Application program log intelligent processing method and device, equipment and medium

Abstract

The invention discloses an application program log intelligent processing method and device, equipment and a medium. The method comprises the steps that terminal fault information corresponding to a program fault reporting instruction is acquired; if it is determined that the terminal fault information passes the integrity verification, log pulling configuration information is generated based on the terminal fault information and sent to the user terminal; receiving log report data sent by the user terminal; based on a pre-trained log classification model, obtaining a target classification result corresponding to the log report data based on the log classification model; and obtaining a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and sending the target troubleshooting strategy to the user terminal. According to the embodiment of the invention, the terminal fault information of the user terminal can be obtained in time, the log pulling configuration information can be automatically generated, the fault type and the corresponding fault removal strategy can be quickly determined after the corresponding log reporting data is obtained, and the fault removal strategy is sent to carry out fault removal in time.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment and medium for intelligent processing of application logs. Background Art

[0002] Currently, medium-to-large applications (APPs) receive a variety of user feedback online, including app crashes, errors, and lags. These issues involve the app's native code, web pages hosted within the app, and React Native pages. For these issues, developers assisting with troubleshooting may not be able to simply reproduce the problem; instead, they often need to rely on user operation logs to help locate the problem.

[0003] Currently, when an application on a user terminal fails, the log reporting process is as follows:

[0004] 1) Users discover a fault while using the app, which affects their use of the app;

[0005] 2) The user seeks help from customer service personnel through customer service telephone or other means and reports problems related to the fault;

[0006] 3) Customer service staff enters problem information based on user feedback;

[0007] 4) Customer service staff reports fault-related issues to the APP developers;

[0008] 5) The developer determines whether user logs are needed to help locate the problem. If so, they inform customer service and contact the user to upload the logs.

[0009] 6) Customer service staff contacts the user and instructs the user to manually upload the log;

[0010] 7) The user manually uploads the log according to the guidance of the customer service staff;

[0011] 8) Developers obtain and analyze the logs uploaded by users from the log package management background to obtain troubleshooting data and send it to users for troubleshooting.

[0012] When obtaining application-related logs from user terminals through the above method, if customer service personnel fail to contact the corresponding user in a timely manner to successfully upload the logs manually, or if the user fails to successfully upload the logs due to complex operations under the guidance of customer service personnel, the user's fault problem will not be resolved in a timely manner, reducing the efficiency of application troubleshooting. Summary of the Invention

[0013] Embodiments of the present invention provide an intelligent processing method, apparatus, device, and medium for application logs, aiming to solve the problem in the prior art that, when obtaining application-related logs from a user terminal, if customer service personnel fail to contact the corresponding user in a timely manner to manually upload the log successfully, or if the user fails to successfully upload the log due to complex operations under the guidance of customer service personnel, the user's fault problem cannot be handled in a timely manner, thereby reducing the efficiency of application troubleshooting.

[0014] In a first aspect, an embodiment of the present invention provides a method for intelligently processing application logs, comprising:

[0015] In response to a program fault reporting instruction from a user terminal, obtaining terminal fault information corresponding to the program fault reporting instruction;

[0016] If it is determined that the terminal fault information passes the integrity verification, generating log pulling configuration information based on the terminal fault information, and sending the log pulling configuration information to the user terminal;

[0017] Receiving log reporting data corresponding to the log pulling configuration information sent by the user terminal;

[0018] Based on a pre-trained log classification model, obtaining a target classification result corresponding to the log reporting data based on the log classification model; and

[0019] A target troubleshooting strategy corresponding to the target classification result is obtained based on a preset troubleshooting strategy, and the target troubleshooting strategy is sent to the user terminal, so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

[0020] In a second aspect, an embodiment of the present invention further provides an application log intelligent processing device, which includes:

[0021] a fault information acquiring unit, configured to respond to a program fault reporting instruction from a user terminal and acquire terminal fault information corresponding to the program fault reporting instruction;

[0022] a log pulling configuration information generating unit, configured to generate log pulling configuration information based on the terminal fault information if it is determined that the terminal fault information passes the integrity verification, and send the log pulling configuration information to the user terminal;

[0023] A log reporting data receiving unit, configured to receive log reporting data corresponding to the log pulling configuration information sent by the user terminal;

[0024] a log classification unit, configured to obtain, based on a pre-trained log classification model, a target classification result corresponding to the log reporting data; and

[0025] A target troubleshooting strategy acquisition unit is used to acquire a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and send the target troubleshooting strategy to the user terminal so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

[0026] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0027] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method described in the first aspect can be implemented.

