Abnormality processing method and device, electronic equipment and storage medium

Abstract

The invention discloses an exception handling method and device, electronic equipment and a storage medium, and relates to the technical field of data processing.The exception handling method comprises the steps that a relation link model is obtained based on target processes, target threads and the corresponding relation between the target processes and the target threads; carrying out positioning processing on the detected abnormal parameters according to the relation link model, and generating abnormal data describing the positions of the abnormal parameters; and matching the abnormal data with an abnormal solution database to obtain a target abnormal solution corresponding to the abnormal parameter. According to the invention, a system exception handling knowledge reserve system based on exception positioning and solution is established, so that the time for manually capturing the system exception and positioning the exception is saved, the system exception can be quickly positioned, the solution can be quickly found, and the exception handling efficiency of the system is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an exception handling method, device, electronic device and storage medium. Background Art

[0002] With the development of TV technology, modern smart TV systems are becoming more and more complex, including a large number of processes, threads and parameters. As a result, the types of abnormalities in TV systems are also complicated, such as software crashes, interface freezes, audio and video asynchrony, etc., and each problem requires a specific solution. The traditional steps for analyzing TV system abnormalities are: capturing logs, analyzing logs, locating abnormal points, and finding solutions. Each step requires personnel from different professional fields to complete, resulting in inefficient processing of system abnormalities.

[0003] Therefore, how to improve the efficiency of exception handling in the system is a problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of this application is to provide an exception handling method, device, electronic device and storage medium, aiming to solve the technical problem of how to improve the efficiency of exception handling for the system.

[0005] To achieve the above purpose, the present application proposes an exception handling method, which includes:

[0006] Based on the target process, the target thread and the corresponding relationship between the target process and the target thread, a relational link model is obtained;

[0007] Performing location processing on the detected abnormal parameters according to the relationship link model, and generating abnormal data describing the location of the abnormal parameters;

[0008] Match the abnormal data with the abnormal solution database to obtain the target abnormal solution corresponding to the abnormal parameters.

[0009] In addition, to achieve the above purpose, the present application also proposes an exception handling device, which includes:

[0010] A construction module is used to obtain a relational link model based on a target process, a target thread, and a corresponding relationship between the target process and the target thread;

[0011] A positioning module, used to perform positioning processing on the detected abnormal parameters according to the relationship link model, and generate abnormal data describing the location of the abnormal parameters;

[0012] The solution module is used to match the abnormal data with the abnormal solution database to obtain the target abnormal solution corresponding to the abnormal parameters.

[0013] In addition, to achieve the above objectives, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the exception handling method described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the exception handling method described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the exception handling method described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart of the first embodiment of the method for handling an exception in this application is provided;

[0019] Figure 2 A schematic diagram of a relationship link model of the exception handling method provided in Example 1 of the present application;

[0020] Figure 3 A schematic diagram of a mapping scenario of the exception handling method provided in Example 1 of the present application;

[0021] Figure 4 A flowchart of the second embodiment of the abnormality handling method of the present application is provided;

[0022] Figure 5 A brief flowchart of the exception handling method provided in Example 2 of the present application;

[0023] Figure 6 This is a schematic diagram of the module structure of the exception handling device according to an embodiment of the present application;

[0024] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the exception handling method in the embodiment of the present application.

[0025] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0027] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0028] The main solution of the embodiment of the present application is: based on the target process, the target thread and the corresponding relationship between the target process and the target thread, a relationship link model is obtained; the detected abnormal parameters are located and processed according to the relationship link model to generate abnormal data describing the location of the abnormal parameters; the abnormal data is matched with the abnormal solution database to obtain the target abnormal solution corresponding to the abnormal parameters.

[0029] The existing technology for analyzing TV system anomalies consists of four steps: capturing logs, analyzing logs, locating anomalies, and finding solutions. Each step requires personnel from different professional fields to complete, resulting in low efficiency in handling system anomalies. Therefore, how to improve the efficiency of handling system anomalies is a problem that needs to be solved urgently.

[0030] The present application provides a solution, by establishing a set of exception handling knowledge reserve system of exception location + solution based on each process, each thread, each parameter and a preset exception solution database in the system, accurately locates any abnormal parameter detected based on the relationship link model, and automatically obtains the exception solution corresponding to the abnormal parameter based on the specific position of the abnormal parameter, eliminating the time of manually capturing system exceptions and locating exceptions, realizing rapid location of system exceptions and finding solutions, and improving the efficiency of exception handling for the system.

