Crash information processing method and device, electronic equipment and storage medium

Abstract

The invention provides a crash information processing method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical fields of artificial intelligence, application program development, application program testing and the like. According to the specific implementation scheme, the method comprises the steps of obtaining current crash information of a target application; determining multiple pieces of historical crash information of the target application program; wherein the multiple pieces of historical crash information correspond to different stability problems of the target application program; performing semantic comparison on the current crash information and multiple pieces of historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used for representing whether the current crash information belongs to repeatedly reported crash information relative to the multiple pieces of historical crash information.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the technical fields of artificial intelligence, application development, application testing, etc., and specifically to a crash information processing method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of computer technology, various applications are emerging. In order to ensure the stable operation of these applications, whether in the early development stage or in the later operation and maintenance stage, it is particularly important to efficiently analyze and process the crash information reported by the application. This is aimed at quickly locating and solving various stability problems of the application. Summary of the invention

[0003] The present disclosure provides a crash information processing method, device, electronic device and storage medium.

[0004] According to a first aspect of the present disclosure, a crash information processing method is provided, comprising:

[0005] Get the current crash information of the target application;

[0006] Determine multiple historical crash information of the target application; wherein the multiple historical crash information corresponds to different stability issues of the target application;

[0007] The current crash information is semantically compared with the multiple historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the multiple historical crash information.

[0008] According to a second aspect of the present disclosure, there is provided a crash information processing device, comprising:

[0009] A first information acquisition unit, used to acquire current crash information of a target application;

[0010] A second information acquisition unit is used to determine a plurality of historical crash information of the target application; wherein the plurality of historical crash information corresponds to different stability problems of the target application;

[0011] The comparison result acquisition unit is used to perform semantic comparison between the current crash information and the multiple historical crash information to obtain the crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is the repeatedly reported crash information relative to the multiple historical crash information.

[0012] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0013] at least one processor;

[0014] a memory communicatively coupled to the at least one processor;

[0015] 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 to enable the at least one processor to execute the method provided by the embodiment of the present disclosure.

[0016] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method provided according to an embodiment of the present disclosure.

[0017] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method provided according to the embodiment of the present disclosure when executed by a processor.

[0018] The use of the present disclosure can reduce the cost of developers for troubleshooting and deduplication of current crash information, thereby improving the efficiency of solving stability problems for target applications.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0021] Figure 1 A flowchart of a crash information processing method provided by an embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of the integrity flow of a crash information processing method provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of a scenario of a crash information processing method provided by an embodiment of the present disclosure;

[0024] Figure 4 A schematic structural block diagram of a crash information processing device provided by an embodiment of the present disclosure;

[0025] Figure 5 A schematic structural block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0027] As described in the background technology, in recent years, with the rapid development of computer technology, various applications have emerged. In order to ensure the stable operation of these applications, whether in the early development stage or in the later operation and maintenance stage, it is particularly important to efficiently analyze and process the crash information reported by the application. This is intended to quickly locate and solve various stability problems of the application. However, at present, the numerous crash information reported by the application often includes a large amount of repeated crash information, and these repeated crash information corresponds to the same stability problem of the application. This situation significantly increases the cost of developers to check and remove duplication of crash information, thereby reducing the efficiency of solving stability problems for the application.

[0028] To this end, the prior art provides an automatic troubleshooting and deduplication method for crash information. Specifically, the first message-digest algorithm (Message-Digest Algorithm5, MD5) can be used to convert the first crash information to be checked into a first coding value, and the second crash information to be checked can be converted into a second coding value using MD5, and the first coding value and the second coding value can be compared to obtain a coding value comparison result, which is used to characterize whether the first crash information and the second crash information are duplicated crash information. Thereafter, when the coding value comparison result characterizes that the first crash information and the second crash information are duplicated crash information, the first crash information or the second crash information is deleted to achieve deduplication processing for the first crash information and the second crash information.

[0029] With regard to the above method, the inventor has found through research that when the first crash information is converted into the first code using MD5, only the surface information of the first crash information is obtained. Similarly, when the second crash information is converted into the second code value using MD5, only the surface information of the second crash information is obtained. Therefore, by comparing the first code value and the second code value, the code value comparison result obtained cannot accurately characterize whether the first crash information and the second crash information are duplicated crash information. In this case, the developer is still required to review the first crash information and the second crash information, which still cannot reduce the developer's cost of troubleshooting and deduplication of the crash information, and cannot improve the efficiency of solving stability problems of the application.

[0030] In view of the above problems, the present disclosure provides a crash information processing method, which can be applied to an electronic device. The electronic device can be a server, a workbench, a large computer or other similar computing device. Figure 1 The flowchart diagram shown illustrates a crash information processing method provided by an embodiment of the present disclosure. It should be noted that although the flowchart diagram shows a logical sequence, in some cases, the steps shown or described may also be performed in other sequences.

[0031] Step S101, obtaining current crash information of the target application.

[0032] The target application may be a desktop application, for example, an Android application developed using the Java programming language. In addition, in the disclosed embodiment, the target application may be a social application, a consulting application, a game application, a shopping application, a tool application, etc., which is not limited in the disclosed embodiment.

