Error link data classification method and device, equipment and medium

Abstract

The invention discloses an error link data classification method and device, equipment and a medium. The method comprises the following steps: standardizing a basic error link information set corresponding to a target application to obtain a standard error link information set which corresponds to the target application and contains each target error link information group; performing fingerprint information calculation on the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information, and performing signature information calculation on the target error link information group based on a preset signature information calculation rule to generate target signature information; and performing error classification on the basic error link information set based on the target fingerprint information and the target signature information corresponding to each target error link information group and a preset vector database to obtain an error classification result set corresponding to the target application. Through the technical scheme of the invention, the error link data can be efficiently and accurately classified, and the classification efficiency and accuracy of the error link data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method, apparatus, device and medium for classifying error link data. Background Art

[0002] In the process of software development and operation and maintenance, the link data in the call chain is usually an important means and basis for troubleshooting problems. However, with the expansion of the system scale and the increase in the number of user collections, the number of error link data has increased sharply, causing a large workload in the process of software development and operation and maintenance. Therefore, how to classify error link data has become very important.

[0003] In the prior art, an exact matching method or a simple text similarity calculation method is usually used for classifying error link data. However, if the classification method in the prior art is adopted, the same error may be identified as different classifications due to slight differences, resulting in repeated alarms. Moreover, relying on exact matching cannot effectively handle semantically similar errors, resulting in a decrease in accuracy.

[0004] In addition, in the process of large-scale data processing, the calculation complexity of using the traditional text similarity calculation method is relatively high, which is difficult to meet the real-time requirement and reduces the classification efficiency of error link data. Therefore, how to classify error link data efficiently and accurately and improve the classification efficiency and accuracy of error link data is an urgent problem to be solved at present. Summary of the Invention

[0005] The present invention provides a method, apparatus, device and medium for classifying error link data, which can solve the problem of low classification efficiency and accuracy of error link data.

[0006] According to one aspect of the present invention, there is provided a method for classifying error link data, including:

[0007] Obtaining a set of basic error link information corresponding to a target application, and performing standardized processing on the set of basic error link information to obtain a set of standard error link information corresponding to the target application; wherein, each target error link information group is included in the set of standard error link information;

[0008] Calculating fingerprint information for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group, and calculating signature information for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group;

[0009] Classify the basic error link information set based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain an error classification result set corresponding to the target application.

[0010] According to another aspect of the present invention, there is provided a classification device for error link data, including:

[0011] A data processing module, configured to obtain a basic error link information set corresponding to a target application, and perform normalization processing on the basic error link information set to obtain a standard error link information set corresponding to the target application; wherein, the standard error link information set contains each target error link information group;

[0012] An information calculation module, configured to calculate fingerprint information for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group, and calculate signature information for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group;

[0013] An error classification module, configured to classify the basic error link information set based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain an error classification result set corresponding to the target application.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the classification method for error link data according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the classification method for error link data according to any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, there is provided a computer program product, including a computer program, and the computer program implements the classification method for error link data according to any embodiment of the present invention when executed by a processor.

[0020] In the technical solution of the embodiment of the present invention, by standardizing the set of basic error link information corresponding to the target application, a standard error link information set corresponding to the target application including each target error link information group is obtained. Furthermore, based on a preset fingerprint information calculation rule, fingerprint information calculation is performed on the target error link information group to generate target fingerprint information corresponding to the target error link information group, and based on a preset signature information calculation rule, signature information calculation is performed on the target error link information group to generate target signature information corresponding to the target error link information group. Finally, based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database, error classification is performed on the set of basic error link information to obtain a set of error classification results corresponding to the target application. Since the fingerprint information, signature information, and vector database are combined to classify the error link data, the problem of low classification efficiency and accuracy of the error link data is solved, and the error link data can be classified efficiently and accurately, improving the classification efficiency and accuracy of the error link data.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0023] Figure 1 is a flowchart of a method for classifying error link data according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flowchart of a method for classifying error link data according to Embodiment 2 of the present invention;

[0025] Figure 3 is a schematic structural diagram of an apparatus for classifying error link data according to Embodiment 3 of the present invention;

[0026] Figure 4 is a schematic structural diagram of an electronic device for implementing the method for classifying error link data of the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", "target", "original", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] In the technical solution of this application, the acquisition, storage, use or processing of data, etc. all comply with the relevant provisions of national laws and regulations.

[0030] Embodiment 1

[0031] Figure 1 It is a flowchart of a method for classifying error link data provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of quickly classifying error link data, can support the real-time processing of a large number of error links, and is applicable to distributed systems and cloud computing environments. This method can be executed by a classification device for error link data. The classification device for error link data can be implemented in the form of hardware and / or software, and the classification device for error link data can be configured in an electronic device. As Figure 1 shown, the method includes:

[0032] S110. Obtain a set of basic error link information corresponding to a target application, and perform normalization processing on the set of basic error link information to obtain a set of standard error link information corresponding to the target application; wherein, each target error link information group is included in the set of standard error link information.