[0031] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, such as a smart TV, etc. The following takes a smart TV as an example to illustrate this embodiment and the following embodiments.

[0032] Based on this, the present application embodiment provides an exception handling method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the exception handling method of the present application.

[0033] In this embodiment, the exception handling method includes steps S10 to S30:

[0034] Step S10, obtaining a relational link model based on the target process, the target thread, and the corresponding relationship between the target process and the target thread;

[0035] It should be noted that the target process and target thread refer to the processes and threads under each process in the TV system of the smart TV; the relationship link model refers to an abstract model that describes the interaction and dependency between the target process, target thread and target parameters called by the target thread in the TV system. The relationship link model is a tree diagram that describes the relationship between processes, threads and parameters, wherein the target process, target thread and target parameter respectively constitute the nodes of the tree diagram, and the relationship between the target process, target thread and target parameter constitutes the connecting line of the tree diagram. This model can clearly display the connection between different components, which helps to understand the overall structure and behavior of the TV system.

[0036] It is understandable that in order to accurately locate and analyze anomalies in the system, it is necessary to establish a comprehensive and detailed relationship link model. Therefore, performing step S10 can avoid inaccurate anomaly positioning due to lack of understanding of the internal structure of the system, which in turn causes misjudgment of abnormal situations or ineffective troubleshooting, thereby providing a clear system structure model, making anomaly positioning more rapid and accurate.

[0037] For example, please refer to Figure 2 The figure shows that the entire system contains multiple processes (divided by functional modules), each process contains multiple threads, and each thread is composed of different functions, interfaces, variables, etc. From top to bottom in terms of hierarchy, a relationship link model can be generated by the process ID (Identity document), thread ID, and the attribution and calling relationships between functions, interfaces, and variables.

[0038] In a feasible implementation, step S10 may include steps S11 to S13:

[0039] Step S11, obtaining the target parameter called by the target thread and the calling relationship between the target thread and the target parameter, and constructing a relationship network diagram based on the target thread and the target parameter with the calling relationship;

[0040] It should be noted that target parameters refer to the functions, methods, interfaces and variables involved in the operation of the television system. These parameters jointly affect the behavior and performance of the television system; the relationship network diagram refers to a graphical representation of all target parameters in the television system and the calling relationship between the target parameters and the target threads, with each target parameter and target thread as a node and the calling relationship between the target parameter and the target thread as a connecting line to form a network diagram.

[0041] It is understandable that since there are a large number of different parameters in the television system and the calling relationships between different parameters are relatively complex, step S11 is performed to convert the complex interactive relationships of all parameters in the television system into an easy-to-understand graphical representation, thereby avoiding the problem of difficult analysis caused by the inability to intuitively understand the complex calling relationships within the system, and achieving intuitive and clear acquisition of the calling relationships of various parameters in the system, which is convenient for analyzing and locating abnormal problems.

[0042] Step S12, for any target process among the target processes, split the relationship network diagram according to the target process and the target thread under the target process to obtain a relationship model of the target process, wherein the relationship model is composed of the target thread under the target process and the target parameter called by the target thread;

[0043] It should be noted that the relationship model refers to the model obtained after splitting the relationship network diagram, which is used to describe the correspondence between the target process and the target thread. This model refers to the model that uniquely corresponds to different functional modules in the TV, which is a tree diagram of the correspondence between a process, multiple threads and multiple parameters, wherein a process, multiple threads and multiple parameters respectively constitute the nodes of the tree diagram, and the belonging relationships between a process, multiple threads and multiple parameters constitute the connecting lines of the tree diagram.

[0044] It is understandable that, since it may be difficult to focus on a specific process or thread when analyzing abnormal parameters due to the complexity of the relationship network diagram, step S12 is performed. By refining the overall relationship network diagram into a more specific model related to processes and threads, the behavior of specific processes and threads can be analyzed more accurately, thereby improving the accuracy of abnormality location.

[0045] Exemplarily, the relationship network diagram is split according to the process ID of the target process and the thread ID of the target thread, and a relationship model is obtained in which there are multiple different target processes under one target process and any target process calls multiple target parameters.

[0046] Step S13, after traversing each target process, the relationship model of each target process is obtained, and each relationship model is combined to obtain a relationship link model.

[0047] It is understandable that, since it is currently difficult to automatically and accurately locate abnormal problems, step S13 is performed to obtain a complete relationship link model by combining the relationship network diagram and the relationship model, thereby avoiding incomplete or inaccurate problem positioning due to lack of a global perspective, helping technical personnel to fully understand the structure of the system, and providing an effective and reliable data platform for quickly and accurately handling abnormal problems.

