Problem processing method and apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a problem processing method and device, electronic equipment and storage medium, belonging to the technical field of computer. The method comprises: in a problem detection interface of an application program, displaying at least one problem existing in the application program, the at least one problem being determined based on user feedback data of the user for the application program; for any one of the at least one problem, displaying a technical severity level of the problem based on at least one of an influence degree feature, an influence range feature and an occurrence frequency feature of the problem; and performing hierarchical processing on the at least one problem based on the technical severity level of the at least one problem. The above method can improve the information transmission efficiency of the problem detection interface, reduce the risk of missing processing of urgent problems, and improve the interactive experience of terminal-side problem management.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a problem-solving method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the increasing popularity of enterprise-level applications, the efficient detection and handling of user feedback issues has become a crucial aspect of product operations.

[0003] Currently, issue detection tools typically display user-reported issues in a list format, with each issue including basic information such as title and submission time. Some tools also support manual priority marking.

[0004] However, when there are a large number of issues, with issues of different importance mixed together, operators need to read them one by one to determine the order of processing, resulting in low efficiency in information filtering. The handling of urgent issues may be delayed due to visual interference. Summary of the Invention

[0005] This disclosure provides a problem-handling method, apparatus, electronic device, and storage medium, which improves the information transmission efficiency of the problem detection interface, reduces the risk of overlooking urgent problems, and enhances the interactive experience of problem management on the terminal side. The technical solution of this disclosure is as follows: According to one aspect of the embodiments of this disclosure, a problem-solving method is provided, including: The application's problem detection interface displays at least one problem existing in the application, which is determined based on user feedback data regarding the application; For any of the at least one problem, the technical severity level of the problem is displayed based on at least one of the problem's impact degree feature, impact range feature, and occurrence frequency feature. The impact degree feature is used to indicate the degree to which the problem blocks users' core tasks. The impact range feature is used to indicate the scale of users affected by the problem. The occurrence frequency feature is used to indicate the number of times the problem is reproduced per unit time. Based on the technical severity level of the at least one problem, the at least one problem is classified and processed accordingly.

[0006] According to one aspect of the embodiments of this disclosure, another problem-solving method is provided, including: Based on user feedback data for the application, at least one problem is identified in the application, wherein the user feedback data is descriptive information submitted by users regarding their experience with the application. For any one of the at least one issues, the technical severity level of the issue is determined based on at least one of the issue's impact degree characteristics, impact scope characteristics, and occurrence frequency characteristics. The impact degree characteristics are used to indicate the degree to which the issue blocks users' core tasks, the impact scope characteristics are used to indicate the scale of users affected by the issue, and the occurrence frequency characteristics are used to indicate the number of times the issue is reproduced per unit time. Based on the technical severity level of the at least one problem, the at least one problem is classified and processed accordingly.

[0007] According to another aspect of the embodiments of this disclosure, a problem-solving apparatus is provided, comprising: The display unit is configured to perform an operation in the application's problem detection interface to display at least one problem existing in the application, the at least one problem being determined based on user feedback data regarding the application. The display unit is further configured to perform a technical severity level of the problem based on at least one of the problem's impact severity feature, impact range feature, and occurrence frequency feature. The impact severity feature is used to indicate the degree to which the problem blocks the user's core tasks. The impact range feature is used to indicate the scale of users affected by the problem. The occurrence frequency feature is used to indicate the number of times the problem is reproduced per unit time. The processing unit is configured to perform graded processing of the at least one problem based on the technical severity level of the at least one problem.

[0008] In some embodiments, the application has several problems; The display unit is configured to perform any of the following: Based on the technical severity level of the problem, the problem detection interface of the application displays multiple problems existing in the application in a categorized manner; According to the response level of the problem, the problem detection interface of the application displays multiple problems existing in the application in categories, and the response level is used to indicate the urgency of handling the problem. Based on the functional modules involved in the problem within the application, the multiple problems present in the application are categorized and displayed in the application's problem detection interface.

[0009] In some embodiments, the processing unit is configured to, for any one of the at least one problems, if the technical severity level reaches the deep analysis condition, display the technical root cause path of the problem. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause. The technical root cause path includes multiple nodes, each node indicating a technical aspect involved in the problem.

[0010] In some embodiments, the display unit is further configured to perform a trigger operation on any node in the technical root cause path, displaying description information of the node, the description information being used to describe the technical principle of the node.

[0011] In some embodiments, the processing unit is configured to perform graded processing of the at least one problem based on the technical severity level of the at least one problem, including at least one of the following: In descending order of technical severity, the at least one problem is sequentially pushed to the terminals of the administrators corresponding to the functional modules in which the problems occur. For any of the at least one problem, if the user who reported the problem reaches a preset level and the technical severity level reaches the condition for in-depth analysis, the problem and the technical root cause path of the problem are pushed to the terminal of the administrator corresponding to the functional module where the problem occurred. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause.

[0012] In some embodiments, the display unit is further configured to, for any one of the at least one problems, display the response level of the problem in the problem detection interface based on multiple features of the problem. The multiple features are obtained by multi-dimensional feature extraction of user feedback data and user behavior data related to the problem. The multiple features are used to reflect the attributes of the problem from multiple different dimensions. The user behavior data is the operation log generated by the user during the use of the application. The response level is used to indicate the urgency of handling the problem.

[0013] In some embodiments, the processing unit is further configured to, for any one of the at least one problems, display the target functional module in the application where the problem occurs in the problem detection interface; and push the problem to the terminal of the administrator corresponding to the target functional module within the time range corresponding to the response level, wherein the higher the response level, the shorter the corresponding time range.

[0014] In some embodiments, the processing unit is further configured to, for any one of the at least one problems, display the user's level of feedback on the problem in the problem detection interface; and, if the level reaches a preset level and the response level of the problem reaches a first preset level, prioritize pushing the problem to the terminal of the administrator corresponding to the target functional module where the problem occurred.

[0015] In some embodiments, the display unit is further configured to display a follow-up object for any of the at least one problem, the follow-up object including at least one of an administrator responsible for resolving the problem and a user who provides feedback on the problem.

[0016] In some embodiments, the display unit is further configured to highlight a target problem among the at least one problems in the problem detection interface; Among them, the user level that provides feedback on the target problem reaches a preset level, and the technical severity level of the target problem reaches a second preset level.

[0017] In some embodiments, the display unit is further configured to perform a triggering operation in response to the target problem, displaying and providing feedback on a user's session service interface for communicating the target problem to the user.

[0018] In some embodiments, the display unit is further configured to perform at least one of the following: The problem detection interface displays the total number of the at least one problem; The problem detection interface displays the total number of functional modules involved in the application for the at least one problem; The problem detection interface displays the number of problems of each technical severity level among the at least one problem; The problem detection interface displays the proportion of each technical severity level of the at least one problem in the at least one problem; The problem detection interface displays the number of problems at each response level among the at least one problem; The problem detection interface displays the proportion of each response level of the at least one problem in the at least one problem; The problem detection interface displays the number of problems in each functional module involved in the at least one problem; The problem detection interface displays the proportion of problems in each functional module involved in the at least one problem in the at least one problem.

[0019] In some embodiments, the display unit is further configured to perform any of the following: In the problem detection interface, the number of problems at each response level in the at least one problem is displayed in the form of a pie chart; In the problem detection interface, the number of problems of each response level in the at least one problem is displayed in separate areas, and the number of problems of each response level is positively correlated with the area of ​​the corresponding area.

[0020] In some embodiments, the display unit is further configured to perform a viewing operation in response to any problem, displaying a details panel of the problem in the problem detection interface, the details panel displaying at least one of the following: multiple characteristics of the problem, the criteria for determining the technical severity level of the problem, the cause of the problem, and the solution to the problem.

[0021] In some embodiments, the display unit is further configured to, in the problem detection interface, display a countdown control for a problem whose response level has reached the highest level. The countdown control is used to indicate the remaining time until the deadline for starting to process the problem, and the response level is used to indicate the urgency of processing the problem.

[0022] In some embodiments, the display unit is further configured to display a sentiment label for any one of the at least one questions, the sentiment label indicating the intensity of negative emotions of the user who submitted the question.

[0023] In some embodiments, the display unit is further configured to, for any one of the at least one problems, display the processing progress of the problem in the problem detection interface in the form of a timeline, wherein the timeline is marked with at least one of response time, start processing time, repair completion time and satisfaction verification time, wherein the response time is the time when the object responsible for resolving the problem receives the problem.

[0024] In some embodiments, the display unit is further configured to display user feedback data for the application in the problem detection interface, the user feedback data being descriptive information submitted by the user regarding the application experience.

[0025] According to another aspect of the embodiments of this disclosure, another problem-solving apparatus is provided, including: The first determining unit is configured to perform an action based on user feedback data for the application to determine at least one problem existing in the application, wherein the user feedback data is descriptive information submitted by a user about the experience of the application. The second determining unit is configured to perform an action based on at least one of the following: the degree of impact characteristic, the scope of impact characteristic, and the frequency of occurrence characteristic, to determine the technical severity level of the problem. The degree of impact characteristic is used to indicate the degree of obstruction of the problem to the user's core tasks. The scope of impact characteristic is used to indicate the scale of users affected by the problem. The frequency of occurrence characteristic is used to indicate the number of times the problem is reproduced per unit time. The processing unit is configured to perform graded processing of the at least one problem based on the technical severity level of the at least one problem.

[0026] In some embodiments, the processing unit is configured to, for any one of the at least one problems, if the technical severity level of the problem reaches the conditions for deep analysis, determine the technical root cause path of the problem based on the module knowledge graph of the application and user behavior data related to the problem. The module knowledge graph is used to indicate the relationship between various functional modules in the application, and the technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause.

[0027] In some embodiments, the processing unit is configured to, for any one of the at least one problems, determine a historical solution matching the problem from a solution library based on the cause and technical severity level of the problem; and, using a large language model, adapt and rewrite the historical solution according to the business scenario of the application to obtain a solution to the problem.

[0028] In some embodiments, the apparatus further includes: The feature extraction unit is configured to perform multi-dimensional feature extraction on user feedback data and user behavior data related to the at least one question for any of the at least one questions, to obtain multiple features of the question, wherein the user behavior data is the operation log generated by the user during the use of the application; The third determining unit is configured to perform a response level for the problem based on multiple characteristics of the problem, the response level being used to indicate the urgency of handling the problem; The processing unit is also configured to execute a response level based on the problem and push the problem to the terminal of the administrator corresponding to the functional module where the problem occurred.

[0029] In some embodiments, the plurality of features of each problem include at least two of magnitude features, pain features, impact features, and cost features; The magnitude feature is used to indicate the frequency with which the problem is reported; The pain signature is used to indicate the degree of negative impact of the problem on the user experience; The impact characteristics are used to indicate the degree of impact of the problem on business metrics or user coverage. The cost characteristic is used to indicate the level of resources required to fix the problem.

[0030] In some embodiments, the third determining unit is configured to perform a weighted summation of the feature values ​​of the plurality of features based on the weights of the plurality of features to obtain a score for the problem, wherein the weight of each feature is used to indicate the degree of contribution of the feature to the urgency of the problem, and the score is used to indicate the overall urgency of the problem; and the response level corresponding to the threshold range in which the score falls is determined as the response level of the problem.