[0033] Among them, the target application may refer to an application program that needs to classify incorrect link data. Exemplarily, the target application can be selected according to actual application requirements. The link information may refer to the anomaly diagnosis information in the link data. Exemplarily, the link information can be stack trace, exception type, error message, or context information, etc. in the link data. The incorrect link information may refer to the link information corresponding to the incorrect link data. The incorrect link information group may refer to an array composed of each incorrect link information corresponding to the same incorrect link data. Usually, at least one incorrect link information is included in an incorrect link information group, and one incorrect link information group corresponds to one incorrect link data. The basic incorrect link information group may refer to the original incorrect link information group that has not been processed. The basic incorrect link information set may refer to a set composed of each basic incorrect link information group corresponding to the same target application.

[0034] Among them, the normalization process may refer to an operation of adjusting the basic incorrect link information set to make it conform to a unified data scale. Exemplarily, the normalization process can be removing line numbers, or removing noise information such as variable values. The standard incorrect link information group may refer to the incorrect link information group obtained after normalizing the basic incorrect link information group in the basic incorrect link information set. The standard incorrect link information set may refer to a set composed of each standard incorrect link information group corresponding to the same basic incorrect link information set. The target incorrect link information group may refer to the standard incorrect link information group selected from the standard incorrect link information set for subsequent operations. Exemplarily, the target incorrect link information group can be any standard incorrect link information group in the standard incorrect link information set.

[0035] S120. Calculate fingerprint information for the target incorrect link information group based on a preset fingerprint information calculation rule, generate target fingerprint information corresponding to the target incorrect link information group, calculate signature information for the target incorrect link information group based on a preset signature information calculation rule, and generate target signature information corresponding to the target incorrect link information group.

[0036] Among them, the fingerprint information may refer to a fixed-length unique identifier obtained by converting raw data of any length. The preset fingerprint information calculation rule may refer to a rule preset for calculating the fingerprint information of the target incorrect link information group. Exemplarily, the preset fingerprint information calculation rule can be any one of hash algorithms. For example, Message-Digest Algorithm 5 (MD5) or Secure Hash Algorithm 1 (SHA-1), etc. The target fingerprint information may refer to the fingerprint information corresponding to the target incorrect link information group calculated using the preset fingerprint information calculation rule.

[0037] Among them, the signature information may refer to an encrypted identifier generated through digital signature technology. Generally, the authenticity, integrity, and non-repudiation of the data source can be verified through the signature information. The preset signature information calculation rule may refer to a rule preset for calculating the signature information of the target error link information group. Exemplarily, the preset signature information calculation rule may be to calculate the signature information using the minimum hash value. The target signature information may refer to the signature information corresponding to the target error link information group calculated using the preset signature information calculation rule.

[0038] S130. Classify the errors in the basic error link information set based on the target fingerprint information, target signature information, and preset vector database corresponding to each target error link information group, and obtain an error classification result set corresponding to the target application.

[0039] Among them, the preset vector database may refer to a database preset for storing semantic vectors of error link data within a historical time period. Exemplarily, the preset vector database may include error link data within a historical time period and the corresponding semantic vectors. Generally, an error link data and the corresponding semantic vector are stored in pairs. The error classification result may refer to the classification result of the error link data. Generally, an error classification result includes error link data of the same error category. The error classification result set may refer to a set composed of each error classification result corresponding to the same target application.

[0040] In an optional implementation manner, after obtaining the error classification result set corresponding to the target application, it further includes: generating a basic problem record based on the classification quantity of the error classification results in the error classification result set and a preset problem record template; filling and processing the basic problem record based on the original link data set corresponding to the target application to generate a target problem record corresponding to the target application, and visually displaying the target problem record.

[0041] Among them, the number of classifications may refer to the number of misclassification results in the misclassification result set. Exemplarily, if the number of misclassification results in the misclassification result set is A, the number of classifications may be A. The preset problem record template may refer to a template preset for generating problem records. Exemplarily, the preset problem record template may include keyword fields of the problem record and field positions, etc. The basic problem record may refer to a problem record initially generated based on the preset problem record template. Exemplarily, the abnormal type or error information in the misclassification result, etc., may be filled into the preset problem record template to generate the basic problem record. Usually, the number of basic problem records is the same as the number of classifications, that is, each misclassification result corresponds to a basic problem record. The original link data may refer to the unprocessed error link data corresponding to the target application. The original link data set may refer to a set composed of each original link data corresponding to the same target application. The target problem record may refer to a problem record generated after filling the original link data in the original link data set into the basic problem record. Usually, the number of target problem records is the same as the number of basic problem records.

[0042] Specifically, after obtaining the misclassification result set corresponding to the target application, the same number of preset problem record templates as the number of classifications can be obtained according to the number of classifications of the misclassification results in the misclassification result set. Then, each misclassification result is filled into the corresponding field position of the preset problem record template to generate each basic problem record. Further, the original link data matching the basic problem record in the original link data set corresponding to the target application is obtained, and the original link data is filled into the basic problem record to generate the target problem record corresponding to the target application. Finally, each target problem record is visually displayed respectively. Thus, all the error link data can be classified and warned, providing an effective basis for the subsequent software development and operation and maintenance processes.