[0033] In one example, the current crash information may be the current crash stack of the target application, which may include key crash information and function call chains. Among them, the key crash information is used to describe the stability problems that occurred in the target application, and may specifically include exception types, error codes, error messages, etc.; the function call chain can be understood as the complete function call path that the target application went through before the stability problem occurred. The starting position of the function call path is the entry point of the target application, and the end position is the node function that caused the stability problem in the target application. Here, stability problems may include flashbacks, freezes, interface anomalies, functional anomalies, application not responding (ANR), memory leaks, data loss or corruption, compatibility issues, etc.

[0034] In another example, the current crash information may also be a stack processing result obtained after processing (eg, extracting feature data) the current crash stack of the target application.

[0035] Step S102: determining multiple historical crash information of the target application.

[0036] Each of the plurality of historical crash information may be a historical crash stack of a target application, or may be a stack processing result obtained after processing (eg, extracting feature data) the historical crash stack of the target application.

[0037] In addition, it should be noted that, in the embodiment of the present disclosure, each piece of historical crash information in the plurality of historical crash information has the same data structure as the current crash information, which will not be described in detail herein.

[0038] It should also be noted that, among the multiple historical crash information mentioned in the embodiments of the present disclosure, any two historical crash information do not belong to the repeatedly reported crash information, that is, the multiple historical crash information corresponds to different stability problems of the target application, which can be specifically understood as the multiple historical crash information is generated due to different stability problems of the target application. Exemplarily, the multiple historical crash information may include historical crash information A1, historical crash information A2, historical crash information A3 and historical crash information A4. Among them, historical crash information A1 corresponds to the flash back problem of the target application, historical crash information A2 corresponds to the freeze problem of the target application, historical crash information A3 corresponds to the interface abnormality problem of the target application, and historical crash information A4 corresponds to the functional abnormality problem of the target application.

[0039] Step S103: perform semantic comparison between the current crash information and multiple historical crash information to obtain a crash information comparison result.

[0040] The crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the multiple historical crash information.

[0041] In one example, each historical crash information among multiple historical crash information can be used as crash information to be compared, and the first semantic information of the current crash information and the second semantic information of the crash information to be compared are obtained. The first semantic information and the second semantic information are then compared to obtain a single comparison result, which is used to characterize whether the current crash information is a duplicated crash information relative to the crash information to be compared.

[0042] Further, in the disclosed embodiment, the single comparison result may be a first single comparison result, which is used to characterize that the current crash information is a repeatedly reported crash information relative to the crash information to be compared, that is, characterizes that the current crash information and the crash information to be compared correspond to the same stability problem of the target application; the single comparison result may also be a second single comparison result, which is used to characterize that the current crash information is not a repeatedly reported crash information relative to the crash information to be compared, that is, characterizes that the current crash information and the crash information to be compared correspond to different stability problems of the target application. Based on this, after obtaining multiple single comparison results corresponding to multiple historical crash information one by one, if the first single comparison result exists in the multiple single comparison results, it is determined that the current crash information is a repeatedly reported crash information relative to the multiple historical crash information; if the first single comparison result does not exist in the multiple single comparison results, it is determined that the current crash information is not a repeatedly reported crash information relative to the multiple historical crash information.

[0043] By adopting the crash information processing method provided by the embodiment of the present disclosure, after obtaining the current crash information of the target application and determining multiple historical crash information of the target application, the deep information (i.e., semantic information) of the current crash information and the deep information (i.e., semantic information) of multiple historical crash information can be mined and compared to obtain the crash information comparison result, so as to accurately express whether the current crash information is a duplicated crash information relative to multiple historical crash information. In this way, there is no need for the developer to recheck the current crash information of the target application, thereby reducing the developer's troubleshooting and deduplication costs for the current crash information, so as to improve the efficiency of solving stability problems for the target application.

[0044] As mentioned above, in the embodiment of the present disclosure, the current crash information may be a stack processing result obtained after processing the current crash stack of the target application. Based on this, in some optional implementations, step S101, that is, "obtaining the current crash information of the target application" may include:

[0045] Get the current crash stack of the target application;

[0046] Based on the key crash information and function call chain in the current crash stack, the stack information to be processed is obtained;

[0047] Vectorize the stack information to be processed to obtain the current crash information.

[0048] The current crash stack may be the latest crash stack information reported by the target application.

[0049] In one example, after the crash stack data reported by the target application is obtained, the crash stack data can be cleaned to obtain the current crash stack, and the key crash information and function call chain can be extracted from the current crash stack by keyword matching, and then the stack information to be processed is obtained based on the key crash information and function call chain in the current crash stack. For example, the key crash information and function call chain in the current crash stack can be used together as the stack information to be processed.

[0050] In one example, after the stack information to be processed is obtained, the stack information to be processed can be vectorized through an embedded model (Embedding) to obtain current crash information.

[0051] Through the above manner, in the embodiment of the present disclosure, on the one hand, after obtaining the current crash stack of the target application, the current crash stack is not vectorized as a whole, but based on the key crash information and function call chain in the current crash stack, the stack information to be processed is obtained, and then the stack information to be processed is vectorized to obtain the current crash information, thereby reducing the amount of information processing involved in the process, so as to improve the execution efficiency of the crash information processing method, and then improve the efficiency of solving the stability problem of the target application; on the other hand, because the current crash information is obtained after vectorizing the stack information to be processed, the current crash information is numerical data that is conducive to machine learning or other algorithm processing, which can not only improve the accuracy of subsequent processing results (for example, crash information comparison results), but also improve the execution efficiency of the crash information processing method, and then improve the efficiency of solving the stability problem of the target application.