[0031] According to another aspect of the present disclosure, an electronic device is provided, the electronic device comprising: One or more processors; Memory used to store the executable program code of the processor; The processor is configured to execute the program code to implement the aforementioned problem-solving method.

[0032] According to another aspect of the present disclosure, a computer-readable storage medium is provided that, when program code in the computer-readable storage medium is executed by a processor of an electronic device, enables the electronic device to perform the above-described problem-solving method.

[0033] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the above-described problem-solving method.

[0034] The solution provided in this disclosure directly displays the severity level of a problem, determined by three technical dimensions: impact degree, impact scope, and frequency of occurrence, within the problem detection interface of the application. This allows operators to intuitively understand the severity of each problem without having to review feedback details one by one, overcoming the shortcomings of traditional problem lists that are cluttered and difficult to quickly locate key issues. Furthermore, by classifying and processing problems according to their severity levels, the solution achieves automated linkage between interface display and processing decisions, preventing delays in addressing severe issues due to visual interference or human judgment bias. This method improves the information transmission efficiency of the problem detection interface, reduces the risk of overlooking severe issues, and enhances the interactive experience of problem management on the terminal side.

[0035] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0037] Figure 1 This is a schematic diagram illustrating the implementation environment of a problem-solving method according to an exemplary embodiment.

[0038] Figure 2 This is a flowchart illustrating a problem-solving method according to an exemplary embodiment.

[0039] Figure 3 This is a flowchart illustrating another problem-solving method according to an exemplary embodiment.

[0040] Figure 4 This is a flowchart illustrating another problem-solving method according to an exemplary embodiment.

[0041] Figure 5 This is a schematic diagram illustrating a classification problem according to an exemplary embodiment.

[0042] Figure 6 This is a schematic diagram illustrating problem statistics according to an exemplary embodiment.

[0043] Figure 7 This is a schematic diagram illustrating another classification of problems according to an exemplary embodiment.

[0044] Figure 8 This is a schematic diagram illustrating a details panel according to an exemplary embodiment.

[0045] Figure 9 This is a block diagram illustrating a problem-solving apparatus according to an exemplary embodiment.

[0046] Figure 10 This is a block diagram illustrating another problem-solving apparatus according to an exemplary embodiment.

[0047] Figure 11 This is a block diagram illustrating a terminal according to an exemplary embodiment.

[0048] Figure 12 This is a block diagram illustrating a server according to an exemplary embodiment. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0050] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0051] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the user feedback data involved in this disclosure was obtained with full authorization.

[0052] Figure 1 This is a schematic diagram illustrating an implementation environment for a problem-solving method according to an exemplary embodiment. Taking an electronic device as an example, see [example description missing]. Figure 1 The implementation environment specifically includes: terminal 101 and server 102.

[0053] In some embodiments, terminal 101 is at least one of a smartphone, tablet, desktop computer, laptop, smartwatch, or similar device. Terminal 101 runs an application that supports problem detection. Users (such as product managers, operations personnel, or technical leads) can log in to the application's problem detection interface through terminal 101 to view and resolve user experience issues. Terminal 101 can connect to server 102 via a wireless or wired network, thereby receiving problem data and response level information pushed by server 102, and displaying the problem detection interface and related interactive controls on the terminal 101's screen.

[0054] Terminal 101 generally refers to one of a plurality of terminals; this embodiment uses terminal 101 as an example. Those skilled in the art will understand that the number of terminals can be more or less. For example, there may be several terminals, or dozens or hundreds of terminals, or even more. This disclosure does not limit the number of terminals or the type of device.

[0055] Server 102 can be at least one of a single server, multiple servers, a cloud computing platform, and a virtualization center. Server 102 can connect to terminal 101 and other terminals via a wireless or wired network. Server 102 can receive user feedback data and user behavior data from multiple channels, perform background analysis operations such as multi-dimensional feature extraction and response level calculation, and push the analyzed problem list and the corresponding response level to terminal 101. In some embodiments, the number of servers can be more or less, and this disclosure does not limit this. Of course, server 102 also includes other functional servers, such as data storage servers and message push servers, to provide more comprehensive and diversified services.

[0056] Figure 2 This is a flowchart illustrating a problem-solving method according to an exemplary embodiment, see [link to flowchart]. Figure 2 The problem-solving method is applied to the terminal and includes the following steps: In step 201, the terminal displays at least one problem in the application's problem detection interface, and the at least one problem is determined based on user feedback data regarding the application.

[0057] In this embodiment, user feedback data refers to descriptive information about experience issues actively submitted by users during application usage, such as "uploading videos always fails" in the feedback portal or "cross-account operations are too cumbersome" in a survey questionnaire. The server or terminal itself can identify specific problems in the application based on this feedback data, such as "slow upload speeds" or "low efficiency in cross-account management." The terminal displays these problems in a list, card, or other visual format on the problem detection interface, allowing operators (such as product managers, operations personnel, or technical leads) to see all the issues requiring attention at a glance without manually reviewing the original feedback records.

[0058] In step 202, for any one of the at least one problems, the terminal displays the technical severity level of the problem based on at least one of the problem's impact degree characteristics, impact range characteristics, and occurrence frequency characteristics. The impact degree characteristics are used to indicate the degree to which the problem blocks the user's core tasks, the impact range characteristics are used to indicate the scale of users affected by the problem, and the occurrence frequency characteristics are used to indicate the number of times the problem is reproduced per unit time.

[0059] In this embodiment, the technical severity level is a quantitative indicator obtained by comprehensively evaluating a problem based on three technical dimensions: impact degree, impact scope, and occurrence frequency. The impact degree characteristic measures the extent to which the problem obstructs a user's core tasks, such as whether it completely blocks critical processes (fatal), causes severe operational difficulties (serious), or whether there are alternative solutions (moderate). The impact scope characteristic measures the scale of users affected by the problem, such as the number of daily active users affected, the proportion of large customers, and the level of functional modules involved. The occurrence frequency characteristic measures the number of times the problem recurs per unit of time, including the frequency of user feedback and the number of errors in the system logs. The server pre-determines the technical severity level of the problem (e.g., fatal, severe, moderate, minor) based on one or more of the above three dimensions using a rule engine or supervised learning model, and sends this level along with the problem to the terminal. The terminal displays the corresponding technical severity level for each problem on the interface, for example, using a red label to indicate "fatal," an orange label to indicate "severe," a yellow label to indicate "moderate," and a gray label to indicate "minor." In this way, operators can quickly understand the technical hazard of each problem without having to open the details, and thus determine which problems need to be prioritized for root cause analysis.

[0060] For example, if the "material upload lag" problem is rated as the highest technical severity level because it completely blocks the creation process of advertising materials (critical impact), affects more than 60% of major clients (wide impact), and recurs frequently every day (high frequency of occurrence), the problem will be presented in a prominent way in the interface, prompting the person in charge to prioritize root cause investigation.

[0061] In step 203, the terminal classifies at least one problem based on its technical severity level.

[0062] In this embodiment of the disclosure, graded processing refers to taking differentiated measures to handle problems based on their severity levels. The terminal can automatically sort the problem list according to their severity level, with critical problems at the top, followed by severe problems, and so on. It can also determine whether to trigger a deep analysis process based on the severity level. For example, for problems reaching the "severe" or "critical" level, the terminal can automatically expand the root cause path display area to allow relevant personnel to quickly locate the root cause of the problem; for "general" or "minor" problems, only the detailed information is displayed normally in the list or collapsed, and the operator handles it according to the conventional process.

[0063] For example, for a "critical" issue like "material upload lag," the terminal can automatically expand and display the root cause of the problem in the details panel (such as timeouts caused by a missing upload commitment fragmentation mechanism) and prompt the relevant personnel to prioritize its handling. For "general" issues like minor interface style adjustments, the problem is simply displayed normally in the issue list, and the product manager will arrange optimization according to the iteration plan. This tiered handling mechanism ensures that limited technical analysis and repair resources are prioritized for the most technically threatening issues, thereby improving the overall efficiency of problem diagnosis and resolution.

[0064] This disclosure provides a problem-handling method that directly displays the severity level of a problem, determined by three technical dimensions: impact degree, impact scope, and frequency of occurrence, on the problem detection interface of an application. This allows operators to intuitively understand the severity of each problem without having to review feedback details one by one, overcoming the shortcomings of traditional problem lists that are cluttered and difficult to quickly locate key issues. Furthermore, by classifying problems according to their severity levels, the method achieves automated linkage between interface display and processing decisions, avoiding delays in handling severe problems due to visual interference or human judgment bias. This method improves the information transmission efficiency of the problem detection interface, reduces the risk of missing severe problems, and enhances the interactive experience of problem management on the terminal side.

[0065] In some embodiments, the application has several problems; The application's problem detection interface displays at least one problem that exists in the application, including any of the following: Based on the technical severity level of the problem, the application's problem detection interface displays multiple problems categorized by the application's severity level. Based on the response level of the problem, the application's problem detection interface displays multiple problems in categories, with the response level indicating the urgency of the problem. Based on the functional modules involved in the application, the application's problem detection interface displays multiple problems categorized by function.

[0066] The solution provided in this disclosure displays multiple issues in the problem detection interface according to their technical severity level, response level, or functional module. This allows operators to quickly filter out the issues that need to be addressed based on their responsibilities or current priorities. Categorizing by technical severity level facilitates prioritizing issues with greater technical risks; categorizing by response level facilitates prioritizing urgent issues; and categorizing by functional module allows the responsible personnel of the corresponding module to focus on issues within their field. This flexible categorization reduces information search time, avoids the cognitive burden caused by a cluttered arrangement of issues, and thus improves the efficiency of problem filtering and location.

[0067] In some embodiments, at least one problem is graded based on its technical severity level, including: For any one of the at least one problems, if the technical severity level reaches the condition for in-depth analysis, the technical root cause path of the problem is displayed. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause. The technical root cause path includes multiple nodes, each node is used to indicate a technical aspect involved in the problem.

[0068] The solution provided in this disclosure, when the technical severity of a problem reaches the level required for in-depth analysis, displays the root cause path in the problem detection interface, presenting the reasoning process from the problem phenomenon to the root cause at the technical level in a visually intuitive way, in the form of a node chain. Each node indicates a technical link, enabling operators to clearly trace the propagation chain and dependencies of the problem. This visual root cause display method helps avoid ambiguity caused by plain text descriptions, improves the accuracy and efficiency of problem localization, and facilitates R&D personnel to quickly identify the target for repair.

[0069] In some embodiments, the method further includes: In response to a trigger operation on any node in the technical root cause path, the node's descriptive information is displayed, which describes the node's technical principles.

[0070] The solution provided in this disclosure responds to user triggering operations on any node in the root cause path, displaying a description of the technical principle of that node. Operators can gain a deeper understanding of the details of specific technical aspects as needed, without needing to consult documentation or others. This solution helps enhance the interpretability and understandability of the root cause path, lowers the knowledge acquisition threshold, improves the depth and efficiency of problem analysis, and facilitates the team's rapid development of remediation solutions.

