Terminal fault monitoring and early warning method and device, electronic equipment and program product

Abstract

The invention discloses a terminal fault monitoring and early warning method and device, electronic equipment and a program product, and relates to the technical field of mobile communication. The method comprises the following steps: collecting characteristic parameters of a data link; analyzing the collected characteristic parameters, and calculating a correlation coefficient value between the characteristic parameters and the user experience degree; setting an early warning threshold value based on historical experience data; and when the correlation coefficient value reaches an early warning threshold value, generating a data link abnormity warning event. The method can ensure that fault monitoring data of a data link has comprehensiveness and pertinence, reduces false alarm and missing alarm conditions, realizes early warning and quick response of faults, can also provide data support for subsequent fault tracing, early warning threshold optimization and monitoring model iteration, and improves the reliability of fault monitoring. The accuracy, timeliness and traceability of terminal fault monitoring are effectively improved, the influence of faults on user experience is reduced, and reliable guarantee is provided for stable operation of the terminal.

Description

Technical Field

[0001] This application relates to the field of mobile communication technology, and in particular to a terminal fault monitoring and early warning method, device, electronic device and program product. Background Technology

[0002] With the rise of emerging technologies such as 5G and the Internet of Things (IoT), data-based networks have been widely applied in various fields of mobile communication systems, including internet access, social media, online shopping, mobile payment, cloud computing, and IoT, providing users with a wide variety of services and conveniences. However, as networks continue to upgrade, their complexity increases, and phenomena affecting user experience occur more frequently, such as video buffering, network drops, inability to access the internet, and slow upload and download speeds. How to quickly locate and identify the key factors affecting user experience has become a pain point that the industry urgently needs to solve. However, existing technologies are insufficient to meet the aforementioned needs for ensuring mobile terminal communication quality and improving user experience.

[0003] Therefore, there is an urgent need for a terminal fault monitoring and early warning technology that can ensure the communication quality of mobile terminals and improve user experience based on the characteristic parameters of the data link layer. Summary of the Invention

[0004] This application provides a terminal fault monitoring and early warning method, device, electronic device, and program product.

[0005] Firstly, this application provides a terminal fault monitoring and early warning method, comprising the following steps: Collect characteristic parameters of the data link; The collected feature parameters are analyzed, and the correlation coefficient between the feature parameters and the user experience is calculated. Set early warning thresholds based on historical experience data; When the correlation coefficient value reaches the warning threshold, a data link anomaly warning event is generated.

[0006] In some embodiments, the characteristic parameters of the data link include at least one of the following: the number of uplink packet losses, the number of uplink transmitted data packets, and the uplink and downlink data rates of the Packet Data Convergence Protocol layer; the number of downlink packet losses and the number of uplink retransmissions of the Radio Link Control layer; the uplink bit error rate and the downlink bit error rate of the Media Access Control layer; and the uplink bandwidth, downlink bandwidth, signal quality of the serving cell, and signal strength of the serving cell of the Radio Resource Control layer.

[0007] In some embodiments, after receiving the collected feature parameters, the feature parameters are classified according to the registration classification information corresponding to the feature parameters, and the classified feature parameters are transmitted to the data processing unit of the communication processing component.

[0008] In some embodiments, the classified feature parameters are processed and control commands are generated, including: Analyze the feature parameters; The feature parameters are aggregated into reported information; The reported information is encapsulated into control commands and transmitted to the application processing component.

[0009] In some embodiments, parsing and processing control commands includes: Receive control commands and parse out characteristic parameters; The correlation coefficient between the feature parameters and the user experience was calculated using the Pearson correlation coefficient method.

[0010] In some embodiments, after generating a data link anomaly warning event, at least one of the corresponding feature parameters, correlation coefficient values, and warning threshold values ​​is stored along with the data link anomaly warning event.

[0011] Secondly, this application provides a terminal fault monitoring and early warning device, including a communication processing component, an application processing component, and a storage component; The communication processing component includes: a data acquisition unit configured to acquire feature parameters of the data link L2 and L3 layers; a data aggregation unit configured to classify and transmit feature parameters based on the registration classification information corresponding to the feature parameters; a data processing unit configured to process feature parameters and generate reporting information; and a command transmission unit configured to encapsulate the reporting information into control commands and transmit them to the application processing component. The application processing components include: a command interface unit configured to receive and parse control commands; and an analysis and processing unit configured to calculate the correlation coefficient between feature parameters and user experience, set a warning threshold based on historical experience data, and generate a data link anomaly warning event when the correlation coefficient reaches the warning threshold. The storage component is configured to store at least one of the following: characteristic parameters, correlation coefficient values, early warning threshold values, and data link anomaly warning events.