[0048] In this embodiment, by constructing a relationship network diagram of all parameters in the television network and a relationship model between threads and processes, the difficulty in locating abnormal problems and the low analysis efficiency of technical personnel due to the complexity of the television system are avoided, and a comprehensive understanding of the internal structure and behavior of the television system is achieved, which enables the rapid and accurate location of abnormal parameters, provides a clear problem-solving path, and improves the efficiency of exception handling and the stability of the system.

[0049] Step S20, performing location processing on the detected abnormal parameters according to the relationship link model, and generating abnormal data describing the location of the abnormal parameters;

[0050] It should be noted that abnormal parameters refer to parameter values ​​detected during the operation of the television system that are inconsistent with the normal state, and the parameter values ​​are the direct or indirect cause of the abnormality of the television system; abnormal data refers to descriptive information generated based on the abnormal parameters, which at least includes the location where the abnormal parameters occur, that is, the thread and process corresponding to the abnormal parameters.

[0051] It is understandable that since determining the solution to the anomaly requires accurately determining the specific location of the anomaly, and the traditional process of locating the anomaly of the television system requires the operator to complete it manually, the positioning efficiency is low, so step S20 is performed to avoid blind troubleshooting due to unclear source of the anomaly, which in turn leads to the inability to accurately adapt the solution to the abnormal situation. By accurately locating the anomaly, targeted anomaly data is generated, providing the necessary information for solving the anomaly. At the same time, by constructing a good relationship link model, the anomaly can be quickly located, thereby improving the efficiency of locating the anomaly of the television system.

[0052] In a feasible implementation, step S20 may include steps S21 to S23:

[0053] Step S21, querying the relationship link model according to the abnormal parameter to obtain the abnormal thread corresponding to the abnormal parameter;

[0054] It should be noted that an abnormal thread refers to a thread that exhibits abnormal behavior when executed in a television system, and there are abnormal parameters in the parameters called by the thread.

[0055] It is understandable that since traditional exception location methods usually have the problem of blindly searching for exception sources in multiple threads and processes, step S21 is performed to quickly locate the specific abnormal thread by querying the relationship link model, avoiding blindly searching for exception sources in multiple threads, saving troubleshooting time, and providing an accurate data basis for subsequent exception processing.

[0056] Step S22, querying the relationship link model according to the abnormal thread to obtain the abnormal process corresponding to the abnormal thread;

[0057] It should be noted that an abnormal process refers to a process that exhibits abnormal behavior when executed in a television system. The process may exhibit overall abnormal behavior due to abnormal parameters in the parameters called by the internal thread.

[0058] It is understandable that in the process of locating abnormal parameters, there may be a problem of only focusing on the process dimension, resulting in incomplete and inaccurate positioning. Therefore, step S22 is performed to trace back to the higher level where the abnormality occurred, that is, to determine which process has a problem, thereby avoiding the problem of incomplete and inaccurate positioning of abnormal parameters and improving the accuracy of abnormal parameter positioning.

[0059] Step S23, generating abnormal data based on the abnormal process, abnormal thread and abnormal parameters.

[0060] It is understandable that in the process of selecting an exception solution, the solution selection is often inaccurate or invalid due to the lack of a detailed description of the location where the exception occurred. Therefore, step S23 is performed to generate exception data based on the specific location of the exception parameter, thereby providing a detailed information basis for matching the exception solution, thereby ensuring the pertinence and effectiveness of the exception solution.

[0061] Exemplarily, the process ID of the abnormal process, the thread ID of the abnormal thread, and the detailed parameter information of the abnormality are all used as basic data for generating abnormal data, and the abnormal data including the specific location where the abnormal parameters occur are generated.

[0062] In this embodiment, by reverse querying the relationship link model, the positioning difficulty and low efficiency caused by the inability to accurately track the source of the exception in the television system exception processing are avoided, the specific thread and process where the exception occurs can be quickly located, and detailed exception data is generated, thereby improving the accuracy of exception positioning and the efficiency of exception processing.

[0063] Step S30, matching the abnormal data with the abnormal solution database to obtain the target abnormal solution corresponding to the abnormal parameters.

[0064] It should be noted that the target anomaly solution refers to an anomaly solution for detected anomaly parameters in a preset anomaly solution database, and the anomaly solution includes information such as steps, tools and methods required to solve the anomaly.