[0071] In some embodiments, at least one problem is graded based on its technical severity level, including at least one of the following: In descending order of technical severity, at least one issue will be pushed sequentially to the terminal of the administrator corresponding to the functional module where the issue occurred. For any one of the at least one issues, if the user who reported the issue reaches a preset level and the technical severity level meets the conditions for in-depth analysis, the issue and its technical root cause path will be pushed to the terminal of the administrator corresponding to the functional module where the issue occurred. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the root cause at the technical level.

[0072] The solution provided in this disclosure pushes issues sequentially according to their technical severity level, from highest to lowest, or prioritizes pushing issues and their root causes when both the user level and the technical severity level meet the criteria. This ensures that issues with greater technical harm are pushed to administrators' terminals earlier, while also guaranteeing that high-value users receive more complete diagnostic information for their high-level technical issues. This solution helps to rationally allocate technical analysis resources, avoids delays in handling high-severity issues due to mixed problem processing, and improves the orderliness and targeting of problem handling.

[0073] In some embodiments, the method further includes: For any one of the at least one issues, the response level of the issue is displayed in the issue detection interface based on multiple features of the issue. These multiple features are obtained by multi-dimensional feature extraction from user feedback data and user behavior data related to the issue. These multiple features are used to reflect the attributes of the issue from multiple different dimensions. The user behavior data is the operation log generated by the user during the use of the application. The response level is used to indicate the urgency of handling the issue.

[0074] The solution provided in this disclosure, based on the display of the technical severity level, further extracts and displays the response level based on multi-dimensional features. It combines the technical severity level (reflecting the degree of technical harm of the problem) with the response level (reflecting the urgency of handling the problem), enabling operators to comprehensively understand the attributes of the problem from both technical depth and business urgency dimensions. This fusion display of multi-level assessment information helps improve the accuracy of operators' judgment of the nature of the problem and optimizes the quality of handling decisions.

[0075] In some embodiments, the method further includes: For any one of the at least one problems, the target functional module in the application where the problem occurs is displayed in the problem detection interface; Within the time frame corresponding to the response level, the issue is pushed to the terminal of the administrator corresponding to the target functional module. The higher the response level, the shorter the corresponding time frame.

[0076] The solution provided in this disclosure displays the target functional module to which the problem belongs in the problem detection interface, enabling operators to clearly understand the responsibility for the problem. Simultaneously, based on the response level, the problem is pushed to the management terminal within the corresponding time frame, with higher response levels having shorter time frames. This solution achieves hierarchical management of problem push notifications based on timeliness, ensuring that urgent problems receive faster response channels and avoiding resource misallocation caused by treating all problems the same, thereby improving the timeliness of problem handling and the rationality of resource allocation.

[0077] In some embodiments, the method further includes: For any one of the at least one issues, the issue detection interface displays the hierarchy of the user who reported the issue; When the level reaches the preset level and the response level of the problem reaches the first preset level, the problem will be pushed to the terminal of the administrator corresponding to the target functional module where the problem occurred.

[0078] The solution provided in this disclosure displays the user hierarchy of feedback issues in a tiered processing mechanism. When a user's hierarchy reaches a preset value and the response level reaches a first preset level, the issue is prioritized for processing. This allows high-value users' urgent issues to receive priority in processing, preventing delays in responding to major customer issues due to ordinary issues consuming processing resources. This solution improves the experience assurance capability for high-value users, reduces the risk of major customer churn caused by processing delays, and optimizes the issue handling priority strategy.

[0079] In some embodiments, the method further includes: For any one of the at least one issues, display the follow-up targets for the issue, which include at least one of the managers responsible for resolving the issue and the users who reported the issue.

[0080] The solution provided in this disclosure displays the follow-up recipients for each issue, including information on the manager responsible for resolving the issue and / or the user who provided feedback. This allows operators to clearly understand the current responsible party and the source of feedback for the issue. This avoids multiple people repeatedly handling the same issue or issues being shelved due to no one claiming responsibility, facilitating team collaboration and communication traceability. This solution improves the transparency of the issue handling process, reduces management costs caused by unclear information, and enhances team collaboration efficiency.

[0081] In some embodiments, the method further includes: In the problem detection interface, the target problem is highlighted in at least one of the problems; Among them, the user level that provides feedback on the target problem reaches the preset level, and the technical severity level of the target problem reaches the second preset level.

[0082] The solution provided in this disclosure highlights target issues that meet both the "user level requirement" and the "technical severity level reaching the second preset level" in the issue detection interface, making high-tech hazard issues of high-value users visually stand out. Operators can notice these key issues immediately without any additional action, thus prioritizing the allocation of attention and processing resources. This solution reduces the probability of overlooking key issues through visual enhancement, improves the speed of identifying high-risk issues, and optimizes the risk warning capability in user experience management.

[0083] In some embodiments, the method further includes: In response to a triggered action on the target issue, the user's session service interface for the target issue is displayed and feedback is provided. The session service interface is used to communicate the status of the target issue with the user.

[0084] The solution provided in this disclosure responds to a user's triggered action regarding a target issue by directly displaying and providing feedback to the user's session service interface for communicating about the issue. This solution establishes a seamless interaction link between issue monitoring and user communication, allowing operators to quickly contact users without manually searching for their contact information or switching systems. This shortens the time required for issue clarification and feedback confirmation, reduces operational steps, improves communication efficiency, and facilitates timely reassurance of users or obtaining more details about the issue.

[0085] In some embodiments, the method further includes at least one of the following: The problem detection interface displays the total number of at least one problem; The problem detection interface displays the total number of functional modules involved in at least one problem within the application; The problem detection interface displays the number of problems at each technical severity level within at least one problem; The problem detection interface displays the proportion of each technical severity level of a problem in at least one problem. The problem detection interface displays the number of problems at each response level for at least one problem; The problem detection interface displays the proportion of each response level in at least one problem. The problem detection interface displays the number of problems in each functional module involved in at least one problem; The problem detection interface displays the proportion of problems in each functional module involved in at least one problem within that problem.

[0086] The solution provided in this disclosure displays multiple statistical information in the problem detection interface, including the total number of problems, the total number of functional modules involved, the number and percentage of problems at each technical severity level, the number and percentage of problems at each response level, and the number and percentage of problems in each functional module. These statistical indicators provide operators with a global perspective, helping them quickly understand the overall distribution and severity of current problems. Key data can be grasped without having to browse through the problem list one by one, facilitating resource allocation and decision-making. This solution improves problem situational awareness and reduces the manual cost of data aggregation and analysis.

[0087] In some embodiments, the problem detection interface displays the number of problems at each response level for at least one problem, including any of the following: The problem detection interface displays the number of problems at each response level in at least one problem in the form of a pie chart; In the problem detection interface, the number of problems at each response level is displayed in separate areas. The number of problems at each response level is positively correlated with the area of ​​the corresponding region.

[0088] The solution provided in this disclosure displays the number of issues at each response level using a pie chart or a region-based partitioning method, transforming abstract data into an intuitive graphical representation. The pie chart facilitates quick comparison of the proportion of issues at each level, while the region-based partitioning visually reflects differences in quantity through the size of the area. This graphical approach lowers the cognitive barrier to data interpretation, enabling operators to quickly assess the scale of the current urgent issue and improving the speed and accuracy of information acquisition.

[0089] In some embodiments, the method further includes: In response to viewing any problem, the problem detection interface displays a problem details panel. The details panel shows at least one of the following: multiple characteristics of the problem, the criteria for determining the technical severity level of the problem, the cause of the problem, and the solution to the problem.

[0090] The solution provided in this disclosure responds to a user's viewing of a problem by displaying at least one of the following in a details panel: multiple characteristics of the problem, the criteria for determining its technical severity level, the cause of the problem, and a solution. This solution presents in-depth analysis results in a centralized panel, allowing operators to obtain complete information without navigating through multiple pages, facilitating a quick understanding of the problem's essence and enabling effective decision-making. This reduces information retrieval time and improves the efficiency of problem diagnosis and resolution.

[0091] In some embodiments, the method further includes: In the problem detection interface, for problems with the highest response level, a countdown control is displayed. The countdown control indicates the remaining time before the deadline for starting problem processing, and the response level indicates the urgency of the problem.

[0092] The solution provided in this disclosure displays a countdown control for the highest-priority issues, dynamically indicating the remaining time until the processing deadline. This countdown control provides an intuitive reminder of time pressure, enabling operators to clearly perceive the level of urgency and avoid timeouts due to a lack of awareness. This helps strengthen the team's awareness of responding to the most urgent issues, improves SLA (Service Level Agreement) compliance rates, and reduces business risks caused by processing timeouts.

[0093] In some embodiments, the method further includes: For any one of the at least one questions, display the question's sentiment label, which indicates the intensity of the negative emotions of the user who submitted the question.

[0094] The solution provided in this disclosure displays an emotion tag for each issue, indicating the intensity of negative emotions from the user providing feedback. Operators can quickly determine the level of user dissatisfaction using these emotion tags, allowing them to prioritize calming users with strong emotions or adopt appropriate communication strategies when handling issues. This solution quantifies and visualizes users' emotional states, enhancing user empathy during problem-solving and improving the targeting and effectiveness of customer satisfaction management.

[0095] In some embodiments, the method further includes: For any one of the at least one issues, the problem detection interface displays the problem's processing progress in the form of a timeline. The timeline is marked with at least one of the following: response time, start processing time, repair completion time, and satisfaction verification time. The response time is the time when the person responsible for solving the problem receives the problem.

[0096] The solution provided in this disclosure displays the problem processing progress in a timeline format, marking key nodes such as response time, start processing time, repair completion time, and satisfaction verification time. The response time is defined as the time the responsible person receives the problem. This timeline clearly presents the timeliness of the entire problem lifecycle, facilitating operators to trace the time spent at each stage and identify processing bottlenecks. This improves the visibility and manageability of the problem handling process, helping to optimize team efficiency.

[0097] In some embodiments, the method further includes: The issue detection interface displays user feedback data for the application, which consists of user-submitted descriptions of the application experience.

[0098] The solution provided in this disclosure directly displays the original content of user feedback data, i.e., the user's original voice, in the problem detection interface. Operators can view the original description information submitted by the user without having to switch to other systems, avoiding distortion or omissions during information transmission. This helps to accurately understand the user's intent, improves the accuracy of problem judgment, and facilitates providing targeted responses to users after problem resolution, thus enhancing the completeness of problem handling and user satisfaction.

[0099] The above Figure 2 The problem-solving method of this disclosure has been illustrated using a terminal as the execution subject. The following section describes the problem-solving method of this disclosure using a server as the execution subject. Figure 3 This is a flowchart illustrating another problem-solving method according to an exemplary embodiment. See also Figure 3 The solution to this problem includes the following steps: In step 301, the server determines at least one problem with the application based on user feedback data for the application, whereby the user feedback data consists of descriptive information submitted by users regarding their application experience.

[0100] In this embodiment, user feedback data refers to text or voice descriptions of the user's experience actively submitted during application usage, such as "the file upload keeps spinning" submitted through the feedback portal, or complaints about "unresponsive interface" in customer service conversations. After collecting this data from various channels (such as feedback forms, surveys, customer group messages, etc.), the server cleans and analyzes it to identify specific problems actually existing in the application. For example, by analyzing multiple feedback entries mentioning "upload failure," the server can determine that there is a problem with the "file upload function malfunction." This step transforms the user's scattered subjective descriptions into a structured list of issues to be addressed, laying the foundation for subsequent analysis.