[0012] In some embodiments, the acquisition unit includes an L2 acquisition subunit and an L3 acquisition subunit; wherein the L2 acquisition subunit is configured to acquire at least one feature parameter from the packet data aggregation protocol layer, the radio link control layer, and the media access control layer; and the L3 acquisition subunit is configured to acquire feature parameters from the radio resource control layer.

[0013] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the terminal fault monitoring and early warning method described above.

[0014] Fourthly, this application provides a computer program product, including a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the terminal fault monitoring and early warning method described in any of the preceding claims.

[0015] The above-mentioned at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: by collecting the feature parameters of the L2 and L3 layers of the data link, the fault monitoring data source is ensured to be both comprehensive and targeted, avoiding the limitations of single parameter monitoring; then, the correlation coefficient value between the feature parameters and the user experience is obtained by the Pearson correlation coefficient method; at the same time, a warning threshold value is set based on historical experience data to reduce false alarms and missed alarms; when the correlation coefficient value reaches the warning threshold value, a data link abnormality warning event is generated to realize early warning and rapid response to faults; and by storing the feature parameters, correlation coefficient values ​​and warning threshold values ​​corresponding to the abnormal events, data support is provided for subsequent fault tracing, threshold optimization and monitoring model iteration, effectively improving the accuracy, timeliness and traceability of terminal fault monitoring, reducing the impact of faults on user experience, and providing reliable protection for the stable operation of the terminal.

[0016] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0017] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 This illustration schematically shows an overall process diagram of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 2 This illustration schematically shows a feature parameter acquisition process of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 3 This illustration schematically shows a feature parameter classification process of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 4 This illustration schematically shows a control command generation process diagram of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 5 This illustration schematically shows an abnormal event generation process of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 6 This illustration schematically shows an abnormal event storage process of a terminal fault monitoring and early warning method according to an embodiment of this application; Figure 7 This illustration schematically shows an overall structural diagram of a terminal fault monitoring and early warning device according to an embodiment of this application; Figure 8 This illustration schematically shows a communication processing component structure of a terminal fault monitoring and early warning device according to an embodiment of this application; Figure 9 This illustration schematically shows a structural diagram of the application processing component of a terminal fault monitoring and early warning device according to an embodiment of this application; Figure 10 This illustration schematically shows a data acquisition unit structure of a terminal fault monitoring and early warning device according to an embodiment of this application; Figure 11 An exemplary block diagram of a computer program product of a terminal fault monitoring and early warning method according to an embodiment of this application is shown schematically.

[0019] In the diagram: 701, Communication Processing Component; 702, Application Processing Component; 703, Storage Component; 704, Display Component; 705, Acquisition Unit; 706, Data Aggregation Unit; 707, Data Processing Unit; 708, Command Transmission Unit; 709, Command Interface Unit; 7010, Analysis and Processing Unit; 7011, L2 Acquisition Subunit; 7012, L3 Acquisition Subunit; 1101, Computer Program. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0021] Figure 1 The illustration shows an overall flowchart of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0022] like Figure 1 As shown, the steps are as follows: S101, Collect characteristic parameters of the data link. The acquisition unit 705 of the communication processing component 701 collects characteristic parameters of the L2 and L3 layers of the data link according to a preset acquisition cycle. The acquisition unit 705 includes an L2 acquisition subunit 7011 and an L3 acquisition subunit 7012. The L2 acquisition subunit 7011 is configured to collect characteristic parameters of each protocol layer in the L2 layer of the data link, specifically including: the number of uplink packet losses, the number of uplink transmitted data packets, and the uplink and downlink data rates of the Packet Data Convergence Protocol (PDCP) layer; the number of downlink packet losses and the number of uplink retransmissions of the Radio Link Control (RANC) layer; and the uplink bit error rate and downlink bit error rate of the Media Access Control (MAC) layer. The L3 acquisition subunit 7012 is configured to collect parameters of the Radio Resource Control (RANC) layer in the L3 layer, specifically including: uplink bandwidth, downlink bandwidth, signal quality of the serving cell, and signal strength of the serving cell.

