and DNS configuration and checking whether there is a conflict or misconfiguration; calculating the time distribution characteristics of the packet loss rate and generating a packet loss pattern feature vector; calculating the mean, variance and peak value of the network delay and constructing a delay feature index; Extract features from the system information, specifically including: analyzing hardware resource status such as CPU usage, memory usage, storage space, etc.; detecting the compatibility of driver versions and firmware versions; extracting physical status parameters such as device temperature and power supply voltage; obtaining network protocol stack configuration parameters and operating status; Performing semantic analysis on the operation log, specifically including: extracting key event information using natural language processing technology; establishing an event time sequence association diagram to identify the fault trigger chain; counting the frequency and distribution characteristics of error codes; extracting the severity level of system warnings and error information; The data obtained after the above processing is input into the pre-trained multimodal fault recognition model, which specifically includes: using convolutional neural network to process time series feature data; using recurrent neural network to analyze event sequence data; using attention mechanism to fuse multi-source feature information; outputting the probability distribution of fault type; It is understandable that CNN is good at processing images and time series data. It can effectively capture spatial and temporal patterns in the data by extracting local features through convolutional layers, which is very important for analyzing the state changes of equipment at different time points; RNN is suitable for processing sequence data and can remember previous input information to analyze the dependencies in the time series, which enables RNN to identify the temporal relationship between events and is suitable for time series analysis of fault occurrence; the attention mechanism allows the model to focus on the most relevant information when processing data and can effectively fuse feature information from different sources. This mechanism improves the sensitivity of the multimodal fault recognition model to important features and enhances the accuracy of fault recognition; ultimately, the multimodal fault recognition model will output a probability distribution for each fault type, which means that it not only identifies the fault type, but also provides the probability of each type occurring. This probability output can help decision makers assess the severity of the fault and the order of priority.

[0029] Decision reasoning is performed based on the model output results, specifically including: determining the main fault type according to the fault probability threshold; analyzing the correlation and causal relationship between fault types; evaluating the severity and urgency of the fault; and generating a first analysis result containing detailed fault information.

[0030] The solution of this embodiment improves the accuracy of fault identification through multi-dimensional feature analysis; reduces false alarm and missed alarm rates and improves diagnostic reliability; adopts a pipelined parallel processing mechanism; optimizes computing resource allocation and improves processing efficiency; comprehensively considers issues at the hardware, software and network levels and establishes a correlation analysis mechanism between faults; and the modular design facilitates functional expansion and supports the dynamic addition of new fault types.

[0031] In some possible implementations of the present invention, a method for constructing a multi-modal fault identification model includes: Collect data from different modalities, such as sensor data, log files, network connection information, etc. This data needs to be cleaned and standardized to ensure its quality and consistency; Use different deep learning models to extract features from various types of data: Convolutional Neural Network (CNN): Suitable for processing image and time series feature data and can extract local features; Recurrent Neural Network (RNN): suitable for analyzing time series data and can capture the temporal dependencies between events; Attention mechanism: used to fuse feature information from different sources and enhance the model's ability to focus on important features; The extracted features are input into a multimodal model, usually a deep learning framework that integrates CNN, RNN and attention mechanism, which can handle multiple types of data and perform effective feature fusion; Use the labeled fault data to train the model and adjust the model parameters to improve its recognition accuracy. After training, evaluate the performance of the model through the validation set to ensure its generalization ability on unseen data. After the model training is completed, it can be used for real-time fault identification; based on the probability distribution of fault types output by the model, a fault diagnosis report is generated, providing information such as fault type, severity and possible causes; Based on feedback from real applications and newly collected data, the model is regularly retrained and optimized to improve its accuracy and adaptability.

[0032] The solution of this embodiment can build an efficient multi-modal fault identification model, which can comprehensively analyze multiple data features, thereby improving the accuracy and efficiency of fault identification.