[0052] In addition, it should be noted that in an embodiment of the present disclosure, when the target application enters the test (for example, Xmonkey) process and a stability problem is detected, crash stack data can be generated and reported to an electronic device that applies the crash information processing method provided by an embodiment of the present disclosure based on the detected stability problem. Thereafter, the electronic device can execute the crash information processing method in response to the received crash stack data, for example, starting to execute step S101 to obtain the current crash stack of the target application, and based on the key crash information and function call chain in the current crash stack, obtaining the stack information to be processed, and then vectorizing the stack information to be processed to obtain the current crash information, and executing steps S102 and S103.

[0053] In some optional implementations, step S102, i.e., “determining a plurality of historical crash information of a target application” may include:

[0054] A plurality of historical crash information similar to the current crash information is selected from a plurality of candidate crash information stored in a historical crash information library.

[0055] Each of the plurality of candidate crash information may be a historical crash stack of a target application within a specified historical period, or may be a crash stack processing result obtained after processing (eg, extracting feature data) the historical crash stack of the target application.

[0056] In addition, it should be noted that, in the embodiment of the present disclosure, each candidate crash information in the plurality of candidate crash information has the same data structure as the current crash stack, which will not be described in detail herein.

[0057] It should also be noted that, among the multiple candidate crash information mentioned in the embodiments of the present disclosure, any two candidate crash information do not belong to the repeatedly reported crash information, that is, the multiple candidate crash information corresponds to different stability problems of the target application, which can be specifically understood as the multiple candidate crash information is generated due to different stability problems of the target application. Exemplarily, the multiple candidate crash information may include candidate crash information A1, candidate crash information A2, candidate crash information A3, candidate crash information A4 and candidate crash information A5. Among them, candidate crash information A1 corresponds to the flash back problem of the target application, candidate crash information A2 corresponds to the freeze problem of the target application, candidate crash information A3 corresponds to the interface abnormality problem of the target application, candidate crash information A4 corresponds to the functional abnormality problem of the target application, and candidate crash information A5 corresponds to the application non-response problem of the target application.

[0058] Furthermore, in one example, the cosine similarity algorithm may be used to calculate the similarity between each candidate crash information in the plurality of candidate crash information and the current crash information, and based on this, a plurality of historical crash information similar to the current crash information may be selected from the plurality of candidate crash information.

[0059] For example, a target number of candidate crash information with the greatest similarity to the current crash information can be selected from multiple candidate crash information as historical crash information; for another example, multiple candidate crash information with a similarity to the current crash information greater than or equal to a preset threshold can be selected from multiple candidate crash information as historical crash information. The target number can be set according to actual application requirements, for example, set to 5, and the embodiments of the present disclosure do not impose any restrictions on this; the preset threshold can be set according to actual application requirements, and the embodiments of the present disclosure do not impose any restrictions on this either. That is to say, in the embodiments of the present disclosure, a diversified determination method is provided for multiple historical crash information, which can improve the flexibility of the crash information processing method.

[0060] Through the above method, in the embodiment of the present disclosure, multiple selected crash information similar to the current crash information can be selected from multiple candidate crash information stored in the historical crash information library as historical crash information to obtain multiple historical crash information. In this way, in the subsequent semantic comparison process, only the current crash information can be semantically compared with multiple historical crash information, and the current crash information can be semantically compared with all candidate crash information in the historical crash information library, thereby reducing the amount of information processing involved in the semantic comparison, thereby improving the execution efficiency of the crash information processing method, and further improving the efficiency of solving stability problems for the target application.

[0061] Furthermore, in the embodiment of the present disclosure, when the crash information comparison result indicates that the current crash information is not a duplicated crash information relative to multiple historical crash information, the current crash information can also be used as new candidate crash information and stored in the historical crash information library to expand the historical crash information library. In this way, in a specified future period, when the new current crash information obtained is a duplicated crash information relative to the current crash information, it can still be ensured that the crash information processing method provided by the embodiment of the present disclosure can accurately check and deduplicate the new current crash information, thereby improving the reliability of the crash information processing method.

[0062] In addition, it should be noted that in the embodiment of the present disclosure, when the crash information comparison result indicates that the current crash information does not belong to the repeatedly reported crash information relative to multiple historical crash information, the current crash information can also be reviewed by the developer. Only when the developer verifies that the current crash information does not belong to the repeatedly reported crash information relative to multiple historical crash information, the current crash information is used as a new candidate crash information and stored in the historical crash information library, so as to expand the historical crash information library, thereby ensuring the reliability of the expansion results of the historical crash information library.

[0063] In some optional implementations, step S103, that is, "performing semantic comparison between the current crash information and the plurality of historical crash information to obtain a crash information comparison result" may include:

[0064] Each of the multiple historical crash information is used as the crash information to be compared, and a large language model is used to perform semantic comparison between the current crash information and the crash information to be compared, so as to obtain a single comparison result; wherein the single comparison result is used to indicate whether the current crash information is a duplicated crash information relative to the crash information to be compared;

[0065] Based on a plurality of single item comparison results corresponding one-to-one to a plurality of historical crash information, a crash information comparison result is obtained.

[0066] Among them, the large language model can be a pre-trained neural network model (for example, an autoregressive generative model of a Transformer architecture), which has general language knowledge, world knowledge, professional knowledge in various fields, etc., and especially has powerful text understanding and analysis capabilities.