[0023] After the feature parameters are collected, all feature parameters are transmitted to the data aggregation unit 706 of the communication processing component 701 to provide a data basis for subsequent processing.

[0024] S102, the feature parameters are parsed and their correlation coefficient with user experience is calculated. The communication processing component 701 encapsulates the feature parameters into a control command through the command transmission unit 708 and sends it to the application processing component 702. The application processing component 702 parses the control command and extracts the feature parameters. Subsequently, the analysis and processing unit 7010 of the application processing component 702 uses the Pearson correlation coefficient method to quantify the correlation between each feature parameter and user experience, obtaining the corresponding correlation coefficient value. The larger the correlation coefficient value, the more significant the impact of the feature parameter on user experience.

[0025] S103, setting early warning thresholds based on historical experience data. The analysis and processing unit 7010 of the application processing component 702 sets differentiated early warning thresholds for each feature parameter based on historical experience data stored in the storage component 703. The historical experience data at least includes the statistical basis for setting the early warning thresholds, and the data comes from the recording and storage of historical feature parameters and user experience status. The early warning thresholds can be dynamically adjusted according to the actual application scenario to adapt to the user experience needs of different network environments, effectively reducing false alarms and missed alarms.

[0026] S104, when a correlation coefficient value reaches the warning threshold, a corresponding data link anomaly warning event is generated. The analysis and processing unit 7010 of the application processing component 702 compares the calculated correlation coefficient value with the set corresponding warning threshold value in real time. If the correlation coefficient value of a certain feature parameter reaches or exceeds its warning threshold value, a corresponding data link anomaly warning event is immediately generated, which includes at least the following information: the name and value of the triggered feature parameter, the calculated correlation coefficient value, the warning threshold value, and the timestamp of the event occurrence.

[0027] The generated abnormal warning events will be uploaded to the storage component 703 in real time for persistent storage, providing data support for subsequent fault tracing, threshold optimization and model iteration. At the same time, the event will also be sent to the display component 704 to notify the operation and maintenance personnel in a visual way, such as alarm pop-ups and reports, so as to achieve rapid fault location and response, ensure stable terminal operation and improve user experience.

[0028] Figure 2 The illustration shows a schematic diagram of the feature parameter acquisition process of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0029] like Figure 2 As shown, the steps are as follows: S201, Initialization and Configuration of Acquisition Unit 705. The acquisition unit 705 is initialized, including activating the L2 acquisition subunit 7011 and L3 acquisition subunit 7012, and uniformly configuring a preset acquisition cycle for both L2 and L3 acquisition subunits 7011 and 7012. The preset acquisition cycle is designed to balance the real-time performance of monitoring with performance overhead, avoiding the omission of instantaneous anomalies in characteristic parameters due to excessively long intervals, while also preventing high-frequency acquisition from excessively consuming terminal resources.

[0030] S202, L2 acquisition subunit 7011 acquires characteristic parameters of the data link L2 layer. In each acquisition cycle, L2 acquisition subunit 7011 acquires characteristic parameters from each protocol layer of the data link L2 layer: including uplink packet loss count, uplink transmitted data packet count, uplink and downlink data rates from the packet data aggregation protocol layer; downlink packet loss count and uplink retransmission count from the radio link control layer; and uplink bit error rate and downlink bit error rate from the media access control layer. These characteristic parameters reflect the integrity, reliability, and efficiency of the data link L2 layer during transmission.

[0031] S203, L3 acquisition subunit 7012 acquires characteristic parameters from the L3 layer of the data link. Within the same acquisition period, L3 acquisition subunit 7012 acquires characteristic parameters from the radio resource control layer of the data link L3 layer, mainly including: uplink bandwidth, downlink bandwidth, signal quality of the serving cell, and signal strength of the serving cell. These characteristic parameters complement the characteristic parameters acquired from the L2 layer, jointly reflecting the status of the data link.

[0032] S204, the collected feature parameters are transmitted to the data aggregation unit 706. The acquisition unit 705 transmits the feature parameters collected by the L2 acquisition subunit 7011 and the L3 acquisition subunit 7012 to the data aggregation unit 706 of the communication processing component 701. The data aggregation unit 706 receives and temporarily stores all feature parameters.