[0065] It is understandable that since there are often human errors and long processing time in the process of manually obtaining exception solutions, step S30 is performed to avoid technicians from repeatedly handling known exception problems, while reducing human errors and processing time. By automatically matching exception data with the solution database, an effective exception handling method is quickly provided, thereby improving the stability of the system and the efficiency of exception handling.

[0066] In a feasible implementation, step S30 may include steps S31 to S33:

[0067] Step S31, determining a first process corresponding to the abnormal parameter based on the abnormal data, and determining a target abnormal model corresponding to the first process in the abnormal database according to the first process;

[0068] It should be noted that the first process refers to the specific process corresponding to the abnormal parameter; the target abnormal model refers to the abnormal data model associated with the specific process, wherein the abnormal models in the abnormal database are divided according to the functional modules of the TV system, for example: LiveTV module abnormal model, media player abnormal model, WIFI module abnormal model, USB peripheral module abnormal model, etc. Each abnormal data model corresponds to a specific functional module of the TV system, and each process also corresponds to a specific functional module of the TV system. Therefore, there is a one-to-one correspondence between the target process and the abnormal data model.

[0069] It is understandable that since it is difficult to accurately identify abnormal situations during the process of identifying abnormal situations in the television system, step S31 is performed to match the abnormal data with a preset abnormal model in order to find abnormal situations that have been recorded in the past, thereby providing a reliable basis for quickly obtaining abnormal solutions.

[0070] Step S32, querying each abnormal data record of the target abnormal model according to the abnormal data, and obtaining the target abnormal data record matching the abnormal data;

[0071] It should be noted that the abnormal data record refers to the data entry about the specific abnormal situation recorded in the abnormal model, which may include information such as the description of the abnormality, the specific location where it occurred, keywords, etc.

[0072] Exemplarily, keyword records of each abnormal data record in the target abnormal model are queried according to the keyword of the abnormal data, and the abnormal data record corresponding to the keyword record consistent with the keyword is used as the target abnormal data record matching the abnormal data.

[0073] In a feasible implementation, step S32 may include steps S321 to S322:

[0074] Step S321, querying each abnormal data record in the target abnormal model according to the abnormal data, if there is an abnormal data record matching the abnormal data in each abnormal data record, then taking the abnormal data record matching the abnormal data as the target abnormal data record;

[0075] It is understandable that since there may be records in the existing exception records that are consistent with the abnormal situation described by the exception data, performing step S321 can avoid misdiagnosis or unnecessary work caused by the inability to match the existing exception records, and achieve rapid and accurate identification of known abnormal situations, thereby directly obtaining the corresponding exception solutions.

[0076] Step S322: if there is no abnormal data record matching the abnormal data in each abnormal data record, the abnormal database is updated based on the abnormal data to obtain an updated abnormal database, and the abnormal data is used as a target abnormal data record.

[0077] It is understandable that since new abnormal situations often arise during the actual abnormal monitoring of the television system, step S322 is performed. By recording new abnormal situations, it is possible to avoid new abnormal data being ignored, thereby preventing the information from being used in future abnormal processing. This expands the coverage of the abnormal database and provides effective data support for future abnormal diagnosis and solution formulation.

[0078] In this embodiment, by querying and updating the exception database, rapid identification of known exceptions and recording and tracking of new exceptions are achieved, thereby improving the comprehensiveness and accuracy of exception handling and ensuring continuous optimization of exception diagnosis and exception solutions.

[0079] Step S33, according to the target abnormal data record, determine the target abnormality solution model corresponding to the target abnormal data record in the abnormality solution database, and query each abnormality solution of the target abnormality solution model based on the abnormal data to obtain the target abnormality solution.

[0080] It should be noted that the target anomaly solution model refers to a model for solutions to specific abnormal situations. The model provides corresponding solution strategies based on different abnormal data records; the target anomaly solution refers to the specific solution steps and methods for the currently detected abnormal situation.

[0081] It is understandable that, since in the process of traditional TV anomaly resolution, it is necessary to manually select a solution based on the located anomaly, there is often a problem of wrong or invalid solution selection, so step S33 is performed to find the corresponding anomaly solution based on the target anomaly data record, thereby avoiding invalid or wrong solutions due to no direct connection to the solution, and realizing the rapid acquisition of accurate anomaly solutions based on the located anomaly conditions, thereby improving the efficiency and reliability of TV anomaly resolution.