[0067] In addition, in one example, “using a large language model to perform semantic comparison on the current crash information and the crash information to be compared, and obtaining a single comparison result” may include:

[0068] Using the large language model, according to the first prompt information, a semantic comparison is performed on the current crash information and the crash information to be compared to obtain a single comparison result.

[0069] Exemplarily, the target application is an Android application developed using the Java programming language. In this case, the first prompt information can be: You are a senior application development engineer, proficient in the analysis of crash information of Android applications developed using the Java programming language. I will provide you with two crash information. Please tell me whether these two crash information are repeated crash information; the first prompt information can also be: You are a senior application development engineer, proficient in the analysis of crash information of Android applications developed using the Java programming language. I will provide you with two crash information. Please judge whether these two crash information correspond to the same stability problem of the target application, and tell me whether these two crash information are repeated crash information. The first prompt information can also be: You are a senior application development engineer, proficient in the analysis of crash information of Android applications developed using the Java programming language. I will provide you with two crash information. Please judge whether these two crash information correspond to the same stability problem of the target application, and tell me whether these two crash information are repeated crash information, and tell me your reasoning basis.

[0070] The two crash information provided for the large language model may be the current crash information and the crash information to be compared.

[0071] In the above example, the large language model can be used to perform semantic comparison on the current crash information and the crash information to be compared according to the first prompt information to obtain a single comparison result. In this way, the process of obtaining the single comparison result can be simplified to improve the execution efficiency of the crash information processing method, thereby improving the efficiency of solving the stability problem of the target application.

[0072] In another example, “using a large language model to perform semantic comparison on the current crash information and the crash information to be compared to obtain a single comparison result” may include:

[0073] Based on the current crash information and the crash information to be compared, the current crash template is obtained;

[0074] Selecting a historical crash template similar to the current crash template from a plurality of candidate crash templates stored in a historical crash template library; wherein the historical crash template is obtained based on the first historical crash information and the second historical crash information, and the first historical crash information and the second historical crash information are crash information that are repeatedly reported;

[0075] Using a large language model and taking historical crash templates as reference cases, a semantic comparison is performed between the current crash information and the crash information to be compared to obtain a single comparison result.

[0076] In one example, the current crash information and the crash information to be compared can be assembled to obtain the current crash information group, and the current crash information group can be vectorized to obtain the current crash template. Among them, "assembling the current crash information and the crash information to be compared to obtain the current crash template" can be: splicing the current crash information and the crash information to be compared to obtain the current crash information group. Thereafter, the current crash information group can be vectorized through Embedding to obtain the current crash template. Since the current crash template is obtained by vectorizing the current crash information group, the current crash template is numerical data that is conducive to machine learning or other algorithm processing, which can not only improve the accuracy of subsequent processing results (for example, crash information comparison results), but also improve the execution efficiency of the crash information processing method, thereby improving the efficiency of solving stability problems for the target application.

[0077] After the current crash template is obtained, the cosine similarity algorithm may be used to calculate the similarity between each of the multiple candidate crash templates and the current crash template, and based on this, a historical crash template most similar to the current crash template may be selected from the multiple candidate crash templates.

[0078] After obtaining the current crash template and the historical crash template, the large language model can be used to perform a semantic comparison between the current crash information and the crash information to be compared, taking the historical crash template as a reference case, to obtain a single comparison result. In a specific example, the large language model can be used to perform a semantic comparison between the current crash information and the crash information to be compared, according to the second prompt information, taking the historical crash template as a reference case, to obtain a single comparison result.

[0079] Exemplarily, the target application is an Android application developed using the Java programming language. In this case, the second prompt information can be: You are a senior application development engineer who is proficient in the analysis of crash information of Android applications developed using the Java programming language. I will provide you with two crash information. Please use the historical crash template as a reference case to tell me whether the two crash information are repeated crash information; the second prompt information can also be: You are a senior application development engineer who is proficient in the analysis of crash information of Android applications developed using the Java programming language. I will provide you with two crash information. Please use the historical crash template as a reference case to determine whether the two crash information correspond to the same stability problem of the target application, and accordingly tell me whether the two crash information are repeated crash information, and tell me your reasoning basis.

[0080] The two crash information provided for the large language model may be the current crash information and the crash information to be compared.

[0081] In addition, it should be noted that in the embodiment of the present disclosure, when the historical crash template has a corresponding reasoning basis, the second prompt information can be: You are a senior application development engineer who is proficient in crash information analysis of Android applications developed using the Java programming language. I will provide you with two crash information. Please use the historical crash template as a reference case and combine it with the reasoning basis corresponding to the historical crash template to determine whether the two crash information correspond to the same stability problem of the target application. Based on this, tell me whether the two crash information are duplicated crash information and tell me your reasoning basis.

[0082] The two crash information provided for the large language model may be the current crash information and the crash information to be compared.

[0083] In the above example, the current crash template can be obtained based on the current crash information and the crash information to be compared, and a historical crash template similar to the current crash template is selected from multiple candidate crash templates stored in the historical crash template library, and then the large language model is used to perform semantic comparison on the current crash information and the crash information to be compared with the historical crash template as a reference case to obtain a single comparison result. In this way, the hallucination problem that may be caused by the large language model can be reduced to improve the accuracy of the single comparison result obtained using the large language model, thereby improving the reliability of the crash information processing method.