[0101] In step 302, for any one of the at least one problems, the server determines the technical severity level of the problem based on at least one of the problem's impact degree characteristics, impact scope characteristics, and occurrence frequency characteristics. The impact degree characteristics are used to indicate the degree to which the problem blocks the user's core tasks, the impact scope characteristics are used to indicate the scale of users affected by the problem, and the occurrence frequency characteristics are used to indicate the number of times the problem is reproduced per unit time.

[0102] In this embodiment, when determining the severity level of a technical issue, the server needs to comprehensively quantify and evaluate three dimensions: the degree of impact, the scope of impact, and the frequency of occurrence. Specifically, the server extracts keywords related to core task blocking (such as "unable," "failed," "stuck," etc.) from user feedback text, and combines this with data such as task interruption rate and operation abandonment rate reflected in user behavior logs to generate a degree of impact feature value; it obtains the number of users affected by the issue and their stratified distribution (such as the proportion of large customers) from the user profile database to generate a scope of impact feature value; and it statistically analyzes the number of error reports or user reproduction records of the issue per unit time from system logs to generate a frequency of occurrence feature value. The server then inputs one or more of the above feature values ​​into a preset evaluation model (such as a rule-based decision tree or a supervised learning model based on historical data) and automatically outputs the technical severity level of the issue. Unlike the terminal, which is only responsible for displaying the calculated level result, the server undertakes the complete calculation logic from raw data to level output in this step, ensuring the objectivity and consistency of the level assessment.

[0103] In step 303, the server classifies at least one problem based on its technical severity level.

[0104] In this embodiment, tiered processing refers to the server automatically triggering differentiated backend processing flows based on the severity level of the technical issue. For issues with a severity level of "fatal" or "serious," the server automatically invokes the root cause analysis engine to perform root cause path reasoning based on the application's module knowledge graph and user behavior data, appending the reasoning results to the issue data and pushing them to the relevant technical personnel. For issues with a severity level of "moderate," the server includes them in a regular iterative processing queue and uniformly assigns them to the responsible personnel of the corresponding functional modules according to a preset cycle. For issues with a severity level of "minor," the server only records and archives them without actively pushing them, and evaluates them when resources are available. In addition, the server can also sort multiple issues in descending order of technical severity level and push them sequentially, or prioritize pushing the issue and its root cause path to the corresponding management personnel when the user level of the reported issue reaches a preset level and the technical severity level meets the conditions for in-depth analysis. This tiered processing mechanism realizes automated closed-loop management from issue level assessment and root cause analysis triggering to differentiated push at the backend level.

[0105] This disclosure provides a problem-handling method that automatically determines the severity level of a problem based on three technical dimensions: impact degree, impact scope, and frequency of occurrence, by integrating user-submitted feedback data and objective user behavior logs. Then, it performs tiered processing based on the severity level. This solution avoids judgment bias caused by relying on a single data source and eliminates the need for manual assessment of the technical hazard of each problem. Automated backend analysis improves the objectivity and consistency of severity level assessment, achieving an intelligent closed loop from data collection to tiered handling, thus contributing to improved overall efficiency and accuracy of problem management.

[0106] In some embodiments, at least one problem is graded based on its technical severity level, including: For any one of the at least one problems, if the technical severity level of the problem reaches the condition for in-depth analysis, the technical root cause path of the problem is determined based on the application's module knowledge graph and user behavior data related to the problem. The module knowledge graph is used to indicate the relationship between the functional modules in the application, and the technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause.

[0107] The solution provided in this disclosure determines the root cause path of a technology when the severity level reaches the criteria for in-depth analysis, based on the application's module knowledge graph and user behavior data. The knowledge graph contains the structural relationships and technological dependencies between various functional modules, and cross-validation using user behavior data enables intelligent root cause localization. This solution overcomes the limitations of traditional static matching, helps improve the accuracy of root cause localization, reduces manual investigation time, provides precise direction for subsequent remediation, and improves the efficiency and quality of problem solving.

[0108] In some embodiments, at least one problem is graded based on its technical severity level, including: For any one of the at least one problems, based on the cause and technical severity level of the problem, identify historical solutions that match the problem from the solution library; By leveraging a large language model and adapting and rewriting historical solutions based on the application's business scenario, a solution to the problem can be obtained.

[0109] The solution provided in this disclosure matches historical solutions from a solution library based on the cause of the problem and its severity, and then adapts and rewrites them according to the current business scenario using a large language model to generate a customized solution. This solution combines experience reuse with intelligent generation, avoiding the need to design a repair solution from scratch each time. Scenario adaptation through the large language model helps ensure the solution's relevance and feasibility, reduces optimization decision-making costs, improves the efficiency and quality of solution generation, and accelerates the closed-loop problem-solving process.

[0110] In some embodiments, the method further includes: For any one of the at least one questions, multi-dimensional feature extraction is performed on the user feedback data and user behavior data related to the question to obtain multiple features of the question. The user behavior data is the operation log generated by the user during the use of the application. Based on multiple characteristics of the problem, the response level of the problem is determined, and the response level is used to indicate the urgency of dealing with the problem. Based on the response level of the issue, the issue is pushed to the terminal of the administrator corresponding to the functional module where the issue occurred.

[0111] The solution provided in this disclosure extracts multi-dimensional features from user feedback data and user behavior data to determine response levels, and then pushes notifications based on these response levels, achieving automated assessment and tiered notification of issue urgency. This solution integrates both subjective feedback and objective log data sources, making feature extraction more comprehensive and accurate, and avoiding the subjectivity and inconsistency of manual judgment. Tiered notifications based on response levels help to rationally allocate processing resources and improve the timeliness and targeted nature of issue responses.

[0112] In some embodiments, the multiple characteristics of each problem include at least two of the following: magnitude characteristics, pain characteristics, impact characteristics, and cost characteristics; The magnitude characteristic is used to indicate the frequency with which a problem is reported; Pain characteristics are used to indicate the degree of negative impact of a problem on the user experience; Impact characteristics are used to indicate the extent to which an issue affects business metrics or user reach. Cost characteristics are used to indicate the level of resources required to fix a problem.

[0113] The solution provided in this disclosure defines at least two of several characteristics, including magnitude, pain level, impact, and cost, and clarifies the definition of each characteristic. By quantifying the frequency of feedback, negative impact on user experience, degree of business impact, and resource investment in remediation, the solution makes problem assessment more comprehensive and scientific. This solution avoids the subjectivity of manual assessment, improves the multi-dimensionality and accuracy of problem classification, ensures that urgent and important problems can be accurately identified, and provides a reliable data foundation for subsequent classification and processing.

[0114] In some embodiments, the response level of a problem is determined based on multiple characteristics of the problem, including: Based on the weights of multiple features, the feature values ​​of multiple features are weighted and summed to obtain the score of the problem. The weight of each feature is used to indicate the degree of contribution of the feature to the urgency of the problem, and the score is used to indicate the overall urgency of the problem. The response level corresponding to the score falling within the threshold range is determined as the response level of the problem.

[0115] The solution provided in this disclosure assigns weights to each feature, performs a weighted summation of feature values ​​to obtain a score, and then maps the score to a threshold range to determine the response level. This solution quantifies multi-dimensional evaluation results into comparable numerical values, achieving standardized calculation of problem urgency. The weight of each feature indicates its contribution to the urgency level, making the evaluation model configurable and optimizable. This improves the accuracy and interpretability of problem classification, avoids inconsistencies in manual scoring, and reduces the misjudgment rate.

[0116] The above Figure 2 and Figure 3 The diagram shown is merely the basic process of this disclosure. The following section will further elaborate on the solution provided in this disclosure based on a specific implementation method. Figure 4 This is a flowchart illustrating another problem-solving method according to an exemplary embodiment. Taking an electronic device provided as a terminal and a server as an example, see [link to example]. Figure 4 The method includes the following steps: In step 401, the server determines at least one problem with the application based on user feedback data for the application, whereby the user feedback data consists of descriptive information submitted by users regarding their experience with the application.

[0117] In this embodiment, the server collects user-submitted descriptive text or speech-to-text content from multiple channels, including feedback forms, customer service conversations, surveys, and customer groups, through APIs, message subscriptions, or web crawlers. After cleaning, deduplicating, and parsing the collected data, the server aggregates multiple feedback responses with similar wording into a single specific question. For example, multiple feedback responses containing keywords such as "upload," "stuck," and "failed" are grouped into a "file upload function malfunction" question. This step transforms scattered subjective feedback into a structured list of questions, laying the foundation for subsequent quantitative evaluation.

[0118] After obtaining a preliminary list of issues, this disclosure does not rely directly on manual judgment or simple feedback counting, but further introduces multi-dimensional quantitative features to achieve a refined assessment of the issues. The specific implementation method in step 402 will be explained below.

[0119] In step 402, for any one of the at least one issues, the server performs multi-dimensional feature extraction on the user feedback data and user behavior data related to the issue to obtain multiple features of the issue. The user behavior data is the operation log generated by the user during the use of the application.

[0120] In this embodiment, the server combines the user feedback text associated with each issue with the corresponding user behavior log. The user behavior log records the user's operational trajectory in the application, including clicks, page jumps, dwell time, task completion status, and error reporting. The server extracts multi-dimensional features reflecting the nature of the issue from these two data sources. For example, it extracts high-frequency words and sentiment from the feedback text, and statistically analyzes the actual number of times the issue occurred, the number of affected users, and whether it blocked core processes from the behavior log. The resulting feature set is more comprehensive and accurate than that from a single data source.

[0121] To specifically quantify the urgency of a problem, this disclosure defines several computable evaluation dimensions. These are described in detail below with reference to specific embodiments. In some embodiments, the multiple characteristics of each problem include at least two of the following: magnitude characteristics, pain characteristics, impact characteristics, and cost characteristics. The definitions and quantification methods of these characteristics are described below.

[0122] The magnitude feature indicates the frequency of feedback on an issue. The server counts the number of similar feedback reports within a unit of time (e.g., a week or a month) and combines this with the frequency of mentions in user surveys and usability testing to automatically rate it as high / medium / low, corresponding to scores of 5 / 3 / 1. For example, if an issue is mentioned by 200 different users in the past week, it can be rated as "high" (5 points); if it is mentioned by only 5 users, it is rated as "low" (1 point).

[0123] Pain profiles are used to indicate the degree of negative impact of an issue on user experience. Based on the issue description and user behavior data, the server evaluates three sub-indicators: blocking (whether it disrupts the user's core task flow), annoyance (how frequently the issue occurs), and flexibility (whether the user has alternative operation paths). The overall rating is either fatal / moderate / recommended, corresponding to 5 / 3 / 1 points. For example, "media upload lag" completely blocks the media resource creation process, and there is no alternative solution; it can be rated "fatal" (5 points).