[0028] The embodiment of the present invention provides an application log intelligent processing method, apparatus, device and medium, the method comprising: in response to a program fault reporting instruction of a user terminal, obtaining terminal fault information corresponding to the program fault reporting instruction; if it is determined that the terminal fault information passes integrity verification, generating log pull configuration information based on the terminal fault information, and sending the log pull configuration information to the user terminal; receiving log report data corresponding to the log pull configuration information sent by the user terminal; based on a pre-trained log classification model, obtaining a target classification result corresponding to the log report data based on the log classification model; and obtaining a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and sending the target troubleshooting strategy to the user terminal. In the embodiment of the present invention, the terminal fault information of the user terminal can be obtained in a timely manner and log pull configuration information can be automatically generated, and after obtaining the corresponding log report data, the fault type and the corresponding troubleshooting strategy can be quickly determined, and the troubleshooting strategy can be sent to timely troubleshoot the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 A schematic diagram of an application scenario of the method for intelligently processing application logs provided by an embodiment of the present invention;

[0031] Figure 2 A flow chart of an intelligent application log processing method according to an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of a sub-process of an application log intelligent processing method provided by an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of a sub-process of an application log intelligent processing method provided by an embodiment of the present invention;

[0034] Figure 5 A schematic diagram of a sub-process of an application log intelligent processing method provided by an embodiment of the present invention;

[0035] Figure 6 A schematic block diagram of an application log intelligent processing device provided by an embodiment of the present invention;

[0036] Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0038] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0039] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0041] Please also see Figure 1 and Figure 2 , Figure 1 Schematic diagram of a scenario of an intelligent processing method for application logs according to an embodiment of the present invention. Figure 2FIG. 1 is a flow chart of an application log intelligent processing method provided by an embodiment of the present invention. The method is applied to a server 10, which can be connected to a user terminal 20 for communication. Figure 2 As shown, the method includes the following steps S110-S150.

[0042] S110 . In response to a program fault reporting instruction from a user terminal, obtain terminal fault information corresponding to the program fault reporting instruction.

[0043] In this embodiment, the technical solution is described with the server as the execution subject. The server can be regarded as a server cluster and at least includes a customer service server (whose main users are customer service personnel) and a backend server (whose main users are developers). When a user encounters a fault in an application on a user terminal, if the fault affects the subsequent normal use of the application and needs to be promptly troubleshooted, the user can manually click the troubleshooting virtual button on the user interaction interface to generate a program fault reporting instruction, or the user terminal can automatically generate a program fault reporting instruction when it detects that the application has a running fault in the background. When the program fault reporting instruction is sent to the customer service server in the server, a terminal fault information acquisition instruction is automatically generated by the customer service server and sent to the user terminal. After that, the user terminal automatically obtains the corresponding terminal fault information according to the terminal fault information acquisition instruction and feeds it back to the customer service server. After receiving the terminal fault information, the customer service server can perform timely analysis and processing to assist the user in quickly troubleshooting. Among them, the terminal fault information acquisition instruction is executed on the user terminal to obtain the device model, system version information, device UID information (the full name of UID is User Identification, which means user identity verification) and fault image / video information, and when the fault image / video information is obtained, a pop-up window can be displayed to prompt the user to record or take a screenshot of the fault interface of the application.

[0044] In one embodiment, after step S110, the method further includes:

[0045] Obtain the device model, system version information, device UID information, and fault image / video information included in the terminal fault information;

[0046] If it is determined that the device model, the system version information, the device UID information, and the fault image / video information are all non-null value information, then it is determined that the terminal fault information passes the integrity verification;

[0047] If it is determined that there is null value information in the device model, the system version information, the device UID information, and the fault image / video information, the information corresponding to the null value is obtained as abnormality verification information, and the abnormality verification information is sent to the user terminal.

[0048] In this embodiment, to enhance the data security of terminal fault information, the terminal fault information in plaintext may be encrypted on the user terminal to update the terminal fault information, and then the ciphertext terminal fault information is sent to the customer service server. The customer service server decrypts the ciphertext terminal fault information to obtain the plaintext terminal fault information, parses the terminal fault information, obtains the device model, system version information, device UID information, and fault image / video information included therein, and then performs an integrity check on the information. If it is determined that the device model, system version information, device UID information, and fault image / video information are all non-null values, the terminal fault information is determined to have passed the integrity check, and the customer service server can then perform corresponding troubleshooting based on the terminal fault information. If it is determined that any of the device model, system version information, device UID information, and fault image / video information contain null values, the corresponding null value information is obtained as exception verification information and sent to the user terminal to prompt the user of the user terminal to manually fill in the missing information corresponding to the exception verification information in a timely manner, thereby completing the terminal fault information.