[0084] Based on the above examples, in the embodiment of the present disclosure, when a single comparison result indicates that the current crash information is a duplicated crash information relative to the crash information to be compared, the current crash template can also be used as a new candidate crash template and stored in the historical crash template library to expand the historical crash template library. In this way, in a specified future period, when the new current crash template obtained is similar to the current crash template this time, the current crash template this time can be selected from the historical crash template library as a historical crash template similar to the new current crash template, and subsequent steps can be performed, thereby improving the reliability of the crash information processing method.

[0085] Through the above method, in the embodiment of the present disclosure, each of the multiple historical crash information can be used as the crash information to be compared, and the large language model can be used to perform semantic comparison between the current crash information and the crash information to be compared to obtain a single comparison result, and based on the multiple single comparison results corresponding to the multiple historical crash information, the crash information comparison result is obtained. That is to say, in the embodiment of the present disclosure, the powerful text understanding and analysis capabilities of the large language model can be used to perform semantic comparison between the current crash information and the crash information to be compared to obtain a single comparison result, so as to accurately characterize whether the current crash information is a duplicated crash information relative to the crash information to be compared, thereby ensuring the reliability of the crash information processing method.

[0086] In some optional implementations, after executing step S101, step S103, and step S104, the crash information processing method provided by the embodiment of the present disclosure may further include:

[0087] When the crash information comparison result indicates that the current crash information is repeatedly reported crash information relative to the plurality of historical crash information, a repeat mark is added to the current crash information to obtain a first crash information marking result.

[0088] The repeat flag may be used to indicate whether the current crash information is the repeatedly reported crash information relative to any one of the multiple historical crash information.

[0089] Based on this, in one example, it can be determined whether the current crash information is a duplicated crash information relative to which historical crash information among multiple historical crash information, and the determined historical crash information is used as the target historical crash information, and then based on the information identifier of the target historical crash information (for example, the crash information name, the crash information serial number, etc.), a duplicate mark is added to the current crash information to obtain the first crash information marking result.

[0090] In some other optional implementations, after executing step S101, step S103, and step S104, the crash information processing method provided by the embodiment of the present disclosure may further include:

[0091] When the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, a non-repetitive mark is added to the current crash information to obtain a second crash information marking result.

[0092] Through the above method, in the embodiment of the present disclosure, when the crash information comparison result indicates that the current crash information is a repeatedly reported crash information relative to multiple historical crash information, a duplicate mark can be added to the current crash information to obtain a first crash information marking result; or, when the crash information comparison result indicates that the current crash information is not a repeatedly reported crash information relative to multiple historical crash information, a non-duplicate mark can be added to the current crash information to obtain a second crash information marking result, so as to provide developers with a clear basis for subsequent processing of the current crash information. For example, when the first crash information marking result is obtained, the current crash information can be deleted; when the second crash information marking result is obtained, the second crash information can be retained and continued to be analyzed. In this way, the cost of checking and deduplicating the current crash information for developers can be further reduced to improve the efficiency of solving stability problems for target applications.

[0093] The following will be combined Figure 2 , the integrity process of a crash information processing method provided by an embodiment of the present disclosure is described.

[0094] Step S201, obtaining the current crash stack of the target application.

[0095] For the detailed description of step S201, please refer to the relevant description of the corresponding part in the above step S101, which will not be repeated here.

[0096] Step S202: obtaining the stack information to be processed based on the key crash information and the function call chain in the current crash stack.

[0097] For the detailed description of step S202, please refer to the relevant description of the corresponding part in the above step S101, which will not be repeated here.

[0098] Step S203: vectorize the stack information to be processed to obtain current crash information.

[0099] For the detailed description of step S203, please refer to the relevant description of the corresponding part in the above step S101, which will not be repeated here.

[0100] Step S204: selecting a plurality of historical crash information similar to the current crash information from a plurality of candidate crash information stored in the historical crash information library.

[0101] For the detailed description of step S204, please refer to the relevant description of the corresponding part in the above step S102, which will not be repeated here.

[0102] Step S205 : semantically compare the current crash information with the plurality of historical crash information to obtain a crash information comparison result.

[0103] The crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the multiple historical crash information.

[0104] In one example, step S205, that is, "performing semantic comparison between current crash information and multiple historical crash information to obtain crash information comparison results" may include:

[0105] Step S205 - 1 , taking each of the multiple historical crash information as the crash information to be compared, assembling the current crash information and the crash information to be compared to obtain a current crash information group, and performing vectorization processing on the current crash information group to obtain a current crash template.

[0106] For the detailed description of step S205 - 1 , please refer to the relevant description of the corresponding part in the above step S103 , which will not be repeated here.

[0107] Step S205 - 2 , selecting a historical crash template similar to the current crash template from a plurality of candidate crash templates stored in the historical crash template library.

[0108] For the detailed description of step S205 - 2 , please refer to the relevant description of the corresponding part in the above step S103 , which will not be repeated here.

[0109] Step S205 - 3 , using the large language model and taking the historical crash template as a reference case, a semantic comparison is performed between the current crash information and the crash information to be compared, and a single comparison result is obtained.

[0110] For the detailed description of step S205 - 3 , please refer to the relevant description of the corresponding part in the above step S103 , which will not be repeated here.

[0111] Step S205 - 4 , obtaining a crash information comparison result based on a plurality of single item comparison results corresponding one-to-one to a plurality of historical crash information.