[0043] In the technical solution of the embodiment of the present invention, by standardizing the set of basic error link information corresponding to the target application, a standard error link information set including each target error link information group corresponding to the target application is obtained. Furthermore, based on the preset fingerprint information calculation rule, fingerprint information calculation is performed on the target error link information group to generate the target fingerprint information corresponding to the target error link information group. Based on the preset signature information calculation rule, signature information calculation is performed on the target error link information group to generate the target signature information corresponding to the target error link information group. Finally, based on the target fingerprint information, target signature information corresponding to each target error link information group, and the preset vector database, error classification is performed on the set of basic error link information to obtain the set of error classification results corresponding to the target application. Since the fingerprint information, signature information, and vector database are combined to classify the error link data, the problems of low classification efficiency and accuracy of the error link data are solved, and the error link data can be classified efficiently and accurately, improving the classification efficiency and accuracy of the error link data.

[0044] Embodiment 2

[0045] Figure 2 The flowchart of a method for classifying error link data provided by Embodiment 2 of the present invention is based on the above embodiment for refinement. In this embodiment, the operation of obtaining the set of basic error link information corresponding to the target application is specifically refined, and may specifically include: obtaining the set of original link data corresponding to the target application; based on the preset link judgment rule, performing link judgment on the set of original link data to determine the basic error link data in the set of original link data; based on the preset information extraction rule, performing information extraction on the basic error link data to obtain the basic error link information group corresponding to the basic error link data, and combining and processing each basic error link information group to obtain the set of basic error link information corresponding to the target application. As Figure 2 shown, the method includes:

[0046] S210. Obtain the set of original link data corresponding to the target application.

[0047] S220. Based on the preset link judgment rule, perform link judgment on the set of original link data to determine the basic error link data in the set of original link data.

[0048] Among them, the preset link judgment rule can refer to a rule preset for error judgment of the original link data. Usually, according to the preset link judgment rule, it can be determined whether the original link data in the original link data set is correct or incorrect. Exemplarily, the preset link judgment rule can be that if the original link data has an error code, it is determined that the original link data is incorrect link data. It can also be that if the error information in the original link data has a keyword field, it is determined that the original link data is incorrect link data. The embodiments of the present invention do not make specific limitations on this.

[0049] Among them, the incorrect link data can refer to the incorrect link data judged according to the preset link judgment rule. Usually, channel noise influence, hardware failure, protocol mechanism processing defects, error detection mechanism limitations or other factors may cause incorrect link data to be generated in the call chain. The basic incorrect link data can refer to the incorrect link data in the original unprocessed state.

[0050] S230. Extract information from the basic incorrect link data based on the preset information extraction rule to obtain a basic incorrect link information group corresponding to the basic incorrect link data, and combine and process each basic incorrect link information group to obtain a basic incorrect link information set corresponding to the target application.

[0051] Among them, the preset information extraction rule can refer to a rule preset for extracting link information from the basic incorrect link data. Exemplarily, the preset information extraction rule can be to extract link information such as stack trace, exception type, error information, and context information according to the information field name.

[0052] Specifically, after obtaining the original link data set corresponding to the target application, the preset link judgment rule can be first used to perform link judgment on each original link data in the original link data set, and the original link data of the error type is screened out as the basic incorrect link data. Furthermore, the preset information extraction rule is used to extract information from each basic incorrect link data respectively to obtain the link information corresponding to each basic incorrect link data, and the link information corresponding to the same basic incorrect link data is combined and processed to obtain a basic incorrect link information group corresponding to the basic incorrect link data. Finally, the basic incorrect link information groups corresponding to the same target application are combined and processed to obtain a basic incorrect link information set corresponding to the target application. Thus, an effective basis is provided for subsequent operations.

[0053] S240. Standardize the basic incorrect link information set to obtain a standard incorrect link information set corresponding to the target application; among them, each target incorrect link information group is included in the standard incorrect link information set.

[0054] In an optional implementation, the target error link information group may include at least one of a stack trace, an exception type, error information, and context information.

[0055] Specifically, after obtaining the set of basic error link information corresponding to the target application, the various basic error link information groups in the set of basic error link information can be standardized using a preset standardization processing rule to obtain the set of standard error link information corresponding to the target application. Exemplarily, taking the standardization processing rule as removing the line numbers and variable values in the stack trace and error information, and the stack trace in the basic error link information group being: File "app.py", line 10, in foo; bar(); File "app.py", line 20, in bar; raise ValueError("Invalidinput") as an example, the stack trace after standardization can be: File "app.py",, in foo; bar(); File "app.py",, in bar; raise ValueError("").

[0056] S250. If the target error link information group includes a stack trace, calculate fingerprint information for the stack trace based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group.

[0057] Specifically, after obtaining the set of standard error link information corresponding to the target application, if the target error link information group includes a stack trace. Then the standardized stack trace can be concatenated into a string. Exemplarily, following the above example, the standardized stack trace can be concatenated into the string "app.py:foo,app.py:bar,ValueError". Furthermore, the hash value of this string can be calculated using a preset fingerprint information calculation rule as the target fingerprint information corresponding to the target error link information group.