[0033] In some possible implementations of the present invention, the step of generating a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes a fault type, a fault severity, and a possible fault cause, includes: Parsing the fault feature data in the first analysis result specifically includes: extracting the fault type probability distribution output by the fault identification model; obtaining the abnormality degree score of each performance indicator; reading the time series event chain related to the fault; and collecting system status and resource usage data; Determine the priority of fault types, including: sorting multiple possible fault types according to the fault probability value; analyzing the cause-effect relationship between fault types and building a fault propagation tree; identifying root faults and derivative faults; marking critical faults that need to be handled first; Evaluate the severity of the fault, including: calculating the impact of the fault on network performance; evaluating the negative impact of the fault on user experience; analyzing the duration and development trend of the fault; generating a fault severity level based on preset scoring rules; Analyze possible causes of failures, including: matching similar cases based on the historical failure database; deriving possible fault triggering conditions using the expert rule system; analyzing the correlation between environmental factors and failures; and evaluating the confidence of various possible causes; Generate a structured diagnostic report, including: organizing fault overview information, including device identification and timestamp; describing the specific manifestation and impact of the fault; listing possible fault causes and their probability weights; adding relevant diagnostic data and performance indicator charts; generating report views for different user roles.

[0034] The solution of this embodiment adopts a hierarchical structured information organization method, supports multi-dimensional data visualization, and adaptively adjusts the content depth according to the user role; completely records the reasoning process of fault judgment, retains key diagnostic data and basis, and facilitates subsequent fault analysis and optimization; clearly identifies fault priority, intuitively displays the scope of fault impact, and speeds up the formulation of fault solutions; standardized fault description format facilitates the construction of a fault knowledge base and promotes the reuse of fault handling experience.

[0035] In some possible implementations of the present invention, the step of selecting and executing a corresponding recovery operation from a pre-stored recovery solution library according to the fault diagnosis report includes: Performing feature mapping on the fault diagnosis report, specifically including: extracting a fault type identifier and a severity level; Analyze the priority of fault causes; obtain relevant performance indicator abnormal data; read the operating status parameters of the current equipment; Matching solutions in the pre-stored recovery solution library includes: building query conditions based on fault characteristics; retrieving historical cases with similar fault characteristics; evaluating the applicability scores of each candidate recovery solution; and sorting the recovery solutions according to the scores; Formulate recovery operation execution strategies, including: classifying candidate recovery plans into different execution levels; setting the execution order and triggering conditions of plans at each level; configuring the timeout and retry rules for plan execution; defining the rollback mechanism in case of execution failure; Execute the selected recovery operation, including: perform security checks on the execution environment; back up the current system configuration and key data; execute recovery instructions in sequence according to the preset strategy; record state changes during the execution process; Evaluate the effectiveness of the recovery operation, including: monitoring the trend of key performance indicators; verifying whether the fault symptoms have been alleviated; checking whether new abnormal conditions have occurred; and generating a recovery operation execution report.

[0036] The solution of this embodiment is based on the precise matching of multi-dimensional features, takes into account the feedback of historical execution effects, and dynamically adjusts the priority of the solution; conducts risk assessment before execution, sets up a complete rollback mechanism, and protects key data and configurations; adopts a progressive recovery strategy, executes independent recovery operations in parallel, optimizes the execution order and reduces waiting time; adaptively selects the optimal recovery solution, dynamically adjusts the execution strategy, and accumulates optimization experience to improve accuracy.

[0037] In some possible implementations of the present invention, during the recovery operation, the step of monitoring the current device status and the current network connection status of the 5G portable WiFi device in real time, and judging whether the recovery operation successfully solves the fault according to the current device status and the current network connection status includes: Establish a real-time monitoring indicator system, including: setting equipment status monitoring indicators, including hardware parameters such as CPU usage, memory usage, temperature, and voltage; determining network connection monitoring indicators, including network parameters such as signal strength, transmission rate, packet loss rate, and delay; configuring the sampling period and threshold range of each indicator; and establishing association rules between indicators; Collect current device status data, including: reading real-time device parameters through hardware interfaces; obtaining system resource usage; detecting driver and service operation status; recording system logs and alarm information; and storing collected data in a temporary cache queue; Obtain current network connection data, including: measuring the current network signal quality; performing network speed tests; detecting the status of the network protocol stack; verifying the stability of data transmission; and recording network connection events; Conduct data analysis and status assessment, including: real-time filtering and preprocessing of the collected current device status and current network connection status; calculating trend changes of various indicators; detecting abnormal fluctuations and over-limit situations; and assessing the health score of the overall operating status; Determine the effectiveness of the recovery operation, specifically including: comparing the state changes before and after the fault occurs; verifying whether the fault symptoms are eliminated; evaluating whether the system stability is improved; checking whether new anomalies appear; and generating an evaluation conclusion of the recovery effect.