[0124] Impact characteristics are used to indicate the degree of impact of an issue on business metrics or user reach. The daily / monthly active users and page views of the module where the issue is located, along with the potential impact of the issue on core business metrics (such as conversion rate, retention rate, and critical task completion rate), are automatically rated as high / medium / low, corresponding to 5 / 3 / 1 points. For example, the creative management module affects 90% of ad creation operations and is frequently used by large clients, so it can be rated as "high" impact (5 points).

[0125] Cost characteristics are used to indicate the level of resources required to fix a problem. Based on the problem type and the modules involved, the server matches a pre-defined R&D cost database to estimate the combined resource investment in design, development, and testing, rating it as high / medium / low, corresponding to 5 / 3 / 1 points. For example, fixing an "upload lag" issue requires rebuilding the backend chain, estimated at 2 people / week, and is rated as "high" cost (5 points); if only the frontend copy needs to be modified, it is rated as "low" cost (1 point).

[0126] Taking "material upload lag" as an example, the server extracts the following features: Scale = 5 (high frequency), Pain Point = 5 (fatal blockage), Impact = 5 (covering major clients and core processes), and Cost = 5 (high investment). These four features will be saved and used in the next step of level calculation. This disclosure does not require the use of all four features; at least two can be selected based on the product stage and business needs.

[0127] In step 403, the server determines the response level of the problem based on multiple characteristics of the problem. The response level is used to indicate the urgency of handling the problem.

[0128] In this embodiment, the server fuses and calculates the multiple feature values ​​extracted in step 402 to output a standardized response level. This level can be a custom level such as "P0 (High) / P1 (Medium) / P2 (Low)". This level indicates the urgency of the problem, with different levels corresponding to different response time limits and processing procedures. A higher level indicates a higher priority for the problem. This step automates the quantification of problem urgency, avoiding the subjectivity of manual grading.

[0129] In some embodiments, to combine feature values ​​of different dimensions into a comprehensive score, this disclosure provides a specific algorithm for weighted summation and threshold mapping. Accordingly, step 403 includes: first, assigning a preset weight to each feature, which reflects the feature's contribution to the urgency of the problem. For example, for commercial B2B products, the weights for business impact and user pain are typically set higher, while the weight for optimization cost is relatively lower, to avoid underestimating the problem due to its high difficulty in fixing. After normalizing each feature value, the server performs a weighted summation of the feature values ​​based on the weights of multiple features to obtain the problem's score. This score indicates the overall urgency of the problem. The higher the score, the more urgent the problem. Then, the server determines the response level corresponding to the threshold range where the score falls as the problem's response level.

[0130] For example, the server presets a weight for each feature. In commercial B2B products, business impact and pain points often have higher weights (e.g., 0.3), followed by magnitude (0.25), and cost has the lowest (0.15). This weight can also be dynamically adjusted based on the business scenario. After normalizing each feature value (e.g., mapping 5 / 3 / 1 to the [1,0] interval or directly using the original score), a weighted sum is obtained to obtain the comprehensive score. For example, if the maximum score for each of the four dimensions is 5, and the weights are 0.3, 0.3, 0.3, and 0.1 respectively, then the score = 5 × 0.3 + 5 × 0.3 + 5 × 0.3 + 5 × 0.1 = 5.0. If the threshold range is set as follows: ≥4.0 points is P0, 2.5-3.9 points is P1, and <2.5 points is P2, then the problem is designated as P0.

[0131] In practical applications, the threshold range can be adjusted based on historical data and SLA requirements. For example, a total score ≥12 points (when not normalized and directly summed from raw scores) can be defined as P0, 8-11 points as P1, and ≤7 points as P2. Using the aforementioned four dimensions, each worth 5 points, the total score is 20 points, far exceeding 12 points, hence P0. Response level P0 requires a response within 24 hours, P1 requires within 3 business days, and P2 is included in regular iterations. This quantification method ensures the consistency and interpretability of the grading.

[0132] It should be noted that the above response levels primarily assess the urgency of the problem from a business perspective, focusing on "how quickly a response is needed." However, in real-world scenarios, there exists another type of problem—although the number of responses is small (i.e., low magnitude), it can cause serious technical failures, such as database deadlocks or core interface timeouts. These types of problems may be underestimated in business scoring due to their low magnitude, but their technical hazard is extremely high. Therefore, this disclosure further introduces a technical severity level as a supplementary and corrective mechanism to assess the severity of the problem from a technical perspective, and to handle it accordingly. See step 404 below for details.

[0133] In step 404, for any one of the at least one problems, the server determines the technical severity level of the problem based on at least one of the problem's impact degree feature, impact scope feature, and occurrence frequency feature. The impact degree feature is used to indicate the degree to which the problem blocks the user's core tasks, the impact scope feature is used to indicate the scale of users affected by the problem, and the occurrence frequency feature is used to indicate the number of times the problem is reproduced per unit time.

[0134] In this embodiment, the determination of the technical severity level is independent of the determination of the aforementioned response level. They assess the problem from different dimensions: the response level determines the response timeliness, while the technical severity level determines the diagnostic depth. The server extracts keywords related to core task blocking (such as "unable," "failed," "stuck," etc.) from user feedback text, and combines this with data such as task interruption rate and operation abandonment rate reflected in user behavior logs to generate impact degree feature values; it obtains the number of users affected by the problem and their hierarchical distribution from the user profile database to generate impact range feature values; and it statistically analyzes the number of error reports or user reproduction records of the problem per unit time from system logs to generate occurrence frequency feature values. The server then inputs the above feature values ​​into a preset evaluation model and automatically outputs the technical severity level of the problem, such as "fatal," "serious," "moderate," or "minor."

[0135] Based on the determination of the above-mentioned technical severity level, this disclosure also provides two specific backend processing strategies: First, when the technical severity level exceeds a second preset level (e.g., "severe" or "fatal") and the current response level has not reached the highest level (e.g., P0), the server automatically upgrades the response level of the problem (e.g., from P1 to P0) to ensure that problems with severe technical hazards receive a faster response channel; Second, when the technical severity level reaches the conditions for deep analysis, the server automatically triggers the root cause analysis process, locating the technical root cause path based on the module knowledge graph and graph neural network. These two strategies are described in detail below.

[0136] For example, a database deadlock issue, even if reported only once a month (low frequency), completely blocks all users' core operations (critical impact) and affects all users (wide scope of impact), can be classified as "critical" in terms of technical severity. If the technical severity level exceeds the second preset level (e.g., classified as "severe" or "critical") and the current response level has not reached the highest level (e.g., not P0), the response level is automatically upgraded; the issue is then processed based on the upgraded response level. For example, upgrading from P1 to P0. The upgraded level will override the original level and be used for subsequent processing. In this way, even if the issue occurs infrequently, as long as its technical hazard is significant enough, it can receive priority processing.

[0137] In some embodiments, for any of the at least one issues, if the technical severity level of the issue reaches the criteria for deep analysis, the server can also determine the technical root cause path of the issue based on the application's module knowledge graph and user behavior data related to the issue. The module knowledge graph pre-constructs a mapping library of structural relationships, technical dependencies, configuration parameter associations, and historical issues and causes between various functional modules in the application. For example, the "Material Upload" node in the graph is associated with sub-nodes such as "Network Request Layer," "File Sharding Component," "Server API," and "Bandwidth Configuration," and records historical root causes such as "Large file timeout due to missing sharding mechanism." The server obtains the issue description (e.g., "Upload lag") and user behavior logs (e.g., upload progress timeout after 98%), and uses a graph neural network (GNN) to traverse the knowledge graph to match possible technical root cause paths. Cross-validation is performed using the behavior logs: for example, if multiple users' uploads fail at the same percentage, it is identified as a "sharding merging logic problem"; if a large number of users fail simultaneously, it is identified as "insufficient server bandwidth." The final output is the root cause path, such as "Upload delays → HTTP request timeouts → Large files not fragmented → Missing resume mechanism". This root cause path is used for subsequent solution recommendations.

[0138] Furthermore, based on obtaining the root cause path of the technology, this disclosure also provides intelligent solution generation capabilities. In some embodiments, for any of the at least one problem, the server can also determine historical solutions matching the problem from a solution library based on the cause and severity level of the problem. Then, the server uses a large language model to adapt and rewrite the historical solutions according to the business scenario of the application to obtain a solution to the problem. The solution library contains repair solutions for various types of problems handled in the past, including technical repair solutions, product process optimization solutions, user guidance solutions, etc., and each solution is given a structured tag (problem type, module, impact level, implementation cost, expected effect). The server retrieves the historical solution with the highest similarity from the library based on the root cause and severity level of the current problem. Then, it calls the large language model and, combined with the specific business scenario of the current application (e.g., "This product is an advertising platform for small and medium-sized enterprises, users mainly operate through mobile devices, and the network environment is unstable"), adapts and rewrites the historical solution accordingly. For example, the previous solution suggested "implementing chunked uploads and resuming interrupted uploads." The large language model rewrote this as "It is recommended to integrate the XX open-source chunked upload component into the media management module, setting the chunk size to 256KB (to adapt to weak network conditions on mobile devices), adding local caching of upload progress, supporting resuming interrupted uploads, and providing a retry button for failures. This is expected to reduce the upload failure rate by 90% and improve the experience for large clients." The generated solution includes the implementation path, estimated cost, and expected results, and is directly pushed to the R&D manager.

[0139] In step 405, the server sends the detected problems, their response levels, and technical severity levels to the terminal.

[0140] In this embodiment, the server packages each problem obtained through the above steps, along with its corresponding response level and technical severity level (and optional root cause path, solution, etc.), and sends it to the terminal device via a wireless or wired network. After receiving this data, the terminal performs interface rendering and user interaction. The terminal-side processing flow is described below.

[0141] After receiving data pushed by the server, the terminal needs to present this data to the operator in an intuitive and efficient manner. To this end, this disclosure provides a series of visual interfaces and interactive methods on the terminal side, including problem list display, category filtering, statistical information overview, details viewing, and tiered processing operations. These will be explained in detail below.

[0142] In step 406, the terminal displays at least one problem existing in the application, as well as the response level and technical severity level of each problem, in the application's problem detection interface.

[0143] In this embodiment of the disclosure, after receiving data pushed by the server, the terminal displays the title, brief description, and response level label (such as P0, P1, P2 or "urgent", "moderate", "low") and technical severity level label (such as "fatal", "serious", "moderate", "minor") of each problem in the problem detection interface in the form of a list, card, or table. Operators can intuitively see the urgency and technical severity of all problems to be processed, which facilitates a comprehensive judgment on the processing priority.

[0144] To facilitate quick filtering and location by operators, this disclosure provides multiple classification display methods. In some embodiments, when an application has multiple issues, the terminal can classify and display the multiple issues in the application's issue detection interface according to the response level of the issues. For example, all P0 issues can be collapsed into one group, and P1 issues into another group. Alternatively, the terminal can also classify and display the multiple issues in the application's issue detection interface according to the functional modules involved in the issues. For example, issues from the "Material Management" module can be displayed together, while issues from the "Data Reports" module can be placed in a separate group. Users can freely switch between classification methods via interface tabs. Alternatively, the terminal can also classify and display the multiple issues in the application's issue detection interface according to the technical severity level of the issues. For example, "Critical" level issues can be displayed at the top, "Severe" level issues next, and "General" and "Minor" level issues collapsed or placed at the bottom of the list, allowing operators to quickly focus on the issues with the most serious technical hazards.