[0058] S260. If the target error link information group includes an exception type and error information and does not include a stack trace, calculate fingerprint information for the exception type and error information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group.

[0059] Specifically, if there is no stack trace in the target error link information group, the exception type and error message in the target error link information group can be concatenated into a string. Exemplarily, taking the exception type in the target error link information group as "ValueError" and the error message as "invalid input" as an example, it can be concatenated into the string "ValueError: Invalid input". Furthermore, the hash value of this string can be calculated using a preset fingerprint information calculation rule as the target fingerprint information corresponding to the target error link information group.

[0060] S270. If the target error link information group only contains context information, fingerprint information calculation is performed on the context information based on a preset fingerprint information calculation rule to generate the target fingerprint information corresponding to the target error link information group.

[0061] Specifically, if there is no stack trace, exception type, and error message in the target error link information group, the context information in the target error link information group can be concatenated into a string. Exemplarily, taking the context information in the target error link information group including database (DataBase, DB) request information, such as URL: / api / query / 1, request method GET, and status code 404 as an example, it can be concatenated into the string "GET: / api / query / 1:404". Furthermore, the hash value of this string can be calculated using a preset fingerprint information calculation rule as the target fingerprint information corresponding to the target error link information group.

[0062] It should be noted that if there is no stack trace, exception type, error message, and context information in the target error link information group, a preset default fingerprint, that is, a fixed hash value, can be used to indicate that the error link data corresponding to the target error link information group cannot be classified. Thus, the classification process solution can be improved, and the classification efficiency and accuracy of error link data can be enhanced.

[0063] S280. Perform a chunking process on the target error link information group based on a preset chunking rule to obtain a chunk set corresponding to the target error link information group.

[0064] Among them, the preset chunking rule can refer to a rule preset for defining the chunking process. Exemplarily, the preset chunking rule can be to perform a chunking process on the stack trace in the target error link information group according to data lines. Specifically, each stack trace data line can be divided into one chunk.

[0065] Among them, a block may refer to a data block obtained after block processing according to a preset block rule. A block set may refer to a set composed of each block corresponding to the same target error link information group. Usually, one target error link information group corresponds to one block set. If the stack trace is not included in the target error link information group, the block set may be empty.

[0066] S290. Calculate signature information for the target block in the block set based on a preset signature information calculation rule, generate a signature value set corresponding to the target block, and use the minimum signature value in the signature value set as the target signature value corresponding to the target block.

[0067] Among them, the target block may refer to the block selected in the block set for signature information calculation. Exemplarily, the target block may be any block in the block set. The signature value may refer to the hash value calculated using a preset signature information calculation rule. Usually, one target block may correspond to multiple signature values. The signature value set may refer to a set composed of multiple signature values corresponding to the same target block. Usually, one target block corresponds to one signature value set. The target signature value may refer to the minimum signature value in the signature value set.

[0068] S2100. Combine and process the target signature values corresponding to each target block in the same block set as the target signature information corresponding to the target error link information group.

[0069] Specifically, the stack trace in the target error link information group can be first chunked using a preset chunking rule to obtain a chunk set corresponding to the target error link information group. After that, each chunk in the chunk set is used as a target chunk, and the signature information calculation rule is used to calculate the signature information for each target chunk respectively, generating a signature value set corresponding to the target chunk. Exemplarily, taking the chunk set corresponding to the target error link information group containing four target chunks, namely target chunk 1, target chunk 2, target chunk 3, and target chunk 4, as an example. The hash function with different parameters can be used to calculate the hash values of the same target chunk, generating multiple hash values, and the multiple hash values corresponding to the same target chunk are combined to generate a signature value set. For example, if target chunk 1 corresponds to four hash values, hash11, hash12, hash13, and hash14, target chunk 2 corresponds to four hash values, hash21, hash22, hash23, and hash24, target chunk 3 corresponds to four hash values, hash31, hash32, hash33, and hash34, and target chunk 4 corresponds to four hash values, hash41, hash42, hash43, and hash44. Then, the four hash values, hash11, hash12, hash13, and hash14, can be combined to generate the signature value set 1 (hash11, hash12, hash13, hash14) corresponding to target chunk 1; the four hash values, hash21, hash22, hash23, and hash24, can be combined to generate the signature value set 2 (hash21, hash22, hash23, hash24) corresponding to target chunk 2; the four hash values, hash31, hash32, hash33, and hash34, can be combined to generate the signature value set 3 (hash31, hash32, hash33, hash34) corresponding to target chunk 3; the four hash values, hash41, hash42, hash43, and hash44, can be combined to generate the signature value set 4 (hash41, hash42, hash43, hash44) corresponding to target chunk 4. Further, the minimum signature value in the signature value set is used as the target signature value corresponding to the target chunk. Continuing with the above example, taking the minimum hash value in signature value set 1 as hash11, the minimum hash value in signature value set 2 as hash22, the minimum hash value in signature value set 3 as hash33, and the minimum hash value in signature value set 4 as hash44 as an example. Then, hash11 can be used as the target signature value corresponding to target chunk 1, hash22 can be used as the target signature value corresponding to target chunk 2, hash33 can be used as the target signature value corresponding to target chunk 3, and hash44 can be used as the target signature value corresponding to target chunk 4.Finally, combine and process the target signature values corresponding to each target block in the same block set as the target signature information corresponding to the target error link information group. Continuing with the above example, the target signature information corresponding to the target error link information group can be [hash11, hash22, hash33, hash44]. Thus, it is possible to solve the problem that stack traces can sometimes be very long and the same error stack can lead to subtle differences, resulting in different hash values and thus causing problems with error classification.