[0038] The solution of this embodiment uses a multi-dimensional indicator monitoring system, real-time data collection and analysis, and an accurate status assessment mechanism; it can promptly detect abnormal conditions, dynamically adjust recovery strategies, and prevent secondary failures; it can continuously monitor system status, promptly warn of potential risks, and ensure network service quality; it has automated effect evaluation, objective success judgment criteria, and complete process record archiving.

[0039] In some possible implementations of the present invention, if the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server, including: Generate advanced solutions, including: analyzing the reasons why the initial recovery operation failed; matching higher-level solutions based on the persistent characteristics of the failure; generating user guidance processes that include hardware inspection steps; and developing alternative solutions for network repair; Arranging the device information of the 5G portable WiFi device, specifically including: collecting the hardware configuration information of the 5G portable WiFi device; extracting the firmware version and driver information; obtaining the unique identification code of the device; recording the usage time and operating environment parameters of the device; and packaging the warranty status information of the device; Summarize the data related to program attribute information and solution library information, including: extracting the configuration parameters of the intelligent diagnosis program; collecting the execution records of the diagnosis algorithm; sorting out the version information of the recovery solution library; statistically analyzing the frequency and success rate of solution use; and recording the detailed process of solution execution; Processing the network connection data, the system information and the operation log, specifically including: compressing the network connection data; extracting key system event logs; screening abnormal status records; collating performance monitoring data; and summarizing fault-related alarm information; The data that has gone through the above processing is encrypted and transmitted, including: desensitizing sensitive data; using encryption algorithms to protect data security; establishing a secure connection with the edge server; transmitting large amounts of data in blocks; and verifying the integrity of data transmission.

[0040] The solution of this embodiment provides a multi-level solution to ensure that faults are effectively handled; enhances the standardization of data management through a complete data collection mechanism, a standardized data organization format, and a reliable data transmission mechanism; and ensures the security of data transmission through strict data encryption measures, a complete privacy protection mechanism, and a reliable transmission verification mechanism.

[0041] In some possible implementations of the present invention, the step of the edge server obtaining fault data and diagnostic reports of multiple similar portable WiFi devices as reference data includes: Determine the matching range of similar devices to obtain the similar portable WiFi devices, specifically including: establishing a product family classification system based on the device model of the 5G portable WiFi device; identifying the similarity of hardware architecture; comparing the compatibility of software versions; evaluating the similarity of functional characteristics; and establishing a device grouping index; Construct a distributed data collection network, including: deploying data collection agents on edge servers; establishing a device node registration mechanism; configuring data collection priority policies; setting collection frequency and data volume thresholds; and implementing adaptive load balancing; Acquiring the fault data of the same type of portable WiFi device, specifically including: receiving the fault information actively reported by the same type of portable WiFi device; regularly synchronizing the operating status data of the same type of portable WiFi device; acquiring the on-site data when the fault of the same type of portable WiFi device occurs; collecting the process record of fault handling; and saving the result data of fault resolution; Collecting diagnostic reports of similar portable WiFi devices, specifically including: obtaining analysis results of corresponding intelligent diagnostic programs on similar portable WiFi devices; collecting expert opinions of manual diagnosis; recording the determination process of the cause of the fault; saving the execution record of the repair plan; and statistically evaluating the effect of fault handling; Data preprocessing is performed on the fault data and the diagnostic report to obtain reference data, specifically including: format conversion of original data; cleaning of abnormal and redundant data; supplementation of missing data items; standardization of data representation; and establishment of data association indexes.

[0042] The solution of this embodiment can improve the comprehensiveness of data collection, enhance the reliability of data quality, improve the efficiency of data management, and optimize the utilization of system resources.