[0082] For example, please refer to Figure 3 ,The figure shows that the exception database and the exception solution database ,formed by data modeling, the exception model and the exception solution model corresponding to the same TV ,functional module are mapped and linked to each other, that the LiveTV module exception model and the LiveTV module exception solution model are mapped and linked to each other, the media player module exception model and the media player module exception solution model are mapped and linked to each other, the WIFI module exception model and the WIFI module exception solution model are mapped and linked to each other, and the USB ,peripheral module exception model and the USB ,peripheral module exception solution model are mapped and linked to each other.

[0083] In this embodiment, based on matching the target abnormality model with the abnormal data, querying the abnormal data records and querying the target abnormality solution model, the problems of difficulty and wrong selection of abnormal solutions caused by lack of automation and targeting in the abnormal processing of the television system are avoided, and rapid and accurate diagnosis and effective resolution of abnormal problems are achieved, thereby improving the stability of the television system.

[0084] This embodiment provides an exception handling method, which establishes a system exception handling knowledge reserve system of exception location + solution based on each process, each thread, each parameter and a preset exception solution database in the system, accurately locates any detected abnormal parameter based on the relationship link model, and automatically obtains the abnormal solution corresponding to the abnormal parameter based on the specific position of the abnormal parameter, eliminating the time of manually capturing system exceptions and locating exceptions, realizing rapid location of system exceptions and finding solutions, and improving the efficiency of obtaining exception handling solutions for the system.

[0085] In a feasible implementation manner, step S33 may include steps S331 to S333:

[0086] Step S331: if there is an exception solution matching the exception data among the exception solutions, the exception solution matching the exception data is used as the target exception solution;

[0087] It is understandable that, since there may be a solution in the existing exception solution that can solve the abnormal situation described by the exception data, performing step S331 can avoid the problem of the exception being difficult to solve due to the inability to match the existing exception solution, and achieve the effect of quickly and accurately identifying the existing exception solution.

[0088] Step S332: if there is no exception solution matching the exception data among the exception solutions, then a supplementary exception solution corresponding to the exception data is obtained;

[0089] It should be noted that the supplementary exception solution refers to a new solution provided externally to solve the current exception data, which is a further supplement to the existing exception solution database.

[0090] It is understandable that in order to handle unknown or new abnormal situations, when there is no solution in the existing database, it is necessary to promptly supplement solutions that can handle new abnormal situations. Therefore, performing step S332 can avoid the problem of stagnation in exception processing due to lack of solutions, and provide an effective solution to newly emerging abnormal situations.

[0091] Exemplarily, if there is no exception solution matching the exception data among the exception solutions, an exception solution request signal is output to request a supplementary exception solution corresponding to the exception data.

[0092] Step S333: Update the exception solution database according to the supplementary exception solution to obtain an updated exception solution database, and use the exception solution corresponding to the supplementary exception solution in the updated exception solution database as the target exception solution.

[0093] It can be understood that in order to update and expand the exception solution database so that similar exception situations can be handled in the future, step S333 is performed, which can avoid repeated searches for solutions when encountering the same exception in the future, thereby enhancing the coverage and practicality of the exception solution database, thereby improving the efficiency of handling abnormal situations in the television system.

[0094] In this implementation, by querying the exception solution model and updating the exception solution database, the inability to effectively handle unknown or newly emerging abnormal situations due to the lack of targeted and dynamically updated exception solutions is avoided, and efficient diagnosis of abnormal situations and continuous optimization of solutions are achieved, thereby improving the automation level, accuracy and comprehensiveness of television system exception handling, and ensuring improved system stability and exception handling efficiency.

[0095] In a feasible implementation manner, the abnormal data includes abnormal occurrence location information and keywords, wherein the abnormal occurrence location information includes multiple location dimensions, and steps S100 to S500 may be included before step S331:

[0096] Step S100, matching each abnormal solution of the target abnormal solution model based on the abnormal location information and the keyword, if there is an abnormal solution that matches both the abnormal location information and the keyword among the abnormal solutions, then determining that there is an abnormal solution that matches the abnormal data among the abnormal solutions;

[0097] It should be noted that the exception location information refers to data used to describe the specific location where the exception occurs, which may include information such as the process, thread or function where the exception occurs; keywords refer to words or codes related to the exception that can be used to describe the characteristics of the exception.

[0098] For example, in the process of performing the first query based on certain abnormal data, various abnormal solutions are queried through abnormal occurrence location information and keywords including all dimensions, and the most matching abnormal solution is found as much as possible to achieve more accurate abnormal processing.