[0070] It should be noted that in the embodiments of the present invention, the calculation process of fingerprint information and the calculation process of signature information can be executed in parallel or sequentially, and the embodiments of the present invention do not make specific limitations on this.

[0071] S2110. Perform error classification on the basic error link information set based on the target fingerprint information corresponding to each target error link information group to generate a first basic error classification set and a first remaining error classification set.

[0072] Among them, basic error classification can refer to the initially formed error classification result. Generally, each target error link information group in the same classification category is included in one basic error classification. The basic error classification set can refer to the set composed of each basic error classification corresponding to the same target application. The first basic error classification set can refer to the basic error classification set obtained after performing error classification using fingerprint information.

[0073] Among them, the remaining error classification can refer to the initially formed remaining classification result. Generally, each target error link information group that has not been classified yet can be included in the remaining error classification. The remaining error classification set can refer to the set composed of each remaining error classification corresponding to the same target application. The first remaining error classification set can refer to the remaining error classification set obtained after performing error classification using fingerprint information.

[0074] Specifically, after obtaining the target fingerprint information corresponding to the target error link information group, the target fingerprint information can be numerically compared. Furthermore, each target error link information group with the same target fingerprint information is divided into one error classification to form one basic error classification. Further, each basic error classification is combined to generate a first basic error classification set, and all target error link information groups except the first basic error classification set are combined to form a first remaining error classification set. Thus, error classification using fingerprint information is realized.

[0075] S2120. Perform error classification on the first remaining error classification set based on the target signature information corresponding to each target error link information group to generate a second basic error classification set and a second remaining error classification set.

[0076] Among them, the second basic error classification set may refer to the basic error classification set obtained after error classification using signature information. The second remaining error classification set may refer to the remaining error classification set obtained after error classification using signature information.

[0077] In an optional implementation manner, error classification is performed on the first remaining error classification set based on the target signature information corresponding to each target error link information group, generating a second basic error classification set and a second remaining error classification set, including: obtaining a target remaining link information group in the first remaining error classification set and a target basic link information group in the first basic error classification set; numerically comparing the target signature information of the target remaining link information group and the target signature information of the target basic link information group, generating a numerical comparison result; numerically judging the numerical comparison result based on a preset signature threshold. If the numerical comparison result meets the preset signature threshold, then taking the target remaining link information group as a second basic error link information group; if the numerical comparison result does not meet the preset signature threshold, then taking the target remaining link information group as a second remaining error link information group; combining and processing each second basic error link information group based on the numerical comparison result to generate a second basic error classification set, and combining and processing each second remaining error link information group to generate a second remaining error classification set.

[0078] Among them, the target remaining link information group may refer to the error link information group selected from the first remaining error classification set for error classification. Exemplarily, the target remaining link information group may be any error link information group in the first remaining error classification set. The target basic link information group may refer to the error link information group selected from the first basic error classification set for assisting error classification. Exemplarily, the target basic link information group may be any error link information group in the first basic error classification set. The numerical comparison result may refer to the ratio result of the same data value between the target signature information of the target remaining link information group and the target signature information of the target basic link information group. Exemplarily, the numerical comparison result may be the ratio of the signature information that is the same as the target signature information of the target basic link information group in the target signature information of the target remaining link information group. For example, if the target signature information of the target remaining link information group is [hash1, hash2, hash3, hash4] and the target signature information of the target basic link information group is [hash1, hash2, hash3, hash5], then the numerical comparison result may be the ratio of hash1, hash2, and hash3 in the target signature information [hash1, hash2, hash3, hash4], that is, 75%. The preset signature threshold may refer to a preset value used to evaluate the numerical comparison result. Exemplarily, the preset signature threshold may be 50%. Generally, the preset signature threshold can reflect the similarity degree between the target remaining link information group and the target basic link information group. If the numerical comparison result exceeds the preset signature threshold, it indicates that the target remaining link information group and the target basic link information group are similar. Otherwise, they are not similar. The second basic error link information group may refer to the error link information group that can be classified after error classification using signature information. The second remaining error link information group may refer to the error link information group that still cannot be classified after error classification using signature information.