[0043] In some possible implementations of the present invention, the step of analyzing the reference data by big data analysis technology, mining potential fault patterns and trends, and obtaining reference analysis data includes: Classifying and preprocessing the reference data, specifically including: establishing data subsets according to fault types; extracting time series feature data; constructing device behavior feature vectors; labeling key attribute labels of the data; and processing missing values ​​and outliers in the data; Perform multi-dimensional correlation analysis, including: calculating the time correlation of fault occurrence; analyzing the correlation between faults and environmental factors; evaluating the propagation relationship between faults; identifying the combination of triggering conditions for faults; and building a fault correlation network model; Apply machine learning algorithms for pattern recognition, including: using clustering algorithms to discover fault patterns; using classification algorithms to predict fault types; predicting fault trends through regression analysis; using anomaly detection to identify new faults; and establishing fault prediction models; Conduct time series feature mining, including: analyzing the periodicity of faults; identifying the seasonal characteristics of faults; extracting the evolutionary pattern of fault development; predicting potential fault inflection points; and evaluating the persistence characteristics of faults; Generate reference analysis data, including: summarizing the statistical characteristics of fault patterns; outputting the probability model of fault prediction; generating a decision tree for fault diagnosis; forming the best practices for fault handling; and building a fault knowledge graph.

[0044] The solution of this embodiment can improve the accuracy of fault prediction based on the analysis of massive historical data, multi-dimensional feature extraction and fusion, and dynamically updated prediction models; it can enhance the intelligence of fault diagnosis by automatically identifying fault modes, accurately locating fault causes, and intelligently recommending solutions; it can improve the efficiency of fault handling by quickly matching historical cases, optimizing processing procedures, and reducing fault handling time.

[0045] In some possible implementations of the present invention, the step of optimizing the intelligent diagnosis program and the recovery solution library according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log includes: Evaluate the performance indicators of existing systems, including: statistically analyzing the accuracy and recall rate of diagnostic procedures; calculating the success rate and efficiency of recovery plans; analyzing system response time and resource usage; evaluating user feedback and satisfaction data; and generating performance evaluation reports; Optimize the intelligent diagnosis program, including: update the fault feature model based on reference analysis data; adjust the weight parameters of fault identification; add newly discovered fault modes; optimize the decision logic of the diagnosis process; update the fault rule knowledge base; Upgrade the recovery solution library, including: screening efficient and reliable recovery solutions; removing inefficient or outdated solutions; adding new solutions; optimizing the execution order of solutions; and updating the applicable conditions of solutions; Perform adaptive optimization, including: adjusting diagnostic parameters according to device characteristics; optimizing execution strategies based on network environment; adjusting resource allocation according to system load; customizing solutions based on user scenarios; and achieving dynamic performance tuning; Verify the optimization effect, including: conduct offline test verification; perform small-scale field tests; collect optimized performance data; compare the effects before and after optimization; and generate an optimization effect report.

[0046] The solution of this embodiment can improve diagnostic accuracy, increase repair success rate, and reduce processing response time; it can support new fault types, adapt to different usage scenarios, and be compatible with multiple equipment models; it can improve fault solving efficiency, reduce the need for manual intervention, and improve service satisfaction; and it can achieve continuous improvement through automated optimization mechanisms, dynamic performance tuning, and continuous knowledge accumulation.

[0047] See also Figure 2 , Another embodiment of the present invention provides a 5G portable WiFi fault diagnosis and recovery system for executing a 5G portable WiFi fault diagnosis and recovery method, including: a 5G portable WiFi device, a communication terminal connected to the 5G portable WiFi device, and an edge server; The 5G portable WiFi device is configured as: Collecting network connection data and system information and operation logs of the communication terminal from the communication terminal; Analyzing the network connection data, the system information and the operation log using a preset intelligent diagnostic program to obtain a first analysis result; Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; The edge server is configured as: Obtain the fault data and diagnostic reports of multiple similar portable WiFi devices as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

[0048] It should be known that Figure 2 The block diagram of the 5G portable WiFi fault diagnosis and recovery system shown is for illustration only, and the number of modules shown does not limit the protection scope of the present invention. The 5G portable WiFi fault diagnosis and recovery system provided in this embodiment can be used to execute the corresponding embodiments of the 5G portable WiFi fault diagnosis and recovery method. For the specific implementation process, please refer to the description of the embodiments of each method, which will not be repeated here.