[0112] For the detailed description of step S205 - 4 , please refer to the relevant description of the corresponding part in the above step S103 , which will not be repeated here.

[0113] Further, in the disclosed embodiment, the single comparison result may be a first single comparison result, which is used to characterize that the current crash information is a repeatedly reported crash information relative to the crash information to be compared, that is, characterizes that the current crash information and the crash information to be compared correspond to the same stability problem of the target application; the single comparison result may also be a second single comparison result, which is used to characterize that the current crash information is not a repeatedly reported crash information relative to the crash information to be compared, that is, characterizes that the current crash information and the crash information to be compared correspond to different stability problems of the target application. Based on this, after obtaining multiple single comparison results corresponding to multiple historical crash information one by one, if the first single comparison result exists in the multiple single comparison results, it is determined that the current crash information is a repeatedly reported crash information relative to the multiple historical crash information; if the first single comparison result does not exist in the multiple single comparison results, it is determined that the current crash information is not a repeatedly reported crash information relative to the multiple historical crash information.

[0114] Step S206: if the crash information comparison result indicates that the current crash information is repeatedly reported crash information relative to the plurality of historical crash information, a repeat mark is added to the current crash information to obtain a first crash information marking result.

[0115] The repeat flag may be used to indicate whether the current crash information is the repeatedly reported crash information relative to any one of the multiple historical crash information.

[0116] Based on this, in one example, it can be determined whether the current crash information is a repeated reported crash information relative to which historical crash information among multiple historical crash information, and the determined historical crash information is used as the target historical crash information, and then based on the information identifier of the target historical crash information (for example, the crash information name, the crash information sequence number, etc.), a duplicate mark is added to the current crash information to obtain a first crash information marking result. After obtaining the first crash information marking result, the first crash information marking result can be stored or discarded, and the embodiments of the present disclosure do not limit this.

[0117] Step S207 : when the crash information comparison result indicates that the current crash information is duplicated crash information relative to the plurality of historical crash information, the current crash template is used as a new candidate crash template and stored in the historical crash template library.

[0118] It should be noted that in the embodiment of the present disclosure, step S206 and step S207 can be executed simultaneously or successively (for example, step S206 is executed first, and then step S207; for another example, step S207 is executed first, and then step S206), and the embodiment of the present disclosure does not limit this.

[0119] Step S208, when the crash information comparison result indicates that the current crash information is not the repeatedly reported crash information relative to the multiple historical crash information, a non-repeating mark is added to the current crash information to obtain a second crash information marking result, and the second crash information marking result is reported.

[0120] After reporting the second crash information marking result, the developer can locate and solve the stability problem corresponding to the second crash information marking result in the target application based on the second crash information marking result.

[0121] Step S209 , when the crash information comparison result indicates that the current crash information is not the repeatedly reported crash information relative to the multiple historical crash information, the current crash information is used as new candidate crash information and stored in the historical crash information library.

[0122] It should be noted that in the embodiment of the present disclosure, step S208 and step S209 can be executed simultaneously or successively (for example, step S208 is executed first, and then step S209; for another example, step S209 is executed first, and then step S208), and the embodiment of the present disclosure does not limit this.

[0123] See also Figure 3 , which is a scenario diagram of a crash information processing method provided in an embodiment of the present disclosure.

[0124] As mentioned above, the crash information processing method provided by the embodiment of the present disclosure is applied to an electronic device, wherein the electronic device may be a server, a workbench, a computer or other similar computing device.

[0125] Among them, electronic equipment is used for:

[0126] Get the current crash information of the target application;

[0127] Determine multiple historical crash information of the target application; wherein the multiple historical crash information corresponds to different stability issues of the target application;

[0128] The current crash information is semantically compared with the multiple historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the multiple historical crash information.

[0129] The current crash information may be the current crash stack of the target application, or may be a stack processing result obtained after processing the current crash stack of the target application (e.g., extracting feature data). Here, the target application is installed on a terminal device, and the terminal device may be a conventional computer (desktop computer, notebook computer, etc.), a car computer, a tablet computer, a smart phone, a personal digital assistant, or other similar computing devices.

[0130] It should be noted that in the embodiments of the present disclosure, Figure 3 The scene diagram shown is only illustrative and not restrictive. Those skilled in the art can Figure 3 Various obvious changes and / or substitutions are made to the examples, and the obtained technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0131] In order to better implement the crash information processing method, the embodiment of the present disclosure also provides a crash information processing device, which can be integrated in an electronic device. The electronic device can be a server, a workbench, a large computer or other similar computing device. Figure 4 The schematic structural block diagram shown is used to illustrate a crash information processing device 400 provided in the disclosed embodiment.

[0132] In the embodiment of the present disclosure, the crash information processing device 400 includes:

[0133] A first information acquisition unit 401 is used to acquire current crash information of a target application;

[0134] A second information acquisition unit 402 is used to determine a plurality of historical crash information of a target application; wherein the plurality of historical crash information corresponds to different stability problems of the target application;

[0135] The comparison result acquisition unit 403 is used to perform semantic comparison between the current crash information and the multiple historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the multiple historical crash information.

[0136] In some optional implementations, the comparison result acquisition unit 403 is used to:

[0137] Each of the multiple historical crash information is used as the crash information to be compared, and a large language model is used to perform semantic comparison between the current crash information and the crash information to be compared, so as to obtain a single comparison result; wherein the single comparison result is used to indicate whether the current crash information is a duplicated crash information relative to the crash information to be compared;

[0138] Based on a plurality of single item comparison results corresponding one-to-one to a plurality of historical crash information, a crash information comparison result is obtained.