[0099] Step S200: If there is no exception solution that matches both the exception occurrence location information and the keyword among the exception solutions, a supplementary exception solution corresponding to the exception data is obtained, and the location dimension of the exception occurrence location information is reduced;

[0100] It should be noted that the location dimension refers to the level of detail or level of the information about the location where the exception occurred. Reducing the location dimension means providing coarser location information in order to match a more accurate exception solution as much as possible.

[0101] Step S300, matching each abnormal solution of the target abnormal solution model based on the keyword and the adjusted abnormal occurrence location information, if there is an abnormal solution that matches both the keyword and the adjusted abnormal occurrence location information among the abnormal solutions, then determining that there is an abnormal solution that matches the abnormal data among the abnormal solutions;

[0102] Step S400: if there is no abnormal solution that matches both the keyword and the adjusted abnormal occurrence location information among the abnormal solutions, return to the step of reducing the location dimension of the abnormal occurrence location information, and when the location dimension reaches the minimum, query each abnormal solution in the target abnormal solution model based on the keyword, and if there is an abnormal solution that matches the keyword among the abnormal solutions, it is determined that there is an abnormal solution that matches the abnormal data among the abnormal solutions;

[0103] Exemplarily, when the exception location information includes the thread and process to which the exception parameter belongs, after continuously reducing the dimension of the exception location information from the parameter dimension, to the thread dimension, and then to the lowest process dimension, if no successful match is found, a query for the exception solution is performed based on the keyword.

[0104] Step S500: If there is no exception solution matching the keyword in each exception solution, it is determined that there is no exception solution matching the exception data in each exception solution.

[0105] In this embodiment, the location dimension of the location where the exception occurs is continuously reduced to obtain an exception solution that matches as much as possible, and a supplementary exception solution is obtained to avoid repeatedly seeking solutions when encountering the same exception in the future. This enhances the integrity and self-learning ability of the exception solution database, improves the accuracy and comprehensiveness of the television system's exception handling, and ensures the efficiency of the system's exception handling.

[0106] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , Figure 4 This is a flow chart of the second embodiment of the exception handling method of the present application.

[0107] After step S322, step S323 may also be included:

[0108] Step S323, obtaining the supplementary exception solution corresponding to the exception data, and updating the exception solution database according to the supplementary exception solution to obtain an updated exception solution database.

[0109] It is understandable that since the exception data newly added to the exception database does not have a corresponding exception solution in the preset exception solution database, performing step S323 can avoid the inability to repair the newly emerging exceptions due to lack of solutions, enhance the integrity of the exception solution database, and improve the accuracy and comprehensiveness of the television system's exception handling. By obtaining special solutions to newly emerging abnormal situations, the efficiency of the system's exception handling is ensured.

[0110] Exemplarily, an exception solution resolution signal is output according to the exception data to request a supplementary exception solution corresponding to the legacy data, and the supplementary exception solution is added to the exception solution database to obtain an updated exception solution database.

[0111] In this embodiment, by updating the exception solution database according to the obtained supplementary exception solutions, the inability to effectively solve new or unknown exception situations due to incomplete exception solution database is avoided, dynamic updating and optimization of exception solutions are achieved, and accurate matching between exception data and solutions is ensured, thereby improving the flexibility and adaptability of exception handling of the television system, enhancing the stability of the system and the efficiency of exception resolution.

[0112] For example, to help understand the implementation process of the exception handling method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 5 , Figure 5 A brief flowchart of an exception handling method is provided, specifically:

[0113] First, the system is monitored for abnormalities. When an abnormality is found during monitoring, that is, an abnormal parameter is detected, abnormal data describing the position of the abnormal parameter is generated, and an abnormal database is queried based on the abnormal data to match an abnormal model corresponding to the abnormal data. When an abnormal data record matching the abnormal data exists in each abnormal data record of the abnormal model, an abnormal solution database is queried based on the abnormal data to match an abnormal solution model corresponding to the abnormal data, and a solution in the abnormal solution model is obtained based on the abnormal data, and the solution is displayed; when an abnormal data record matching the abnormal data does not exist in each abnormal data record of the abnormal model, the abnormal data is inserted into the abnormal database, that is, the abnormal data is updated based on the abnormal data, and the abnormal occurrence point is located according to the system relationship link model diagram, so as to obtain a supplementary abnormal solution, and then the supplementary abnormal solution is inserted into the abnormal solution database, that is, the abnormal solution database is updated according to the supplementary abnormal solution, and the solution is displayed.