[0049] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0050] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0051] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0052] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0054] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0055] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0056] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0057] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A 5G portable WiFi fault diagnosis and recovery method, characterized in that: include: Collect network connection data from a communication terminal connected to the 5G portable WiFi device, as well as system information and operation logs of the communication terminal; Analyze the network connection data, the system information, and the operation log using a preset intelligent diagnostic program of the 5G portable WiFi device to obtain a first analysis result; Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; The edge server obtains fault data and diagnostic reports of multiple similar portable WiFi devices as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

2. The 5G portable WiFi fault diagnosis and recovery method according to claim 1 is characterized in that: The step of analyzing the network connection data, the system information and the operation log using the intelligent diagnostic program preset by the 5G portable WiFi device to obtain a first analysis result includes: The intelligent diagnosis program pre-processes the network connection data, specifically including: standardizing the signal strength data and converting the original dBm value into a percentage; performing time series analysis on the connection speed data and calculating the speed fluctuation rate and stability index; verifying the validity of the IP address and DNS configuration and checking whether there is a conflict or misconfiguration; calculating the time distribution characteristics of the packet loss rate and generating a packet loss pattern feature vector; calculating the mean, variance and peak value of the network delay and constructing a delay feature index; Extract features from the system information, specifically including: analyzing hardware resource status such as CPU usage, memory usage, storage space, etc.; detecting the compatibility of driver versions and firmware versions; extracting physical status parameters such as device temperature and power supply voltage; obtaining network protocol stack configuration parameters and operating status; Performing semantic analysis on the operation log, specifically including: extracting key event information using natural language processing technology; establishing an event time sequence association diagram to identify the fault trigger chain; counting the frequency and distribution characteristics of error codes; extracting the severity level of system warnings and error information; The data obtained after the above processing is input into the pre-trained multimodal fault recognition model, which specifically includes: using convolutional neural network to process time series feature data; using recurrent neural network to analyze event sequence data; using attention mechanism to fuse multi-source feature information; outputting the probability distribution of fault type; Decision reasoning is performed based on the model output results, specifically including: determining the main fault type according to the fault probability threshold; analyzing the correlation and causal relationship between fault types; evaluating the severity and urgency of the fault; and generating a first analysis result containing detailed fault information.

3. The 5G portable WiFi fault diagnosis and recovery method according to claim 2 is characterized in that: The step of generating a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause, comprises: Parsing the fault feature data in the first analysis result specifically includes: extracting the fault type probability distribution output by the fault identification model; obtaining the abnormality degree score of each performance indicator; reading the time series event chain related to the fault; and collecting system status and resource usage data; Determine the priority of fault types, including: sorting multiple possible fault types according to the fault probability value; analyzing the cause-effect relationship between fault types and building a fault propagation tree; identifying root faults and derivative faults; marking critical faults that need to be handled first; Evaluate the severity of the fault, including: calculating the impact of the fault on network performance; evaluating the negative impact of the fault on user experience; analyzing the duration and development trend of the fault; generating a fault severity level based on preset scoring rules; Analyze possible causes of failures, including: matching similar cases based on the historical failure database; deriving possible fault triggering conditions using the expert rule system; analyzing the correlation between environmental factors and failures; and evaluating the confidence of various possible causes; Generate a structured diagnostic report, including: organizing fault overview information, including device identification and timestamp; describing the specific manifestation and impact of the fault; listing possible fault causes and their probability weights; adding relevant diagnostic data and performance indicator charts; generating report views for different user roles.

4. The 5G portable WiFi fault diagnosis and recovery method according to claim 3 is characterized in that: The step of selecting and executing a corresponding recovery operation from a pre-stored recovery solution library according to the fault diagnosis report comprises: Performing feature mapping on the fault diagnosis report, specifically including: extracting a fault type identifier and a severity level; Analyze the priority of fault causes; obtain relevant performance indicator abnormal data; read the operating status parameters of the current equipment; Matching solutions in the pre-stored recovery solution library includes: building query conditions based on fault characteristics; retrieving historical cases with similar fault characteristics; evaluating the applicability scores of each candidate recovery solution; and sorting the recovery solutions according to the scores; Formulate recovery operation execution strategies, including: classifying candidate recovery plans into different execution levels; setting the execution order and triggering conditions of plans at each level; configuring the timeout and retry rules for plan execution; defining the rollback mechanism in case of execution failure; Execute the selected recovery operation, including: perform security checks on the execution environment; back up the current system configuration and key data; execute recovery instructions in sequence according to the preset strategy; record state changes during the execution process; Evaluate the effectiveness of the recovery operation, including: monitoring the trend of key performance indicators; verifying whether the fault symptoms have been alleviated; checking whether new abnormal conditions have occurred; and generating a recovery operation execution report.