[0033] Figure 3 The illustration shows a schematic diagram of the characteristic parameter classification process of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0034] like Figure 3 As shown, the steps are as follows: S301, the data aggregation unit 706 receives feature parameters. The data aggregation unit 706 receives feature parameters collected by the L2 acquisition subunit 7011 and the L3 acquisition subunit 7012, and synchronously reads the registration classification information corresponding to the feature parameters.

[0035] S302, classifying feature parameters based on registration classification information. Data aggregation unit 706, based on registration classification information, automatically identifies and classifies each received feature parameter, transforming the unordered input feature parameters into clearly categorized structured data.

[0036] S303, the classified feature parameters are transmitted to the data processing unit 707. After receiving the classified feature parameters, the data aggregation unit 706 transmits the classified feature parameters to the data processing unit 707 according to the classification.

[0037] Figure 4 The diagram illustrates a control command generation process of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0038] like Figure 4 As shown, the steps are as follows: S401, the data processing unit 707 receives the characteristic parameters transmitted for classification. The data processing unit 707 receives the characteristic parameters transmitted for classification from the data aggregation unit 706. Optionally, these characteristic parameters can be transmitted in a structured form and include classification identification information to facilitate identification and parsing by the data processing unit 707.

[0039] S402, parse the received feature parameters. The data processing unit 707 parses the parameter values ​​of the feature parameters. In some embodiments, this process may further include verifying the feature parameters to check the rationality of the parameter values ​​and marking suspected abnormal feature parameters to provide an accurate and reliable data foundation for subsequent data aggregation.

[0040] S403, Aggregate and generate reporting information. Data processing unit 707 aggregates the parsed feature parameters into reporting information. Optionally, a unique terminal identifier and data processing unit 707 number are added to the reporting information to facilitate traceability of the source of the reporting information.

[0041] S404, encapsulate and generate control commands and transmit them. Data processing unit 707 encapsulates the reported information into control commands. Specifically, it encapsulates the information according to the AT command protocol to generate control commands conforming to the AT command format. Command transmission unit 708 transmits the generated control commands to command interface unit 709 of application processing component 702.

[0042] Figure 5 The illustration shows a schematic diagram of the abnormal event generation process of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0043] like Figure 5 As shown, the steps are as follows: S501, the command interface unit 709 receives a control command. The command interface unit 709 of the application processing component 702 receives a control command from the communication processing component 701, which conforms to the AT protocol format and carries encapsulated reporting information.

[0044] S502, parse the control command and extract characteristic parameters. The command interface unit 709 parses the control command. Optionally, the integrity of the data can be further verified by means of CRC check or other methods. After the verification is successful, the characteristic parameters encapsulated in the reported information are parsed out.

[0045] S503, acquire feature parameters and historical experience data. The analysis and processing unit 7010 receives feature parameters from the command interface unit 709; the analysis and processing unit 7010 accesses the storage component 703 to acquire historical experience data. The historical experience data may include records of the correspondence between feature parameter values ​​and the degree of user experience anomalies under different network scenarios, providing a statistical basis for subsequent calculations and judgments.

[0046] S504, calculate the correlation coefficient between feature parameters and user experience. The analysis and processing unit 7010 uses the Pearson correlation coefficient method, based on historical experience data, to calculate the correlation coefficient between each feature parameter and user experience. Specifically, the current feature parameter value sequence and the corresponding quantified user experience sequence can be used as variables and substituted into the Pearson correlation coefficient formula for calculation. It can be understood that the larger the absolute value of the calculated correlation coefficient, the more significant the impact of that feature parameter on user experience.

[0047] S505, compare the correlation coefficient value with the warning threshold value. The analysis and processing unit 7010 obtains the corresponding warning threshold value based on the current feature parameters. Subsequently, the calculated correlation coefficient value is compared with the corresponding warning threshold value in real time.

[0048] If the correlation coefficient value reaches the warning threshold, an abnormal risk is identified. If the warning threshold is not reached, the current state is considered normal, and the relevant data is simply archived and stored without triggering a warning.

[0049] S506, a data link anomaly warning event is generated when the correlation coefficient value reaches the warning threshold. When it is determined that there is an abnormal risk in the data link, the analysis and processing unit 7010 generates a corresponding data link anomaly warning event, which includes at least: the triggering characteristic parameter, the calculated correlation coefficient value, the warning threshold value, and the warning trigger timestamp.