[0114] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the exception handling method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0115] This application also provides an exception handling device, please refer to Figure 6 , the exception handling device comprises:

[0116] A construction module 10 is used to obtain a relational link model based on a target process, a target thread, and a corresponding relationship between the target process and the target thread;

[0117] A positioning module 20, used to perform positioning processing on the detected abnormal parameters according to the relationship link model, and generate abnormal data describing the position of the abnormal parameters;

[0118] The solution module 30 is used to match the abnormal data with the abnormal solution database to obtain a target abnormal solution corresponding to the abnormal parameters.

[0119] Optionally, the building block 10 is further used for:

[0120] Obtaining the target parameter called by the target thread and the calling relationship between the target thread and the target parameter, and constructing a relationship network diagram based on the target thread and the target parameter with the calling relationship;

[0121] For any target process among the target processes, the relationship network diagram is split according to the target process and the target thread under the target process to obtain a relationship model of the target process, wherein the relationship model is composed of the target thread under the target process and the target parameter called by the target thread;

[0122] After traversing each target process, the relationship model of each target process is obtained, and each relationship model is combined to obtain a relationship link model.

[0123] Optionally, the building block 10 is further used for:

[0124] According to the abnormal parameters, the relation link model is queried to obtain the abnormal thread corresponding to the abnormal parameters;

[0125] According to the abnormal thread query relationship link model, the abnormal process corresponding to the abnormal thread is obtained;

[0126] Generate exception data based on abnormal processes, abnormal threads and abnormal parameters.

[0127] Optionally, the solution module 30 is further configured to:

[0128] Determine a first process corresponding to the abnormal parameter based on the abnormal data, and determine a target abnormal model corresponding to the first process in the abnormal database according to the first process;

[0129] Query each abnormal data record of the target abnormal model according to the abnormal data to obtain the target abnormal data record matching the abnormal data;

[0130] According to the target abnormal data record, a target abnormal solution model corresponding to the target abnormal data record in the abnormal solution database is determined, and each abnormal solution of the target abnormal solution model is queried based on the abnormal data to obtain the target abnormal solution.

[0131] Optionally, the solution module 30 is further configured to:

[0132] Query each abnormal data record in the target abnormal data model according to the abnormal data, and if there is an abnormal data record matching the abnormal data in each abnormal data record, then use the abnormal data record matching the abnormal data as the target abnormal data record;

[0133] If there is no abnormal data record matching the abnormal data in each abnormal data record, the abnormal database is updated based on the abnormal data to obtain an updated abnormal database, and the abnormal data is used as a target abnormal data record.

[0134] Optionally, the solution module 30 is further configured to:

[0135] A supplementary exception solution corresponding to the exception data is obtained, and the exception solution database is updated according to the supplementary exception solution to obtain an updated exception solution database.

[0136] Optionally, the solution module 30 is further configured to:

[0137] If there is an exception solution matching the exception data among the exception solutions, the exception solution matching the exception data is used as the target exception solution;

[0138] If there is no exception solution matching the exception data among the exception solutions, then a supplementary exception solution corresponding to the exception data is obtained;

[0139] The exception solution database is updated according to the supplementary exception solution to obtain an updated exception solution database, and the exception solution corresponding to the supplementary exception solution in the updated exception solution database is used as the target exception solution.

[0140] The exception handling device provided by the present application adopts the exception handling method in the above embodiment, which can solve the technical problem of how to improve the efficiency of exception handling for the system. Compared with the prior art, the beneficial effects of the exception handling device provided by the present application are the same as the beneficial effects of the exception handling method provided by the above embodiment, and other technical features in the exception handling device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0141] The present application provides an electronic device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the exception handling method in the above-mentioned embodiment 1.

[0142] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0143] like Figure 7As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0144] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0145] The electronic device provided by the present application adopts the exception handling method in the above embodiment to solve the technical problem of how to improve the efficiency of exception handling for the system. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the exception handling method provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0146] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0148] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the exception handling method in the above-mentioned embodiment.

[0149] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0150] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0151] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: obtains a relationship link model based on the target process, the target thread and the corresponding relationship between the target process and the target thread; locates and processes the detected abnormal parameters according to the relationship link model, and generates abnormal data describing the location of the abnormal parameters; matches the abnormal data with the abnormal solution database to obtain the target abnormal solution corresponding to the abnormal parameters.