[0139] In some optional implementations, the comparison result acquisition unit 403 is used to:

[0140] Based on the current crash information and the crash information to be compared, the current crash template is obtained;

[0141] Selecting a historical crash template similar to the current crash template from a plurality of candidate crash templates stored in a historical crash template library; wherein the historical crash template is obtained based on the first historical crash information and the second historical crash information, and the first historical crash information and the second historical crash information are crash information reported repeatedly;

[0142] Using a large language model and taking historical crash templates as reference cases, a semantic comparison is performed between the current crash information and the crash information to be compared to obtain a single comparison result.

[0143] In some optional implementations, the comparison result acquisition unit 403 is used to:

[0144] Assembling the current crash information and the crash information to be compared to obtain a current crash information group;

[0145] The current crash information group is vectorized to obtain the current crash template.

[0146] In some optional implementations, the crash information processing apparatus 400 further includes a template library expansion unit, which is used to:

[0147] When the single comparison result indicates that the current crash information is duplicated crash information compared with the crash information to be compared, the current crash template is used as a new candidate crash template and stored in the historical crash template library.

[0148] In some optional implementations, the first information acquisition unit 401 is used to:

[0149] Get the current crash stack of the target application;

[0150] Based on the key crash information and function call chain in the current crash stack, the stack information to be processed is obtained;

[0151] Vectorize the stack information to be processed to obtain the current crash information.

[0152] In some optional implementations, the second information acquisition unit 402 is used to:

[0153] A plurality of historical crash information similar to the current crash information is selected from a plurality of candidate crash information stored in a historical crash information library.

[0154] In some optional implementations, the second information acquisition unit 402 is used to:

[0155] Selecting a target number of candidate crash information having the greatest similarity to the current crash information from a plurality of candidate crash information stored in the historical crash information library as the historical crash information;

[0156] Alternatively, multiple candidate crash information whose similarity with the current crash information is greater than or equal to a preset threshold is selected from multiple candidate crash information stored in the historical crash information library as the historical crash information.

[0157] In some optional implementations, the crash information processing apparatus 400 further includes an information base expansion unit, which is used to:

[0158] When the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, the current crash information is used as new candidate crash information and stored in the historical crash information library.

[0159] In some optional implementations, the crash information processing apparatus 400 further includes a crash information processing unit, which is used to:

[0160] When the crash information comparison result indicates that the current crash information is repeatedly reported crash information relative to the plurality of historical crash information, a repeat mark is added to the current crash information to obtain a first crash information marking result;

[0161] Alternatively, when the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, a non-repetitive mark is added to the current crash information to obtain a second crash information marking result.

[0162] For the description of the specific functions and examples of each unit in the crash information processing device provided in the embodiment of the present disclosure, reference may be made to the relevant description of the corresponding steps in the aforementioned crash information processing method embodiment, which will not be repeated here.

[0163] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0164] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0165] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0166] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0167] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, for example, the crash information processing method. For example, in some embodiments, the crash information processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, for example, a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps in the crash information processing method described above may be executed. Alternatively, in other embodiments, the computing unit 501 may be configured as a crash information processing method in any other appropriate manner (eg, by means of firmware).

[0168] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0169] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0170] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM) or a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) display or a liquid crystal display (LCD)) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices are also used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0172] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), and the Internet.

[0173] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0174] The embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the crash information processing method.

[0175] The embodiment of the present disclosure also provides a computer program product, including a computer program, which implements the crash information processing method when executed by a processor.

[0176] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and this document is not limited here. In addition, in the present disclosure, relational terms such as "first", "second", "third", etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. In addition, "multiple" in the present disclosure can be understood as at least two.

[0177] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A crash information processing method, comprising: Get the current crash information of the target application; Determining a plurality of historical crash information of the target application; wherein the plurality of historical crash information corresponds to different stability problems of the target application; The current crash information is semantically compared with the plurality of historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is duplicated crash information relative to the plurality of historical crash information.

2. The method according to claim 1, wherein: The semantically comparing the current crash information with the plurality of historical crash information to obtain a crash information comparison result includes: Each of the plurality of historical crash information is used as the crash information to be compared, and a large language model is used to perform a semantic comparison between the current crash information and the crash information to be compared, to obtain a single comparison result; wherein the single comparison result is used to indicate whether the current crash information is a repeatedly reported crash information relative to the crash information to be compared; The crash information comparison result is obtained based on a plurality of single comparison results corresponding one-to-one to the plurality of historical crash information.

3. The method according to claim 2, wherein: The using of the large language model to perform semantic comparison on the current crash information and the crash information to be compared to obtain a single comparison result includes: Based on the current crash information and the crash information to be compared, a current crash template is obtained; Selecting a historical crash template similar to the current crash template from a plurality of candidate crash templates stored in a historical crash template library; wherein the historical crash template is obtained based on first historical crash information and second historical crash information, and the first historical crash information and the second historical crash information are crash information that are repeatedly reported; The large language model is used to perform semantic comparison on the current crash information and the crash information to be compared by taking the historical crash template as a reference case, so as to obtain the single comparison result.