[0050] After a data link anomaly warning event is generated, it is transmitted to the display component 704 in real time to notify maintenance personnel to intervene promptly in a visual manner. Related characteristic parameters, correlation coefficient values, and other data are then stored in the storage component 703 for subsequent fault tracing and model optimization.

[0051] Figure 6 The illustration shows a schematic diagram of the abnormal event storage process of a terminal fault monitoring and early warning method according to an embodiment of this application.

[0052] like Figure 6 As shown, the steps are as follows: S601, Prepare the data link anomaly warning event and related data to be stored. After generating the data link anomaly warning event, the analysis and processing unit 7010 prepares the event and the corresponding characteristic parameters that triggered the event, the calculated correlation coefficient value, the corresponding warning threshold value, and the timestamp, and other related data.

[0053] S602, the abnormal alarm event-related data is transmitted to the storage component 703. The analysis and processing unit 7010 transmits the data link abnormal warning event and related data to the storage component 703. Optionally, this transmission process can be based on a network protocol such as FTP, or on any other applicable transmission protocol, to ensure that the data can be reliably transmitted to the storage component 703.

[0054] S603, storage component 703 receives data. Storage component 703 receives data link anomaly warning events and related data sent by analysis and processing unit 7010. Optionally, storage component 703 may perform reception confirmation, such as verifying data link anomaly warning events and related data, thereby establishing a reliable foundation for subsequent persistent storage operations.

[0055] S604, Storage component 703 saves and backs up data. Storage component 703 persistently stores the received relevant data. Optionally, to prevent data loss, storage component 703 can use a backup mechanism to synchronously store the data to a standby storage node.

[0056] Figure 7 The schematic diagram illustrates the overall structure of a terminal fault monitoring and early warning device according to an embodiment of this application.

[0057] like Figure 7 As shown, it includes a communication processing component 701, an application processing component 702, and a storage component 703.

[0058] The communication processing component 701 includes a data acquisition unit 705, a data aggregation unit 706, a data processing unit 707, and a command transmission unit 708. The data acquisition unit 705 actively acquires relevant characteristic parameters of the data link's L2 and L3 layers according to a preset acquisition cycle. The data aggregation unit 706 classifies and transmits the received characteristic parameters. The data processing unit 707 parses and aggregates the classified parameters to generate reporting information. The command transmission unit 708 encapsulates the reporting information into control commands according to the AT command format and transmits them to the application processing component 702.

[0059] Application processing component 702 includes command interface unit 709 and analysis processing unit 7010. Command interface unit 709 receives and parses control commands from communication processing component 701, extracting characteristic parameters. Analysis processing unit 7010 uses the Pearson correlation coefficient method to calculate the correlation coefficient between characteristic parameters and user experience; sets dynamic warning thresholds based on historical experience data; and compares the calculated correlation coefficient value with the threshold value, generating a corresponding data link anomaly warning event when the condition is met.

[0060] Storage component 703 is configured to persistently store multiple key data, including but not limited to: feature parameters collected by communication processing component 701, correlation coefficient values ​​and warning threshold values ​​calculated by application processing component 702, and generated data link anomaly warning events. Under normal conditions, communication processing component 701 periodically transmits the aforementioned feature parameters and correlation coefficient values ​​to storage component 703 for storage according to a preset collection cycle for the feature parameters, and stores the data link anomaly warning event and related data in real time when a data link anomaly warning event is generated. To ensure data security, storage component 703 can synchronize relevant data to a backup storage node through a data backup mechanism to prevent data loss due to single point of failure.

[0061] Optionally, the device may also include a display component 704. This component is connected to the application processing component 702 and the storage component 703 via a data interface, and can acquire data link anomaly warning events in real time, and present them to the operation and maintenance personnel in a visual form such as alarm pop-ups and statistical charts, so that the operation and maintenance personnel can quickly identify faults and locate the problem links, thereby accelerating the fault response and handling process.

[0062] Figure 8 The diagram illustrates the structure of the communication processing component of a terminal fault monitoring and early warning device according to an embodiment of this application.

[0063] like Figure 8As shown, it includes a data acquisition unit 705, a data aggregation unit 706, a data processing unit 707, and a command transmission unit 708.