5. The 5G portable WiFi fault diagnosis and recovery method according to claim 4 is characterized in that: The step of monitoring the current device status and the current network connection status of the 5G portable WiFi device in real time during the recovery operation, and judging whether the recovery operation successfully solves the fault according to the current device status and the current network connection status, includes: Establish a real-time monitoring indicator system, including: setting equipment status monitoring indicators, including hardware parameters such as CPU usage, memory usage, temperature, and voltage; determining network connection monitoring indicators, including network parameters such as signal strength, transmission rate, packet loss rate, and delay; configuring the sampling period and threshold range of each indicator; and establishing association rules between indicators; Collect current device status data, including: reading real-time device parameters through hardware interfaces; obtaining system resource usage; detecting driver and service operation status; recording system logs and alarm information; and storing collected data in a temporary cache queue; Obtain current network connection data, including: measuring the current network signal quality; performing network speed tests; detecting the status of the network protocol stack; verifying the stability of data transmission; and recording network connection events; Conduct data analysis and status assessment, including: real-time filtering and preprocessing of the collected current device status and current network connection status; calculating trend changes of various indicators; detecting abnormal fluctuations and over-limit situations; and assessing the health score of the overall operating status; Determine the effectiveness of the recovery operation, specifically including: comparing the state changes before and after the fault occurs; verifying whether the fault symptoms are eliminated; evaluating whether the system stability is improved; checking whether new anomalies appear; and generating an evaluation conclusion of the recovery effect.

6. The 5G portable WiFi fault diagnosis and recovery method according to claim 5 is characterized in that: If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server, including: Generate advanced solutions, including: analyzing the reasons why the initial recovery operation failed; matching higher-level solutions based on the persistent characteristics of the failure; generating user guidance processes that include hardware inspection steps; and developing alternative solutions for network repair; Arranging the device information of the 5G portable WiFi device, specifically including: collecting the hardware configuration information of the 5G portable WiFi device; extracting the firmware version and driver information; obtaining the unique identification code of the device; recording the usage time and operating environment parameters of the device; and packaging the warranty status information of the device; Summarize the data related to program attribute information and solution library information, including: extracting the configuration parameters of the intelligent diagnosis program; collecting the execution records of the diagnosis algorithm; sorting out the version information of the recovery solution library; statistically analyzing the frequency and success rate of solution use; and recording the detailed process of solution execution; Processing the network connection data, the system information and the operation log, specifically including: compressing the network connection data; extracting key system event logs; screening abnormal status records; collating performance monitoring data; and summarizing fault-related alarm information; The data that has gone through the above processing is encrypted and transmitted, including: desensitizing sensitive data; using encryption algorithms to protect data security; establishing a secure connection with the edge server; transmitting large amounts of data in blocks; and verifying the integrity of data transmission.

7. The 5G portable WiFi fault diagnosis and recovery method according to claim 6 is characterized in that: The step of the edge server obtaining the fault data and diagnostic reports of multiple similar portable WiFi devices as reference data includes: Determine the matching range of similar devices to obtain the similar portable WiFi devices, specifically including: establishing a product family classification system based on the device model of the 5G portable WiFi device; identifying the similarity of hardware architecture; comparing the compatibility of software versions; evaluating the similarity of functional characteristics; and establishing a device grouping index; Construct a distributed data collection network, including: deploying data collection agents on edge servers; establishing a device node registration mechanism; configuring data collection priority policies; setting collection frequency and data volume thresholds; and implementing adaptive load balancing; Acquiring the fault data of the same type of portable WiFi device, specifically including: receiving the fault information actively reported by the same type of portable WiFi device; regularly synchronizing the operating status data of the same type of portable WiFi device; acquiring the on-site data when the fault of the same type of portable WiFi device occurs; collecting the process record of fault handling; and saving the result data of fault resolution; Collecting diagnostic reports of similar portable WiFi devices, specifically including: obtaining analysis results of corresponding intelligent diagnostic programs on similar portable WiFi devices; collecting expert opinions of manual diagnosis; recording the determination process of the cause of the fault; saving the execution record of the repair plan; and statistically evaluating the effect of fault handling; Data preprocessing is performed on the fault data and the diagnostic report to obtain reference data, specifically including: format conversion of original data; cleaning of abnormal and redundant data; supplementation of missing data items; standardization of data representation; and establishment of data association indexes.