[0145] For example, Figure 5 This is a schematic diagram illustrating a classification display problem according to an exemplary embodiment. See also Figure 5 The terminal displays the problem in the corresponding functional module of the application; as the user switches the tab of any functional module, the terminal switches to displaying the relevant problem. Figure 5 As you can see, the problems currently displayed are those in the "Standard Creation" module. Due to the limitations of the interface size, only a portion of the problems are shown at present.

[0146] To provide overall situational awareness, this disclosure also integrates several statistical information items into the problem detection interface. In some embodiments, the terminal displays at least one of the following: the total number of at least one problem; the total number of functional modules involved in at least one problem within the application; the number of problems at each technical severity level within at least one problem; the proportion of problems at each technical severity level in at least one problem; the number of problems at each response level within at least one problem; the proportion of problems at each response level in at least one problem; the number of problems in each functional module involved in at least one problem; and the proportion of problems in each functional module involved in at least one problem within at least one problem. This information may be presented in the form of number cards, bar charts, or pie charts.

[0147] For example, Figure 6 This is a schematic diagram illustrating problem statistics according to an exemplary embodiment. See also... Figure 6 A total of 195 issues were detected in the application, mainly appearing in four functional modules. These included 95 P0-level issues, 24 P1-level issues, and 76 P2-level issues. These issues are presented in a pie chart format.

[0148] To more intuitively display the comparison of the number of problems at different levels, the terminal can display the number of problems at each response level within at least one problem in the form of a pie chart on the problem detection interface. Alternatively, the terminal can also display the number of problems at each response level within at least one problem in different regions on the problem detection interface, with the number of problems at each response level being positively correlated with the area of ​​the corresponding region.

[0149] For example, see continue. Figure 6 For issues identified by users in the user feedback data (i.e., issues identified only from user feedback data), the terminal displays the issues appearing in each functional module in different areas. Among these, 34 issues were mentioned and 57 issues were raised in the user feedback data. Functional module A had the most issues, accounting for 20.7% of the total, and correspondingly, its area was the largest.

[0150] In some embodiments, while displaying the list of issues, this disclosure also supports displaying the follow-up recipient for each issue to clarify the division of responsibilities. Accordingly, for any one of at least one issues, the terminal displays the follow-up recipient for the issue, which includes at least one of the managers responsible for resolving the issue and the users who provided feedback on the issue. For example, the card might be labeled "Follow-up Person: Zhang San (R&D)" and "Feedback Person: Li Si (Major Account)". If no assignment is made, a quick assignment entry point (such as adding a control to the object) can be provided.

[0151] The follow-up targets for the aforementioned issues can be determined automatically or defined by the user, and this disclosure does not limit this. Optionally, for any of the at least one issues, the terminal displays an object addition control for the issue; in response to the triggering operation of adding a control to the object, an object list is displayed; in response to the selection operation of at least one object in the object list, at least one object is displayed as the follow-up target for the issue.

[0152] For example, Figure 7 This is a schematic diagram illustrating another classification of problems according to an exemplary embodiment. See also Figure 7 The terminal displays various issues in the application categorized by response level. Each issue corresponds to an object addition control, which allows operators to add follow-up objects.

[0153] For high-value users with urgent issues, this disclosure provides highlighting and rapid communication. In some embodiments, the terminal highlights a target issue from at least one issue in the issue detection interface. The user reporting the target issue is at a preset level (e.g., "major customer"), and the technical severity of the target issue is at a second preset level (e.g., "serious"). The highlighting method can be background highlighting, border flashing, or a special icon; this disclosure does not limit the specific methods used.

[0154] In some embodiments, in response to a triggering operation on a target issue, the terminal displays a session service interface for the user who provided feedback on the target issue. This session service interface is used to communicate with the user regarding the target issue. The session service interface may embed an instant chat window, email editor, or telephone dial pad, and automatically populate a problem summary and preset communication scripts (e.g., "We have noticed your feedback..."). The technical team is working urgently to fix the issue and expects to resolve it within 2 hours. This helps to reassure high-value customers and reduce the risk of churn.

[0155] To allow operators to directly read the user's original feedback and avoid information loss, in some embodiments, the terminal displays user feedback data (original user feedback) for the application in the problem detection interface. This user feedback data consists of descriptive information submitted by the user regarding their application experience. For example, the original feedback text is displayed collapsed below the problem card, with the source channel (e.g., "from customer group 2025-01-15") and user identifier indicated. Operators can unfold and read this information to obtain a more complete context, preventing information distortion during multiple processing layers.

[0156] In step 407, in response to viewing any problem, the terminal displays a problem details panel in the problem detection interface. The details panel displays at least one of the following: multiple characteristics of the problem, the criteria for determining the response level of the problem, the cause of the problem, and the solution to the problem.

[0157] In this embodiment, when an operator clicks on a problem card or selects "View Details," the terminal switches from the current list view or pops up a details panel to centrally display in-depth information about the problem. The details panel displays at least one of the following: multiple characteristics of the problem (specific scores for each dimension), the criteria for determining the response level (e.g., "Scale 5 points + Pain 5 points + Impact 5 points + Cost 3 points, weighted total score 16 points, exceeding the 12-point threshold, therefore classified as P0"), the cause of the problem (a textual description of the technical root cause path), and the solution to the problem (customized suggestions generated by LLM). This allows operators to obtain complete decision support materials without switching to other systems.

[0158] For example, Figure 8 This is a schematic diagram illustrating a details panel according to an exemplary embodiment. See also Figure 8 The details panel displays information such as the description of the problem, user feedback data, conclusions, and solutions.

[0159] In some embodiments, for issues that meet the criteria for in-depth analysis, the details panel will also highlight the technical root cause path. The technical root cause path indicates the reasoning chain from the problem phenomenon to the underlying technical cause. The technical root cause path includes multiple nodes, each indicating a technical aspect of the problem. For example, "Phenomenon: Material upload lag" → "Node 1: HTTP request timeout" → "Node 2: Large file merging failure" → "Root cause node: Missing chunked upload mechanism". Nodes are connected by arrows, allowing users to clearly see the causal chain.

[0160] To help technical personnel gain a deeper understanding of each technical aspect, this disclosure also supports expanding and viewing root cause path nodes. In some embodiments, in response to a triggered operation (such as clicking or hovering the mouse) on any node in the technical root cause path, the terminal displays the node's description information. This description information describes the node's technical principles, related code locations, or configuration parameters, assisting technical personnel in in-depth troubleshooting. For example, clicking the "Missing chunked upload mechanism" node displays: "Chunked upload refers to splitting large files into multiple smaller chunks and uploading them in parallel to avoid timeouts for single requests. This mechanism is not implemented in the current system. It is recommended to refer to the RFC 7233 standard to implement HTTP range requests. Relevant code location: / uploader.js line 120." This significantly lowers the knowledge threshold for technical personnel to troubleshoot problems.

[0161] Furthermore, to enhance timeliness management of the most urgent issues, this disclosure also provides a countdown reminder function. In some embodiments, the terminal can also display a countdown control for issues that have reached the highest response level in the issue detection interface. The countdown control is used to indicate the remaining time (e.g., a 24-hour countdown) before the deadline for starting issue processing. The countdown flashes red as it approaches the end, reminding the responsible person to take immediate action. If the issue is not processed within the time limit, a further escalation notification can be issued.

[0162] Meanwhile, the user's emotional state is also a significant factor influencing the processing strategy. In some embodiments, for any of the at least one questions, the terminal can also display an emotion label indicating the intensity of the user's negative emotion (such as "anger," "dissatisfaction," or "neutral"). For example, different emotion intensities correspond to different colors or styles of emotion icons, with high-intensity negative emotions corresponding to a red warning icon, medium-intensity to an orange neutral icon, and low-intensity to a gray icon. Operators can then prioritize handling questions with strong emotional intensity based on this information.

[0163] In step 408, the terminal classifies at least one problem based on at least one of the technical severity level and response level of the problem.

[0164] In this embodiment, the terminal not only passively displays information but also proactively performs differentiated processing actions based on the response level. These actions include automatic sorting, push notifications, and marking follow-up personnel to ensure that urgent issues receive a rapid response.

[0165] A common tiered approach is to determine the timing and recipients of push notifications based on the response level. In some embodiments, for any of the at least one issues, the terminal displays the target functional module in the application where the problem occurs in the issue detection interface; within the time frame corresponding to the response level (e.g., P0 requires a response within 24 hours, P1 requires a response within 3 days), the issue is pushed to the terminal of the administrator corresponding to the target functional module, where a higher response level corresponds to a shorter time frame. The push method can be SMS, email, or in-system notification. A higher response level and a shorter time frame ensure that urgent issues are addressed more quickly. For example, a P0 issue might be pushed with an "urgent" label and sent immediately; a P2 issue might be summarized and pushed weekly.

[0166] In some embodiments, to further protect high-value users, this disclosure introduces a user-tier-based priority push mechanism. Accordingly, for any one of the at least one issues, the terminal displays the user's tier in the issue detection interface. If the tier reaches a preset tier (e.g., VIP) and the issue's response level reaches a first preset level (e.g., P0 or P1), the terminal sets the push priority of this issue to the highest level, prioritizing the push to the terminal of the administrator corresponding to the target functional module, ensuring that the administrator can handle it immediately. The terminal can set the push priority of this issue to the highest level, placing it before other ordinary issues in the push queue, and can even bypass the conventional polling mechanism, directly sending a strong reminder (e.g., voice call + SMS) to the responsible person's terminal. This ensures that high-urgent issues of high-value users can be handled immediately, reducing the risk of losing major customers.

[0167] In addition to the response level-based push mechanism, this disclosure also provides a graded processing strategy based on the severity level of the technical issue, focusing on prioritizing the push of issues with severe technical hazards for root cause analysis. In some embodiments, the terminal pushes at least one issue sequentially to the terminals of the administrators corresponding to the functional modules where the issues occur, in descending order of technical severity level. Alternatively, for any of the at least one issues, if the user who reported the issue reaches a preset level and the technical severity level meets the criteria for in-depth analysis, the terminal prioritizes pushing the issue and its technical root cause path to the terminals of the administrators corresponding to the functional modules where the issues occur. Through the cooperation of these two graded processing mechanisms—the response level determines "how quickly to push," and the technical severity level determines "what content to push" and "whether to include root cause analysis results"—this disclosure achieves a complete automated closed loop from issue identification, level assessment, root cause location to differentiated push, ensuring that limited human resources are prioritized for the most urgent and technically hazardous issues.

[0168] Finally, for review and improvement, this disclosure provides a visual traceability of processing progress. In some embodiments, for any of the at least one issues, the terminal can also display the processing progress of the issue in a timeline format on the issue detection interface. The timeline is marked with at least one of the following: response time, start processing time, fix completion time, and satisfaction verification time. The response time is the time when the person responsible for resolving the issue receives the issue (e.g., the moment they click the "Accept" button). The start processing time is the moment when the administrator changes the issue status from "Pending" to "Processing". The fix completion time is the moment when the issue is fixed and uploaded (or marked as resolved). The satisfaction verification time is the moment when the system sends a survey questionnaire to the user who provided feedback and receives a response. The time elapsed can be displayed next to each time point (e.g., "Response time 0.5 hours"). Such a timeline helps the team identify bottlenecks in the processing process and continuously optimize Service Level Agreement (SLA) compliance rates.