[0064] The acquisition unit 705 includes an L2 acquisition subunit 7011 and an L3 acquisition subunit 7012.

[0065] The L2 acquisition subunit 7011 collects relevant characteristic parameters from the L2 layer of the data link according to a preset acquisition period. The collected characteristic parameters include: uplink packet loss count, uplink transmitted data packet count, and uplink / downlink data rates in the packet data aggregation protocol layer; downlink packet loss count and uplink retransmission count in the radio link control layer; and uplink bit error rate and downlink bit error rate in the media access control layer. The L3 acquisition subunit 7012 collects relevant characteristic parameters from the radio resource control layer in the L3 layer of the data link according to a preset acquisition period, including: uplink bandwidth, downlink bandwidth, signal quality of the serving cell, and signal strength of the serving cell.

[0066] The data aggregation unit 706 receives feature parameters from the acquisition unit 705, classifies the feature parameters based on the registration classification information, and transmits the classified feature parameters to the data processing unit 707.

[0067] The data processing unit 707 receives the classified feature parameters from the data aggregation unit 706 and parses the relevant feature parameters. Optionally, the reasonableness of the feature parameter values ​​can be verified. Finally, the processed feature parameters are aggregated into reporting information.

[0068] Command transmission unit 708 encapsulates the reported information generated by data processing unit 707 into control commands according to the AT command protocol. For example, the encapsulation process may include converting the reported information into a binary data stream and adding an AT command frame header, frame trailer, and CRC check field. After completing the above operations, command transmission unit 708 transmits the generated control commands to application processing component 702.

[0069] Figure 9 The illustration shows a schematic diagram of the application processing component structure of a terminal fault monitoring and early warning device according to an embodiment of this application.

[0070] like Figure 9 As shown, it includes a command interface unit 709 and an analysis and processing unit 7010.

[0071] The command interface unit 709 is configured to interact with the communication processing component 701, receive control commands from the communication processing component 701, and parse them to extract the encapsulated feature parameters. Optionally, the command interface unit 709 can verify the control command data, for example, by using CRC check to ensure integrity, and request retransmission if data corruption is found. Finally, the extracted feature parameters are forwarded to the analysis and processing unit 7010.

[0072] After receiving the data forwarded by the command interface unit 709, the analysis and processing unit 7010 performs the following operations: The Pearson correlation coefficient method was used to calculate the correlation coefficient between each feature parameter and the user experience.

[0073] By accessing storage component 703, historical experience data can be obtained, and warning thresholds can be set based on the historical experience data. The warning thresholds can be set differently for different types of feature parameters and can be dynamically adjusted.

[0074] The calculated correlation coefficient value is compared with the corresponding warning threshold value. When the correlation coefficient value reaches the warning threshold value, a data link anomaly warning event is immediately generated, which contains necessary context information, such as the triggering feature parameters, correlation coefficient value, warning threshold value, timestamp, etc.

[0075] After generating a data link anomaly warning event, the analysis and processing unit 7010 sends it to the storage component 703 for persistent storage, and at the same time pushes the corresponding data link anomaly warning event to the display component 704 to realize real-time alarm for operation and maintenance personnel.

[0076] Figure 10 The schematic diagram illustrates the structure of the data acquisition unit of a terminal fault monitoring and early warning device according to an embodiment of this application.

[0077] like Figure 10 As shown, it includes an L2 acquisition subunit 7011 and an L3 acquisition subunit 7012.

[0078] The L2 acquisition subunit 7011 is configured to collect relevant characteristic parameters of each protocol layer in the data link L2 layer according to a preset acquisition period, forming the basis of evaluation data for transmission integrity and transmission efficiency. These parameters include: uplink packet loss, uplink transmitted data packets, and uplink and downlink data rates in the packet data aggregation protocol layer; downlink packet loss and uplink retransmission count in the radio link control layer; and uplink bit error rate and downlink bit error rate in the media access control layer.

[0079] The L3 acquisition subunit 7012 is also configured to acquire relevant characteristic parameters in the L3 layer of the data link according to the same preset acquisition period, specifically including: uplink bandwidth, downlink bandwidth, signal quality of the serving cell, signal strength of the serving cell, etc. in the radio resource control layer.