[0152] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0153] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0154] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0155] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned exception handling method, and can solve the technical problem of how to improve the efficiency of exception handling for the system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the exception handling method provided by the above-mentioned embodiment, and will not be repeated here.

[0156] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned exception handling method when executed by a processor.

[0157] The computer program product provided by the present application can solve the technical problem of how to improve the efficiency of exception handling for the system. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the exception handling method provided by the above embodiment, which will not be repeated here.

[0158] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An exception handling method, characterized in that: The method comprises: Obtaining a relational link model based on a target process, a target thread, and a corresponding relationship between the target process and the target thread; Performing location processing on the detected abnormal parameters according to the relationship link model to generate abnormal data describing the location of the abnormal parameters; The abnormal data is matched with an abnormal solution database to obtain a target abnormal solution corresponding to the abnormal parameter.

2. The exception handling method according to claim 1, characterized in that: The step of obtaining a relational link model based on a target process, a target thread, and a corresponding relationship between the target process and the target thread comprises: Obtaining a target parameter called by a target thread and a calling relationship between the target thread and the target parameter, and constructing a relationship network diagram based on the target thread and the target parameter having the calling relationship; For any one of the target processes, the relationship network diagram is split according to the target process and the target thread under the target process to obtain a relationship model of the target process, wherein the relationship model is composed of the target thread under the target process and the target parameter called by the target thread; After traversing each target process, the relationship models of the target processes are obtained, and the relationship models are combined to obtain the relationship link model.

3. The exception handling method according to claim 1, characterized in that: The step of performing location processing on the detected abnormal parameters according to the relationship link model and generating abnormal data describing the location of the abnormal parameters comprises: Query the relationship link model according to the abnormal parameter to obtain the abnormal thread corresponding to the abnormal parameter; Querying the relationship link model according to the abnormal thread to obtain the abnormal process corresponding to the abnormal thread; The abnormal data is generated based on the abnormal process, the abnormal thread and the abnormal parameter.

4. The exception handling method according to claim 1, characterized in that: The step of matching the abnormal data with the abnormal solution database to obtain a target abnormal solution corresponding to the abnormal parameter comprises: Determine a first process corresponding to the abnormal parameter based on the abnormal data, and determine a target abnormal model corresponding to the first process in an abnormal database according to the first process; Query each abnormal data record of the target abnormal model according to the abnormal data to obtain a target abnormal data record matching the abnormal data; According to the target abnormal data record, a target abnormal solution model corresponding to the target abnormal data record in an abnormal solution database is determined, and each abnormal solution of the target abnormal solution model is queried based on the abnormal data to obtain the target abnormal solution.

5. The exception handling method according to claim 4, characterized in that: The step of querying each abnormal data record of the target abnormal model according to the abnormal data to obtain the target abnormal data record matching the abnormal data comprises: Querying each abnormal data record in the target abnormal data model according to the abnormal data, and if there is an abnormal data record matching the abnormal data in each abnormal data record, taking the abnormal data record matching the abnormal data as the target abnormal data record; If there is no abnormal data record matching the abnormal data in the abnormal data records, the abnormal database is updated based on the abnormal data to obtain an updated abnormal database, and the abnormal data is used as the target abnormal data record.

6. The exception handling method according to claim 5, characterized in that: After the step of recording the abnormal data as the target abnormal data, the following step further comprises: A supplementary exception solution corresponding to the exception data is obtained, and the exception solution database is updated according to the supplementary exception solution to obtain an updated exception solution database.

7. The exception handling method according to claim 4, characterized in that: The step of querying each abnormal solution of the target abnormal solution model based on the abnormal data to obtain the target abnormal solution comprises: If there is an exception solution matching the exception data among the exception solutions, the exception solution matching the exception data is used as the target exception solution; If there is no exception solution matching the exception data among the exception solutions, obtaining a supplementary exception solution corresponding to the exception data; The exception solution database is updated according to the supplementary exception solution to obtain an updated exception solution database, and the exception solution corresponding to the supplementary exception solution in the updated exception solution database is used as the target exception solution.

8. An exception handling device, characterized in that: The device comprises: A construction module, used for obtaining a relational link model based on a target process, a target thread, and a corresponding relationship between the target process and the target thread; A positioning module, used for performing positioning processing on the detected abnormal parameters according to the relationship link model, and generating abnormal data describing the position of the abnormal parameters; The solution module is used to match the abnormal data with the abnormal solution database to obtain a target abnormal solution corresponding to the abnormal parameter.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the exception handling method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the exception handling method according to any one of claims 1 to 7 are implemented.

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