[0079] Specifically, after generating the first basic error classification set and the first remaining error classification set, any error link information group in the first remaining error classification set can be obtained as the target remaining link information group and any error link information group in the first basic error classification set as the target basic link information group. Furthermore, a numerical comparison is made between the target signature information of the target remaining link information group and the target signature information of the target basic link information group to generate a numerical comparison result. Further, a numerical judgment is made on this numerical comparison result using a preset signature threshold. If the numerical comparison result exceeds the preset signature threshold, the target remaining link information group is taken as the second basic error link information group. Conversely, if the numerical comparison result is lower than the preset signature threshold, the target remaining link information group is taken as the second remaining error link information group. Finally, according to the numerical comparison result, each second basic error link information group of the same category is combined respectively to generate a second basic error classification, and each second basic error classification is combined to generate a second basic error classification set, and each second remaining error link information group is combined to generate a second remaining error classification set. Thus, error classification using signature information is realized.

[0080] S2130. Based on a preset vector database, error classification is performed on the second remaining error classification set to generate a third basic error classification set.

[0081] Specifically, after generating the second remaining error classification set, a pre-selected text embedding model can be used to perform semantic conversion on each error link data group in the second basic error classification set to generate an error semantic vector corresponding to each error link data group. Furthermore, semantic query is performed in the preset vector database using this error semantic vector. If there is a stored semantic vector consistent with this error semantic vector in the preset vector database, each error link data group is combined using the error classification corresponding to the stored semantic vector to generate a third basic error classification. Finally, each third basic error classification is combined to generate a third basic error classification set. Thus, through vectorized calculation, the time and space complexity of similarity calculation are significantly reduced, and the efficiency of error classification is improved.

[0082] S2140. Combine and process the first basic error classification set, the second basic error classification set, and the third basic error classification set to obtain an error classification result set corresponding to the target application.

[0083] Specifically, after generating the first basic error classification set, the second basic error classification set, and the third basic error classification set, the basic error classifications with the same error classification can be merged. For example, the first basic error classification that meets the preset signature threshold is merged with the second basic error classification. Thus, the final error classification result set is obtained.

[0084] In the technical solution of the embodiment of the present invention, the original link data set corresponding to the target application is judged by a preset link judgment rule to determine the basic error link data in the original link data set, and information extraction is performed on the basic error link data based on a preset information extraction rule to obtain a basic error link information group corresponding to the basic error link data. Each basic error link information group is combined and processed to obtain a basic error link information set corresponding to the target application. The basic error link information set is standardized to obtain a standard error link information set corresponding to the target application. Furthermore, if the target error link information group in the standard error link information set contains a stack trace, fingerprint information calculation is performed on the stack trace based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group. If the target error link information group contains an exception type and error information and does not contain a stack trace, fingerprint information calculation is performed on the exception type and error information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group. If the target error link information group only contains context information, fingerprint information calculation is performed on the context information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group. At the same time, the target error link information group is block-processed based on a preset block rule to obtain a block set corresponding to the target error link information group. Signature information calculation is performed on the target block in the block set based on a preset signature information calculation rule to generate a signature value set corresponding to the target block, and the minimum signature value in the signature value set is used as the target signature value corresponding to the target block. The target signature values corresponding to each target block in the same block set are combined and processed as the target signature information corresponding to the target error link information group. Further, error classification is performed on the basic error link information set based on the target fingerprint information corresponding to each target error link information group to generate a first basic error classification set and a first remaining error classification set. Error classification is performed on the first remaining error classification set based on the target signature information corresponding to each target error link information group to generate a second basic error classification set and a second remaining error classification set. Error classification is performed on the second remaining error classification set based on a preset vector database to generate a third basic error classification set. Finally, the first basic error classification set, the second basic error classification set, and the third basic error classification set are combined and processed to obtain an error classification result set corresponding to the target application. Since the error link data is classified by combining fingerprint information, signature information, and a vector database, the problem of low classification efficiency and accuracy of the error link data is solved, and the error link data can be classified efficiently and accurately, improving the classification efficiency and accuracy of the error link data.

[0085] Embodiment III

[0086] Figure 3 This is a schematic structural diagram of a classification device for error link data provided in Embodiment 3 of the present invention. As Figure 3 shown, the device includes: a data processing module 310, an information calculation module 320, and an error classification module 330;

[0087] Among them, the data processing module 310 is used to obtain a set of basic error link information corresponding to the target application, and standardize the set of basic error link information to obtain a set of standard error link information corresponding to the target application; wherein, each target error link information group is included in the set of standard error link information;

[0088] The information calculation module 320 is used to calculate fingerprint information for the target error link information group based on a preset fingerprint information calculation rule, generate target fingerprint information corresponding to the target error link information group, calculate signature information for the target error link information group based on a preset signature information calculation rule, and generate target signature information corresponding to the target error link information group;

[0089] The error classification module 330 is used to perform error classification on the set of basic error link information based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database, and obtain a set of error classification results corresponding to the target application.

[0090] The technical solution of the embodiment of the present invention standardizes the set of basic error link information corresponding to the target application to obtain a set of standard error link information corresponding to the target application, which includes each target error link information group. Furthermore, fingerprint information is calculated for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group, and signature information is calculated for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group. Finally, error classification is performed on the set of basic error link information based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain a set of error classification results corresponding to the target application. Since the fingerprint information, signature information, and vector database are combined to classify the error link data, the problems of low classification efficiency and accuracy of the error link data are solved, and the error link data can be classified efficiently and accurately, improving the classification efficiency and accuracy of the error link data.