8. The 5G portable WiFi fault diagnosis and recovery method according to claim 7 is characterized in that: The step of analyzing the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data includes: Classifying and preprocessing the reference data, specifically including: establishing data subsets according to fault types; extracting time series feature data; constructing device behavior feature vectors; labeling key attribute labels of the data; and processing missing values ​​and outliers in the data; Perform multi-dimensional correlation analysis, including: calculating the time correlation of fault occurrence; analyzing the correlation between faults and environmental factors; evaluating the propagation relationship between faults; identifying the combination of triggering conditions for faults; and building a fault correlation network model; Apply machine learning algorithms for pattern recognition, including: using clustering algorithms to discover fault patterns; using classification algorithms to predict fault types; predicting fault trends through regression analysis; using anomaly detection to identify new faults; and establishing fault prediction models; Conduct time series feature mining, including: analyzing the periodicity of faults; identifying the seasonal characteristics of faults; extracting the evolutionary pattern of fault development; predicting potential fault inflection points; and evaluating the persistence characteristics of faults; Generate reference analysis data, including: summarizing the statistical characteristics of fault patterns; outputting the probability model of fault prediction; generating a decision tree for fault diagnosis; forming the best practices for fault handling; and building a fault knowledge graph.

9. The 5G portable WiFi fault diagnosis and recovery method according to claim 8, characterized in that: The step of optimizing the intelligent diagnosis program and the recovery solution library according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log includes: Evaluate the performance indicators of existing systems, including: statistically analyzing the accuracy and recall rate of diagnostic procedures; calculating the success rate and efficiency of recovery plans; analyzing system response time and resource usage; evaluating user feedback and satisfaction data; and generating performance evaluation reports; Optimize the intelligent diagnosis program, including: update the fault feature model based on reference analysis data; adjust the weight parameters of fault identification; add newly discovered fault modes; optimize the decision logic of the diagnosis process; update the fault rule knowledge base; Upgrade the recovery solution library, including: screening efficient and reliable recovery solutions; removing inefficient or outdated solutions; adding new solutions; optimizing the execution order of solutions; and updating the applicable conditions of solutions; Perform adaptive optimization, including: adjusting diagnostic parameters according to device characteristics; optimizing execution strategies based on network environment; adjusting resource allocation according to system load; customizing solutions based on user scenarios; and achieving dynamic performance tuning; Verify the optimization effect, including: conduct offline test verification; perform small-scale field tests; collect optimized performance data; compare the effects before and after optimization; and generate an optimization effect report.

10. A 5G portable WiFi fault diagnosis and recovery system, used to execute the 5G portable WiFi fault diagnosis and recovery method according to any one of claims 1 to 9, characterized in that: Comprising: a 5G portable WiFi device, a communication terminal connected to the 5G portable WiFi device, and an edge server; The 5G portable WiFi device is configured as: Collecting network connection data and system information and operation logs of the communication terminal from the communication terminal; Analyzing the network connection data, the system information and the operation log using a preset intelligent diagnostic program to obtain a first analysis result; Generate a fault diagnosis report according to the first analysis result, wherein the fault diagnosis report includes the fault type, fault severity and possible fault cause; According to the fault diagnosis report, select and execute corresponding recovery operations from a pre-stored recovery solution library; During the recovery operation, the current device status and the current network connection status of the 5G portable WiFi device are monitored in real time, and it is determined whether the recovery operation successfully solves the fault according to the current device status and the current network connection status; If the recovery operation fails to successfully resolve the fault, an advanced solution is provided, and the device information of the 5G portable WiFi device, the program attribute information of the intelligent diagnosis program, the solution library information of the recovery solution library, the network connection data, the system information and the operation log are sent to the edge server; The edge server is configured as: Obtain the fault data and diagnostic reports of multiple similar portable WiFi devices as reference data; Analyze the reference data by using big data analysis technology to mine potential fault patterns and trends to obtain reference analysis data; The intelligent diagnosis program and the recovery solution library are optimized according to the reference analysis data, the device information, the program attribute information, the solution library information, the network connection data, the system information and the operation log.

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