4. The method according to claim 3, wherein: The obtaining a current crash template based on the current crash information and the crash information to be compared includes: Assembling the current crash information and the crash information to be compared to obtain a current crash information group; Vectorization processing is performed on the current crash information group to obtain the current crash template.

5. The method according to claim 3, further comprising: When the single comparison result indicates that the current crash information is duplicated crash information relative to the crash information to be compared, the current crash template is used as a new candidate crash template and stored in the historical crash template library.

6. The method according to any one of claims 1 to 5, wherein: The obtaining of the current crash information of the target application includes: Get the current crash stack of the target application; Based on the key crash information and the function call chain in the current crash stack, obtaining the stack information to be processed; Vectorization is performed on the stack information to be processed to obtain the current crash information.

7. The method according to any one of claims 1 to 5, wherein: The determining of a plurality of historical crash information of the target application comprises: The plurality of historical crash information similar to the current crash information is selected from the plurality of candidate crash information stored in the historical crash information library.

8. The method according to claim 7, wherein: The selecting the plurality of historical crash information similar to the current crash information from the plurality of candidate crash information stored in the historical crash information library comprises: Selecting a target number of candidate crash information having the greatest similarity to the current crash information from a plurality of candidate crash information stored in the historical crash information library as the historical crash information; Alternatively, multiple candidate crash information whose similarity with the current crash information is greater than or equal to a preset threshold is selected from multiple candidate crash information stored in the historical crash information library as the historical crash information.

9. The method according to claim 7, further comprising: When the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, the current crash information is taken as new candidate crash information and stored in the historical crash information library.

10. The method according to any one of claims 1 to 5, further comprising: When the crash information comparison result indicates that the current crash information is repeatedly reported crash information relative to the plurality of historical crash information, adding a repeat mark to the current crash information to obtain a first crash information marking result; Alternatively, when the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, a non-repetitive mark is added to the current crash information to obtain a second crash information marking result.

11. A crash information processing device, comprising: A first information acquisition unit, used to acquire current crash information of a target application; A second information acquisition unit, configured to determine a plurality of historical crash information of the target application; wherein the plurality of historical crash information corresponds to different stability problems of the target application; The comparison result acquisition unit is used to perform semantic comparison between the current crash information and the multiple historical crash information to obtain a crash information comparison result; wherein the crash information comparison result is used to indicate whether the current crash information is a repeatedly reported crash information relative to the multiple historical crash information.

12. The device according to claim 11, wherein The comparison result acquisition unit is used for: Each of the plurality of historical crash information is used as the crash information to be compared, and a large language model is used to perform a semantic comparison between the current crash information and the crash information to be compared, to obtain a single comparison result; wherein the single comparison result is used to indicate whether the current crash information is a repeatedly reported crash information relative to the crash information to be compared; The crash information comparison result is obtained based on a plurality of single comparison results corresponding one-to-one to the plurality of historical crash information.

13. The device according to claim 12, wherein: The comparison result acquisition unit is used for: Based on the current crash information and the crash information to be compared, a current crash template is obtained; Selecting a historical crash template similar to the current crash template from a plurality of candidate crash templates stored in a historical crash template library; wherein the historical crash template is obtained based on first historical crash information and second historical crash information, and the first historical crash information and the second historical crash information are crash information that are repeatedly reported; The large language model is used to perform semantic comparison on the current crash information and the crash information to be compared by taking the historical crash template as a reference case, so as to obtain the single comparison result.

14. The device according to claim 13, wherein: The comparison result acquisition unit is used for: Assembling the current crash information and the crash information to be compared to obtain a current crash information group; Vectorization processing is performed on the current crash information group to obtain the current crash template.

15. The device according to claim 13, further comprising a template library expansion unit, configured to: When the single comparison result indicates that the current crash information is duplicated crash information relative to the crash information to be compared, the current crash template is used as a new candidate crash template and stored in the historical crash template library.

16. The device according to any one of claims 11 to 15, wherein: The first information acquisition unit is used for: Get the current crash stack of the target application; Based on the key crash information and the function call chain in the current crash stack, obtaining the stack information to be processed; Vectorization is performed on the stack information to be processed to obtain the current crash information.

17. The device according to any one of claims 11 to 15, wherein: The second information acquisition unit is used for: The plurality of historical crash information similar to the current crash information is selected from the plurality of candidate crash information stored in the historical crash information library.

18. The device according to claim 17, wherein: The second information acquisition unit is used for: Selecting a target number of candidate crash information having the greatest similarity to the current crash information from a plurality of candidate crash information stored in the historical crash information library as the historical crash information; Alternatively, multiple candidate crash information whose similarity with the current crash information is greater than or equal to a preset threshold is selected from multiple candidate crash information stored in the historical crash information library as the historical crash information.

19. The device according to claim 17, further comprising an information base expansion unit, configured to: When the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, the current crash information is taken as new candidate crash information and stored in the historical crash information library.

20. The apparatus according to any one of claims 11 to 15, further comprising a crash information processing unit, configured to: When the crash information comparison result indicates that the current crash information is repeatedly reported crash information relative to the plurality of historical crash information, adding a repeat mark to the current crash information to obtain a first crash information marking result; Alternatively, when the crash information comparison result indicates that the current crash information is not repeatedly reported crash information relative to the plurality of historical crash information, a non-repeated mark is added to the current crash information to obtain a second crash information marking result.

21. An electronic device, comprising: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 10.

23. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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