[0091] Optionally, the data processing module 310 may specifically be used for:

[0092] Obtain a set of original link data corresponding to the target application;

[0093] Perform link judgment on the original link data set based on a preset link judgment rule to determine the basic error link data in the original link data set;

[0094] Extract information from the basic error link data based on a preset information extraction rule to obtain a basic error link information group corresponding to the basic error link data, and combine and process each basic error link information group to obtain a basic error link information set corresponding to the target application.

[0095] Optionally, the target error link information group includes at least one of stack trace, exception type, error message, and context information;

[0096] The information calculation module 320 can specifically be used for:

[0097] If the stack trace is included in the target error link information group, calculate fingerprint information for the stack trace based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group;

[0098] If the exception type and error message are included in the target error link information group and the stack trace is not included, calculate fingerprint information for the exception type and error message based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group;

[0099] If only the context information is included in the target error link information group, calculate fingerprint information for the context information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group.

[0100] Optionally, the information calculation module 320 can specifically be used for:

[0101] Perform block processing on the target error link information group based on a preset block rule to obtain a block set corresponding to the target error link information group;

[0102] Calculate signature information for the target blocks in the block set based on a preset signature information calculation rule to generate a signature value set corresponding to the target blocks, and use the minimum signature value in the signature value set as the target signature value corresponding to the target blocks;

[0103] Combine and process the target signature values corresponding to each target block in the same block set as the target signature information corresponding to the target error link information group.

[0104] Optionally, the error classification module 330 can specifically be used for:

[0105] Classify the basic error link information set based on the target fingerprint information corresponding to each target error link information group to generate a first basic error classification set and a first remaining error classification set;

[0106] Classify the first remaining error classification set based on the target signature information corresponding to each target error link information group to generate a second basic error classification set and a second remaining error classification set;

[0107] Classify the second remaining error classification set based on a preset vector database to generate a third basic error classification set;

[0108] Combine and process the first basic error classification set, the second basic error classification set, and the third basic error classification set to obtain an error classification result set corresponding to the target application.

[0109] Optionally, the error classification module 330 can specifically be used for:

[0110] Obtain the target remaining link information group in the first remaining error classification set and the target basic link information group in the first basic error classification set;

[0111] Numerically compare the target signature information of the target remaining link information group and the target signature information of the target basic link information group to generate a numerical comparison result;

[0112] Based on a preset signature threshold, perform a numerical judgment on the numerical comparison result. If the numerical comparison result meets the preset signature threshold, use the target remaining link information group as the second basic error link information group; if the numerical comparison result does not meet the preset signature threshold, use the target remaining link information group as the second remaining error link information group;

[0113] Based on the numerical comparison result, combine and process each second basic error link information group to generate a second basic error classification set, and combine and process each second remaining error link information group to generate a second remaining error classification set.

[0114] Optionally, the classification device for error link data may further include: a problem record generation module, which is used to, after obtaining the error classification result set corresponding to the target application, generate a basic problem record based on the classification quantity of the error classification results in the error classification result set and a preset problem record template; fill and process the basic problem record based on the original link data set corresponding to the target application to generate a target problem record corresponding to the target application, and visually display the target problem record.

[0115] The classification device for error link data provided by the embodiments of the present invention can execute the classification method for error link data provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0116] Embodiment 4

[0117] Figure 4 FIG. shows a schematic structural diagram of an electronic device 410 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, 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, for example, personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0118] As Figure 4 shown, the electronic device 410 includes at least one processor 420, and a memory communicatively connected to the at least one processor 420, such as a read-only memory (ROM) 430, a random access memory (RAM) 440, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 420 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 430 or the computer program loaded from the storage unit 490 into the random access memory (RAM) 440. In the RAM 440, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 420, the ROM 430, and the RAM 440 are connected to each other through a bus 450. The input / output (I / O) interface 460 is also connected to the bus 450.

[0119] Multiple components in the electronic device 410 are connected to the I / O interface 460, including: an input unit 470, such as a keyboard, a mouse, etc.; an output unit 480, such as various types of displays, speakers, etc.; a storage unit 490, such as a magnetic disk, an optical disk, etc.; and a communication unit 4100, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 4100 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 420 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 420 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 420 executes the various methods and processes described above, such as the method for classifying error link data.

[0121] The method includes:

[0122] Obtaining a set of basic error link information corresponding to a target application, and performing normalization processing on the set of basic error link information to obtain a set of standard error link information corresponding to the target application; wherein, each target error link information group is included in the set of standard error link information;

[0123] Calculating fingerprint information for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group, and calculating signature information for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group;

[0124] Performing error classification on the set of basic error link information based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain a set of error classification results corresponding to the target application.

[0125] In some embodiments, the method for classifying error link data may be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 490. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 410 via the ROM 430 and / or the communication unit 4100. When the computer program is loaded into the RAM 440 and executed by the processor 420, one or more steps of the method for classifying error link data described above may be executed. Alternatively, in other embodiments, the processor 420 may be configured to execute the method for classifying error link data in any other suitable manner (e.g., by means of firmware).