[0169] This disclosure provides a problem-handling method. On the server side, by integrating user feedback data and user behavior logs, the method extracts quantitative features of problems from multiple dimensions such as scale, pain level, impact, and cost. It then automatically determines the response level by combining weighted summation and threshold mapping, avoiding the subjectivity and inconsistency of manual grading in traditional solutions and improving the accuracy and efficiency of problem urgency assessment. Simultaneously, it introduces a technical severity level based on impact degree, impact scope, and frequency of occurrence as a correction mechanism. This automatically raises the response level when business scores underestimate low-frequency, high-risk problems, further enhancing the comprehensiveness and reliability of the grading. Furthermore, it utilizes knowledge graphs and graph neural networks to automatically locate root cause paths and rewrites historical solutions using a large language model for scenario-based adaptation, significantly reducing the time cost of manually investigating root causes and designing remediation plans, thus improving the speed and quality of problem resolution. On the terminal side, the problem detection interface intuitively displays the response level, classification display method, pie charts, and other visual elements of each problem, enabling operators to quickly distinguish the urgency and distribution of problems, reducing information filtering time. The details panel centrally displays multi-dimensional characteristics, grading criteria, root cause paths, and solutions, supporting node expansion to view technical principles and reducing communication and decision-making costs. Features such as countdown controls, sentiment tags, and highlighting of high-value user issues further enhance the timeliness management and risk warning capabilities for key issues. Ultimately, the tiered processing based on response levels includes targeted push notifications by time range, priority push notifications for high-value users, and timeline progress tracking, forming a complete closed-loop chain from issue identification to resolution.

[0170] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0171] Figure 9This is a block diagram illustrating a problem-solving apparatus according to an exemplary embodiment. See also: Figure 9 The problem-solving device includes a display unit 901 and a processing unit 902.

[0172] Display unit 901 is configured to display at least one problem in the application's problem detection interface, wherein the at least one problem is determined based on user feedback data regarding the application. The display unit 901 is also configured to perform at least one of the problem's impact severity feature, impact range feature, and occurrence frequency feature to display the technical severity level of the problem. The impact severity feature is used to indicate the degree to which the problem blocks the user's core tasks. The impact range feature is used to indicate the scale of users affected by the problem. The occurrence frequency feature is used to indicate the number of times the problem is reproduced per unit time. Processing unit 902 is configured to perform graded processing of at least one problem based on the technical severity level of at least one problem.

[0173] In some embodiments, the application has several problems; Display unit 901 is configured to perform any of the following: Based on the technical severity level of the problem, the application's problem detection interface displays multiple problems categorized by the application's severity level. Based on the response level of the problem, the application's problem detection interface displays multiple problems in categories, with the response level indicating the urgency of the problem. Based on the functional modules involved in the application, the application's problem detection interface displays multiple problems categorized by function.

[0174] In some embodiments, the processing unit 902 is configured to perform the following: for any one of the at least one problems, if the technical severity level reaches the deep analysis condition, display the technical root cause path of the problem. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause. The technical root cause path includes multiple nodes, each node indicating a technical aspect involved in the problem.

[0175] In some embodiments, the display unit 901 is further configured to perform a triggering operation on any node in the technical root cause path, displaying the node's description information, which describes the node's technical principles.

[0176] In some embodiments, the processing unit 902 is configured to perform graded processing of at least one problem based on the technical severity level of at least one problem, including at least one of the following: In descending order of technical severity, at least one issue will be pushed sequentially to the terminal of the administrator corresponding to the functional module where the issue occurred. For any one of the at least one issues, if the user who reported the issue reaches a preset level and the technical severity level meets the conditions for in-depth analysis, the issue and its technical root cause path will be pushed to the terminal of the administrator corresponding to the functional module where the issue occurred. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the root cause at the technical level.

[0177] In some embodiments, the display unit 901 is further configured to display the response level of the problem in the problem detection interface for any one of the at least one problems, based on multiple features of the problem. The multiple features are obtained by multi-dimensional feature extraction of user feedback data and user behavior data related to the problem. The multiple features are used to reflect the attributes of the problem from multiple different dimensions. The user behavior data is the operation log generated by the user during the use of the application. The response level is used to indicate the urgency of handling the problem.

[0178] In some embodiments, the processing unit 902 is further configured to perform the following actions for any one of the at least one problems: displaying the target functional module in the problem detection interface where the problem occurs in the application; and pushing the problem to the terminal of the administrator corresponding to the target functional module within the time range corresponding to the response level, wherein the higher the response level, the shorter the corresponding time range.

[0179] In some embodiments, the processing unit 902 is further configured to, for any one of the at least one problems, display the user's level of feedback on the problem in the problem detection interface; and, if the level reaches a preset level and the response level of the problem reaches a first preset level, prioritize pushing the problem to the terminal of the administrator corresponding to the target functional module.

[0180] In some embodiments, the display unit 901 is further configured to display the follow-up object for any one of at least one issues, the follow-up object including at least one of the managers responsible for resolving the issue and the user who reported the issue.

[0181] In some embodiments, the display unit 901 is further configured to highlight a target problem among at least one problem in the problem detection interface; Among them, the user level that provides feedback on the target problem reaches the preset level, and the technical severity level of the target problem reaches the second preset level.

[0182] In some embodiments, the display unit 901 is further configured to perform a triggering operation in response to a target problem, displaying and providing feedback on a user's session service interface for communicating with the user about the target problem.

[0183] In some embodiments, the display unit 901 is further configured to perform at least one of the following: The problem detection interface displays the total number of at least one problem; The problem detection interface displays the total number of functional modules involved in at least one problem within the application; The problem detection interface displays the number of problems at each technical severity level within at least one problem; The problem detection interface displays the proportion of each technical severity level of a problem in at least one problem. The problem detection interface displays the number of problems at each response level for at least one problem; The problem detection interface displays the proportion of each response level in at least one problem. The problem detection interface displays the number of problems in each functional module involved in at least one problem; The problem detection interface displays the proportion of problems in each functional module involved in at least one problem within that problem.

[0184] In some embodiments, the display unit 901 is further configured to perform any of the following: The problem detection interface displays the number of problems at each response level in at least one problem in the form of a pie chart; In the problem detection interface, the number of problems at each response level is displayed in separate areas. The number of problems at each response level is positively correlated with the area of ​​the corresponding region.

[0185] In some embodiments, the display unit 901 is further configured to perform a viewing operation in response to any problem, displaying a details panel of the problem in the problem detection interface. The details panel displays at least one of the following: multiple characteristics of the problem, the criteria for determining the technical severity level of the problem, the cause of the problem, and the solution to the problem.

[0186] In some embodiments, the display unit 901 is further configured to, in the problem detection interface, display a countdown control for a problem whose response level has reached the highest level. The countdown control is used to indicate the remaining time until the deadline for starting problem processing, and the response level is used to indicate the urgency of processing the problem.

[0187] In some embodiments, the display unit 901 is further configured to display a sentiment label for any one of at least one questions, the sentiment label being used to indicate the intensity of negative emotions of the user who submitted the question.

[0188] In some embodiments, the display unit 901 is further configured to display the processing progress of the problem in the problem detection interface in the form of a timeline for any one of the at least one problems. The timeline is marked with at least one of the following: response time, start processing time, repair completion time, and satisfaction verification time. The response time is the time when the object responsible for solving the problem receives the problem.

[0189] In some embodiments, the display unit 901 is further configured to display user feedback data for the application in the problem detection interface, wherein the user feedback data is descriptive information submitted by the user about the application experience.

[0190] This disclosure provides a problem handling device that directly displays the severity level of a problem, determined by three technical dimensions: impact degree, impact scope, and frequency of occurrence, on the problem detection interface of an application. This allows operators to intuitively understand the severity of each problem without having to review feedback details one by one, overcoming the shortcomings of traditional problem lists that are cluttered and difficult to quickly locate key issues. Furthermore, by classifying and processing problems according to their severity levels, the device achieves automated linkage between interface display and processing decisions, avoiding delays in handling severe problems due to visual interference or human judgment bias. This method improves the information transmission efficiency of the problem detection interface, reduces the risk of missing severe problems, and enhances the interactive experience of problem management on the terminal side.

[0191] Figure 10 This is a block diagram illustrating another problem-solving apparatus according to an exemplary embodiment. See also Figure 10 The problem-solving device includes: a first determining module 1001, a feature extraction module 1002, a second determining module 1003, and a processing module 1004.

[0192] The first determining unit 1001 is configured to determine at least one problem in the application based on user feedback data for the application, wherein the user feedback data is descriptive information submitted by the user about the application experience. The second determining unit 1002 is configured to perform an action based on at least one of the following: the degree of impact characteristic, the scope of impact characteristic, and the frequency of occurrence characteristic, to determine the technical severity level of the problem. The degree of impact characteristic is used to indicate the degree of obstruction of the problem to the user's core tasks. The scope of impact characteristic is used to indicate the scale of users affected by the problem. The frequency of occurrence characteristic is used to indicate the number of times the problem is reproduced per unit time. The processing unit 1003 is configured to perform graded processing on at least one problem based on the technical severity level of at least one problem.

[0193] In some embodiments, the processing unit 1003 is configured to perform the following: for any one of the at least one problems, if the technical severity level of the problem reaches the condition for deep analysis, determine the technical root cause path of the problem based on the module knowledge graph of the application and user behavior data related to the problem. The module knowledge graph is used to indicate the relationship between various functional modules in the application, and the technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause.

[0194] In some embodiments, the processing unit 1003 is configured to perform the following actions for any one of at least one problems: determining a historical solution matching the problem from a solution library based on the cause and technical severity of the problem; and using a large language model to adapt and rewrite the historical solution according to the business scenario of the application to obtain a solution to the problem.

[0195] In some embodiments, the apparatus further includes: The feature extraction unit is configured to perform multi-dimensional feature extraction on user feedback data and user behavior data related to the problem for any one of the at least one problems, to obtain multiple features of the problem. The user behavior data is the operation log generated by the user during the use of the application. The third determining unit is configured to perform multiple characteristics based on the problem to determine the response level of the problem, which is used to indicate the urgency of handling the problem. The processing unit 1003 is also configured to execute a problem-based response level and push the problem to the terminal of the administrator corresponding to the functional module where the problem occurred.

[0196] In some embodiments, the multiple characteristics of each problem include at least two of the following: magnitude characteristics, pain characteristics, impact characteristics, and cost characteristics; The magnitude characteristic is used to indicate the frequency with which a problem is reported; Pain characteristics are used to indicate the degree of negative impact of a problem on the user experience; Impact characteristics are used to indicate the extent to which an issue affects business metrics or user reach. Cost characteristics are used to indicate the level of resources required to fix a problem.