[0080] The L2 acquisition subunit 7011 and the L3 acquisition subunit 7012 ensure comprehensive coverage of the feature parameters of the L2 and L3 layers in the data link, avoiding abnormal misjudgments or omissions that may be caused by missing feature parameters, and laying a comprehensive and reliable data foundation for subsequent accurate correlation analysis.

[0081] Figure 11 An exemplary block diagram of a computer program product of a terminal fault monitoring and early warning method according to an embodiment of this application is shown schematically.

[0082] like Figure 11 As shown, the computer program product stores a computer program 1101, which, when executed by a processor, implements the method provided in any embodiment of this application.

[0083] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0084] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0085] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0086] It should also be noted that in the system and method of this application, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of this application.

[0087] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this application is not limited to the specific aspects of the processes, machines, manufacturing, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufacturing, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described above can be utilized. Therefore, the appended claims include such processes, machines, manufacturing, events, means, methods, or actions within their scope.

[0088] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0089] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A terminal fault monitoring and early warning method, characterized in that, Includes the following steps: Collect characteristic parameters of the data link; The collected feature parameters are analyzed, and the correlation coefficient between the feature parameters and the user experience is calculated. Set early warning thresholds based on historical experience data; When the correlation coefficient value reaches the warning threshold, a data link anomaly warning event is generated.

2. The terminal fault monitoring and early warning method as described in claim 1, characterized in that, The characteristic parameters of the data link include at least one of the following: uplink packet loss, uplink transmitted data packets, and uplink / downlink data rates at the Packet Data Convergence Protocol (PDCP) layer; downlink packet loss and uplink retransmission count at the Radio Link Control (RANC) layer; uplink bit error rate and downlink bit error rate at the Media Access Control (MAC) layer; and uplink bandwidth, downlink bandwidth, signal quality of the serving cell, and signal strength of the serving cell at the Radio Resource Control (RRC) layer.

3. The terminal fault monitoring and early warning method as described in claim 2, characterized in that, After receiving the collected feature parameters, the system classifies the features according to the registration classification information corresponding to the feature parameters, and then transmits the classified feature parameters to the data processing unit of the communication processing component.

4. The terminal fault monitoring and early warning method as described in claim 3, characterized in that, The classified feature parameters are processed to generate control commands, including: Analyze the feature parameters; The feature parameters are aggregated into reported information; The reported information is encapsulated into the control command and transmitted to the application processing component.

5. The terminal fault monitoring and early warning method as described in claim 4, characterized in that, The control commands are parsed and processed, including: Receive the control command and parse the feature parameters; The correlation coefficient between the feature parameter and the user experience is calculated using the Pearson correlation coefficient method.

6. The terminal fault monitoring and early warning method as described in claim 1, characterized in that, After generating the data link anomaly warning event, at least one of the corresponding feature parameters, the correlation coefficient value, and the warning threshold value is stored along with the data link anomaly warning event.

7. A terminal fault monitoring and early warning device, characterized in that, It includes communication processing components, application processing components, and storage components; among which, The communication processing component includes: a data acquisition unit configured to acquire feature parameters of the data link L2 and L3 layers; a data aggregation unit configured to classify and transmit the feature parameters based on the registration classification information corresponding to the feature parameters; a data processing unit configured to process the feature parameters and generate reporting information; and a command transmission unit configured to encapsulate the reporting information into control commands and transmit them to the application processing component. The application processing component includes: a command interface unit configured to receive and parse the control command; and an analysis and processing unit configured to calculate the correlation coefficient between the feature parameters and the user experience, set a warning threshold based on historical experience data, and generate a data link abnormality warning event when the correlation coefficient reaches the warning threshold. The storage component is configured to store at least one of the feature parameters, the correlation coefficient value, the early warning threshold value, and the data link anomaly warning event.

8. The terminal fault monitoring and early warning device as described in claim 7, characterized in that, The acquisition unit includes an L2 acquisition subunit and an L3 acquisition subunit; wherein... The L2 acquisition subunit is configured to acquire at least one of the characteristic parameters described in the packet data aggregation protocol layer, the radio link control layer, and the media access control layer. The L3 acquisition subunit is configured to acquire the characteristic parameters in the radio resource control layer.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program implements the terminal fault monitoring and early warning method according to any one of claims 1 to 6.

10. A computer program product comprising a computer-readable storage medium on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the terminal fault monitoring and early warning method according to any one of claims 1 to 6.

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