[0126] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0127] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be 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).

[0130] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0131] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0132] An embodiment of the present application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the classification method for error link data provided in any embodiment of the present application. This program product and the classification method for error link data disclosed in each embodiment of the present application belong to the same inventive concept, and thus will not be elaborated herein.

[0133] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0134] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A classification method for error link data, characterized in that, Including: Obtain the set of basic error link information corresponding to the target application, and standardize the set of basic error link information to obtain the set of standard error link information corresponding to the target application; wherein, each target error link information group is included in the set of standard error link information. Calculate fingerprint information for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group, and calculate signature information for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group. Classify errors for the set of basic error link information based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain the set of error classification results corresponding to the target application.

2. The method according to claim 1, characterized in that The obtaining of the set of basic error link information corresponding to the target application includes: Obtain the set of original link data corresponding to the target application. Perform link judgment on the set of original link data based on a preset link judgment rule to determine the basic error link data in the set of original link data. Extract information from the basic error link data based on a preset information extraction rule to obtain the basic error link information group corresponding to the basic error link data, and perform combined processing on each basic error link information group to obtain the set of basic error link information corresponding to the target application.

3. The method according to claim 1, wherein The target error link information group includes at least one of stack trace, exception type, error information, and context information. The calculating of fingerprint information for the target error link information group based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group includes: If the stack trace is included in the target error link information group, calculate fingerprint information for the stack trace based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group. If the exception type and error information are included in the target error link information group and the stack trace is not included, calculate fingerprint information for the exception type and error information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group. If only context information is included in the target error link information group, calculate fingerprint information for the context information based on a preset fingerprint information calculation rule to generate target fingerprint information corresponding to the target error link information group.

4. The method according to claim 1, characterized in that, The calculating of signature information for the target error link information group based on a preset signature information calculation rule to generate target signature information corresponding to the target error link information group includes: Perform block processing on the target error link information group based on a preset block rule to obtain the block set corresponding to the target error link information group. Calculate signature information for the target block in the block set based on a preset signature information calculation rule to generate the signature value set corresponding to the target block, and use the minimum signature value in the signature value set as the target signature value corresponding to the target block. Combine and process the target signature values corresponding to each target block in the same block set as the target signature information corresponding to the target error link information group.

5. The method according to claim 1, wherein Classify the errors in the basic error link information set based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database to obtain an error classification result set corresponding to the target application, including: Classify the errors in the basic error link information set based on the target fingerprint information corresponding to each target error link information group to generate a first basic error classification set and a first remaining error classification set; Classify the errors in the first remaining error classification set based on the target signature information corresponding to each target error link information group to generate a second basic error classification set and a second remaining error classification set; Classify the errors in the second remaining error classification set based on the preset vector database to generate a third basic error classification set; Combine and process the first basic error classification set, the second basic error classification set, and the third basic error classification set to obtain an error classification result set corresponding to the target application.

6. The method according to claim 5, wherein The step of classifying the errors in the first remaining error classification set based on the target signature information corresponding to each target error link information group to generate a second basic error classification set and a second remaining error classification set includes: Obtain the target remaining link information group in the first remaining error classification set and the target basic link information group in the first basic error classification set; Compare the target signature information of the target remaining link information group and the target signature information of the target basic link information group numerically to generate a numerical comparison result; Make a numerical judgment on the numerical comparison result based on a preset signature threshold. If the numerical comparison result meets the preset signature threshold, use the target remaining link information group as the second basic error link information group; if the numerical comparison result does not meet the preset signature threshold, use the target remaining link information group as the second remaining error link information group; Based on the numerical comparison result, combine and process each second basic error link information group to generate a second basic error classification set, and combine and process each second remaining error link information group to generate a second remaining error classification set.

7. The method according to claim 1, characterized in that After obtaining the error classification result set corresponding to the target application, it further includes: Generate a basic problem record based on the classification quantity of the error classification results in the error classification result set and a preset problem record template; Fill and process the basic problem record based on the original link data set corresponding to the target application to generate a target problem record corresponding to the target application, and visually display the target problem record.

8. An apparatus for classifying error link data, characterized in that, Including: A data processing module, configured to obtain a basic error link information set corresponding to a target application, and perform a normalization process on the basic error link information set to obtain a standard error link information set corresponding to the target application; wherein, the standard error link information set includes each target error link information group. An information calculation module, configured to calculate fingerprint information for the target error link information group based on a preset fingerprint information calculation rule, generate target fingerprint information corresponding to the target error link information group, calculate signature information for the target error link information group based on a preset signature information calculation rule, and generate target signature information corresponding to the target error link information group; An error classification module, configured to classify errors in the basic error link information set based on the target fingerprint information, target signature information corresponding to each target error link information group, and a preset vector database, and obtain an error classification result set corresponding to the target application.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the error link data classification method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement the error link data classification method according to any one of claims 1-7 when executed.

Images