[0197] In some embodiments, the third determining unit is configured to perform a weighted summation of the feature values ​​of multiple features based on multiple features to obtain a score for the problem. The weight of each feature is used to indicate the degree of contribution of the feature to the urgency of the problem, and the score is used to indicate the overall urgency of the problem. The response level corresponding to the threshold range in which the score falls is determined as the response level of the problem.

[0198] This disclosure provides a problem-handling device that automatically determines the severity level of a problem based on three technical dimensions: impact degree, impact scope, and frequency of occurrence, by integrating user-submitted feedback data and objective user behavior logs. The device then performs tiered processing based on the severity level. This approach avoids judgment biases caused by relying on a single data source and eliminates the need for manual assessment of the technical hazard of each problem. Automated background analysis improves the objectivity and consistency of severity level assessment, achieving an intelligent closed loop from data collection to tiered handling, thus enhancing the overall efficiency and accuracy of problem management.

[0199] It should be noted that the problem-handling device provided in the above embodiments is only illustrated by the division of the above functional units when detecting problems in applications. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the electronic device can be divided into different functional units to complete all or part of the functions described above. In addition, the problem-handling device and the problem-handling method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0200] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0201] When an electronic device is provided as a terminal, Figure 11 This is a block diagram illustrating a terminal 1100 according to an exemplary embodiment. The terminal... Figure 11 A structural block diagram of a terminal 1100 provided in an exemplary embodiment of this disclosure is shown. The terminal 1100 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1100 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0202] Typically, terminal 1100 includes a processor 1101 and a memory 1102.

[0203] Processor 1101 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1101 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1101 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1101 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1101 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0204] The memory 1102 may include one or more computer-readable storage media, which may be non-transitory. The memory 1102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1102 are used to store at least one computer program, which is executed by the processor 1101 to implement the problem-solving method provided in the method embodiments of this application.

[0205] In some embodiments, the terminal 1100 may also optionally include a peripheral device interface 1103 and at least one peripheral device. The processor 1101, memory 1102, and peripheral device interface 1103 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1103 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1104, a display screen 1105, a camera assembly 1106, an audio circuit 1107, and a power supply 1108.

[0206] Peripheral device interface 1103 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1101 and memory 1102. In some embodiments, processor 1101, memory 1102 and peripheral device interface 1103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1101, memory 1102 and peripheral device interface 1103 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0207] The radio frequency (RF) circuit 1104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1104 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1104 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. In some embodiments, the RF circuit 1104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1104 can communicate with other terminals via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1104 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0208] Display screen 1105 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1105 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1101 for processing. In this case, display screen 1105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1105, disposed on the front panel of terminal 1100; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 1100 or in a folded design; in still other embodiments, display screen 1105 may be a flexible display screen, disposed on a curved or folded surface of terminal 1100. Furthermore, display screen 1105 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1105 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0209] The camera assembly 1106 is used to acquire images or videos. In some embodiments, the camera assembly 1106 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1106 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash is a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0210] The audio circuit 1107 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1101 for processing, or input to the radio frequency circuit 1104 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 1100. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1101 or the radio frequency circuit 1104 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1107 may also include a headphone jack.

[0211] Power supply 1108 is used to power the various components in terminal 1100. Power supply 1108 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1108 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0212] Those skilled in the art will understand that Figure 11 The structure shown does not constitute a limitation on terminal 1100 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0213] When electronic devices are provided as servers, Figure 12 This is a block diagram illustrating a server 1200 according to an exemplary embodiment. The server 1200 can vary significantly due to differences in configuration or performance. It may include one or more Central Processing Units (CPUs) 1201 and one or more memories 1202. The memories 1202 store at least one line of program code, which is loaded and executed by the processor 1201 to implement the problem-solving methods provided in the various method embodiments described above. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input / output. The server 1200 may also include other components for implementing device functions, which will not be elaborated upon here.

[0214] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as memory 1102 or memory 1202 including instructions, which can be executed by processor 1101 of terminal 1100 or processor 1201 of server 1200 to complete the above-described problem-solving method. Optionally, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0215] A computer program product includes a computer program / instructions that, when executed by a processor, implement the aforementioned problem-solving method.

[0216] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0217] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A problem-solving method, characterized in that, The method includes: The application's problem detection interface displays at least one problem existing in the application, which is determined based on user feedback data regarding the application; For any of the at least one problem, the technical severity level of the problem is displayed based on at least one of the problem's impact degree feature, impact range feature, and occurrence frequency feature. The impact degree feature is used to indicate the degree to which the problem blocks users' core tasks. The impact range feature is used to indicate the scale of users affected by the problem. The occurrence frequency feature is used to indicate the number of times the problem is reproduced per unit time. Based on the technical severity level of the at least one problem, the at least one problem is classified and processed accordingly.

2. The problem-solving method according to claim 1, characterized in that, The application has several problems; The problem detection interface of the application displays at least one problem existing in the application, including any one of the following: Based on the technical severity level of the problem, the problem detection interface of the application displays multiple problems existing in the application in a categorized manner; According to the response level of the problem, the problem detection interface of the application displays multiple problems existing in the application in categories, and the response level is used to indicate the urgency of handling the problem. Based on the functional modules involved in the problem within the application, the multiple problems present in the application are categorized and displayed in the application's problem detection interface.

3. The problem-solving method according to claim 1, characterized in that, The classification of the at least one problem based on its technical severity level includes: For any of the at least one problems, if the technical severity level reaches the condition for in-depth analysis, the technical root cause path of the problem is displayed. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause. The technical root cause path includes multiple nodes, each node indicating a technical aspect involved in the problem.

4. The problem-solving method according to claim 3, characterized in that, The method further includes: In response to a trigger operation on any node in the technical root cause path, the description information of the node is displayed, which describes the technical principle of the node.

5. The problem-solving method according to claim 1, characterized in that, The classification of the at least one problem based on its technical severity level includes at least one of the following: In descending order of technical severity, the at least one problem is sequentially pushed to the terminals of the administrators corresponding to the functional modules in which the problems occur. For any of the at least one problem, if the user who reported the problem reaches a preset level and the technical severity level reaches the condition for in-depth analysis, the problem and the technical root cause path of the problem are pushed to the terminal of the administrator corresponding to the functional module where the problem occurred. The technical root cause path is used to indicate the reasoning link from the problem phenomenon to the technical root cause.

6. The problem-solving method according to claim 1, characterized in that, The method further includes: For any one of the at least one problems, the response level of the problem is displayed in the problem detection interface based on multiple features of the problem. The multiple features are obtained by multi-dimensional feature extraction of user feedback data and user behavior data related to the problem. The multiple features are used to reflect the attributes of the problem from multiple different dimensions. The user behavior data is the operation log generated by the user during the use of the application. The response level is used to indicate the urgency of handling the problem.

7. The problem-solving method according to claim 6, characterized in that, The method further includes: For any one of the at least one problems, the target functional module in the application where the problem occurs is displayed in the problem detection interface; Within the time range corresponding to the response level, the issue is pushed to the terminal of the administrator corresponding to the target functional module, wherein the higher the response level, the shorter the corresponding time range.

8. The problem-solving method according to claim 6, characterized in that, The method further includes: For any one of the at least one problems, the problem detection interface displays the hierarchy of the user who reported the problem; When the level reaches a preset level and the response level of the problem reaches a first preset level, the problem is preferentially pushed to the terminal of the administrator corresponding to the target functional module where the problem occurred.

9. The problem-solving method according to claim 1, characterized in that, The method further includes: For any one of the at least one issues, display the follow-up target for that issue, which includes at least one of the administrators responsible for resolving the issue and the users who provided feedback on the issue.

10. The problem-solving method according to claim 1, characterized in that, The method further includes: In the problem detection interface, the target problem among the at least one problem is highlighted; Among them, the user level that provides feedback on the target problem reaches a preset level, and the technical severity level of the target problem reaches a second preset level.

11. The problem-solving method according to claim 10, characterized in that, The method further includes: In response to a triggering operation on the target issue, a session service interface for the user of the target issue is displayed and provided, the session service interface being used to communicate the status of the target issue with the user.

12. The problem-solving method according to claim 1, characterized in that, The method further includes at least one of the following: The problem detection interface displays the total number of the at least one problem; The problem detection interface displays the total number of functional modules involved in the application for the at least one problem; The problem detection interface displays the number of problems of each technical severity level among the at least one problem; The problem detection interface displays the proportion of each technical severity level of the at least one problem in the at least one problem; The problem detection interface displays the number of problems at each response level among the at least one problem; The problem detection interface displays the proportion of each response level of the at least one problem in the at least one problem; The problem detection interface displays the number of problems in each functional module involved in the at least one problem; The problem detection interface displays the proportion of problems in each functional module involved in the at least one problem in the at least one problem.

13. The problem-solving method according to claim 12, characterized in that, The problem detection interface displays the number of problems at each response level for the at least one problem, including any of the following: In the problem detection interface, the number of problems at each response level in the at least one problem is displayed in the form of a pie chart; In the problem detection interface, the number of problems of each response level in the at least one problem is displayed in separate areas, and the number of problems of each response level is positively correlated with the area of ​​the corresponding area.

14. The problem-solving method according to claim 1, characterized in that, The method further includes: In response to viewing any problem, a details panel for the problem is displayed on the problem detection interface. The details panel displays at least one of the following: the characteristics of the problem, the criteria for determining the technical severity level of the problem, the cause of the problem, and the solution to the problem.

15. The problem-solving method according to claim 1, characterized in that, The method further includes: In the problem detection interface, for problems with the highest response level, a countdown control is displayed to indicate the remaining time before the deadline for starting to process the problem. The response level indicates the urgency of processing the problem.

16. The problem-solving method according to claim 1, characterized in that, The method further includes: For any one of the at least one questions, display the emotion tag for that question, which indicates the intensity of negative emotions of the user who submitted the question.

17. The problem-solving method according to claim 1, characterized in that, The method further includes: For any one of the at least one problems, the processing progress of the problem is displayed in the problem detection interface in the form of a timeline. The timeline is marked with at least one of the following: response time, start processing time, repair completion time, and satisfaction verification time. The response time is the time when the object responsible for solving the problem receives the problem.

18. The problem-solving method according to claim 1, characterized in that, The method further includes: The problem detection interface displays user feedback data for the application, which consists of descriptive information submitted by users regarding their experience with the application.

19. A problem-solving device, characterized in that, The device includes: The display unit is configured to perform an operation in the application's problem detection interface to display at least one problem existing in the application, the at least one problem being determined based on user feedback data regarding the application. The display unit is further configured to, for any one of the at least one problems, display the technical severity level of the problem based on at least one of the problem's impact degree characteristics, impact range characteristics, and occurrence frequency characteristics, wherein the impact degree characteristics are used to indicate the degree to which the problem blocks the user's core tasks, the impact range characteristics are used to indicate the scale of users affected by the problem, and the occurrence frequency characteristics are used to indicate the number of times the problem is reproduced per unit time. The processing unit is configured to perform graded processing of the at least one problem based on the technical severity level of the at least one problem.

20. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory used to store the executable program code of the processor; The processor is configured to execute the program code to implement the problem-solving method as described in any one of claims 1 to 18.

21. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the problem-solving method as described in any one of claims 1 to 18.

22. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the problem-solving method according to any one of claims 1 to 18.

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