[0049] In one embodiment, after the step of obtaining information corresponding to the null value as abnormality verification information if determining that null value information exists in the device model, the system version information, the device UID information, and the fault image / video information, and sending the abnormality verification information to the user terminal, the method further includes:

[0050] Information filling prompt information and information filling associated question information corresponding to the abnormal verification information are obtained and sent to the user terminal, so as to obtain corresponding terminal fault additional information through the information filling prompt information and the information filling associated question information.

[0051] In this embodiment, considering that some users are unable to accurately fill in the missing information to be supplemented corresponding to the abnormal verification information due to professional issues, the customer service server can also automatically obtain information filling prompt information corresponding to the abnormal verification information (for example, including search prompt information on how to search for device model, system version information, and device UID information, and also including operation guidance information on how to take a screenshot of the application fault interface) and information filling related question information (for example, including related question information for obtaining information such as the time of occurrence of the fault and the duration of the fault), and the customer service server sends the information filling prompt information and information filling related question information to the user terminal.

[0052] After receiving and viewing the prompts and associated questions for filling in the information through the user terminal, the user can promptly edit and submit the corresponding additional information regarding the terminal fault and send it to the customer service server. Upon receiving the additional information, the customer service server can merge and update it with the terminal fault information previously uploaded by the user terminal to obtain complete terminal fault information.

[0053] S120: If it is determined that the terminal fault information passes the integrity verification, generate log pulling configuration information based on the terminal fault information, and send the log pulling configuration information to the user terminal.

[0054] In this embodiment, if it is determined in the customer service server of the server that the terminal fault information passes the integrity verification, log pulling configuration information is generated based on the terminal fault information. By sending the log pulling configuration information to the user terminal for automatic configuration and automatic execution, the log related to the terminal fault information can be automatically obtained, rather than pulling and uploading all the log information related to the application, thereby effectively reducing the amount of log data obtained and realizing the operational difficulty of log acquisition.

[0055] In one embodiment, if Figure 3 As shown, step S120 includes:

[0056] S121. Perform image recognition on the fault image / video information in the terminal fault information based on a pre-trained image recognition model to obtain target fault description information corresponding to the terminal fault information;

[0057] S122. Obtain a target log pull type corresponding to the target fault description information;

[0058] S123. Generate the log pulling configuration information according to the target log pulling type, the device model, the system version information, and the device UID information.

[0059] In this embodiment, if a pre-trained image recognition model (such as a convolutional neural network, a Yolo V3 target detection model, a Yolo V5 target detection model, etc.) is deployed in the customer service server to perform image recognition on the fault image / video information in the terminal fault information, the information therein is extracted as the target fault description information. Moreover, the target log pull type corresponding to the target fault description information can also be obtained in the customer service server. For example, when a first preset keyword (such as a freeze) exists in the target fault description information, the corresponding target log pull type is obtained. After determining the target log pull type, the device model, the system version information, and the device UID information, they are filled into a preset log pull configuration information template that includes a log automatic pull instruction, and the log pull configuration information can be quickly generated.

[0060] S130: Receive log reporting data corresponding to the log pulling configuration information sent by the user terminal.

[0061] In this embodiment, after receiving the log reporting data sent by the user terminal and corresponding to the log pulling configuration information, the customer service server may further analyze the data to obtain troubleshooting related data.

[0062] S140: Based on a pre-trained log classification model, obtain a target classification result corresponding to the log reporting data based on the log classification model.

[0063] In this embodiment, since a pre-trained log classification model can be deployed in the customer service server, the log classification model can be used to extract key information and classify the log reporting data uploaded by the user terminal, thereby performing specific classification to obtain the target classification result.

[0064] In one embodiment, if Figure 4 As shown, step S140 includes:

[0065] S141, extracting core data features of the log reporting data;

[0066] S142: Input the core data features into the log classification model to obtain the target classification result.

[0067] In this embodiment, since the log reporting data can be uploaded in JSON format (which is JavaScript Object Notation and is also an open standard file format and data exchange format), which includes information such as network type, battery status, disk space, disk usage space, remaining memory space, whether it is debug mode, device screen size, device brand, original log, etc., the core data extraction model based on the log classification model (such as the convolution layer) can first extract the core data features of the log reporting data, and then input the core data features into the log classification model (specifically, such as inputting them into the pooling layer and the fully connected layer in sequence) to obtain the target classification results. The target classification results obtained can enable the customer service server to quickly determine the relevant troubleshooting strategies.

[0068] S150. Obtain a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and send the target troubleshooting strategy to the user terminal, so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

[0069] In this embodiment, a troubleshooting strategy is pre-deployed on the customer service server or backend server. Once the target classification results are obtained, the server can quickly extract the appropriate target troubleshooting strategy from the multiple troubleshooting sub-strategies included in the troubleshooting strategy and send it to the user terminal, allowing for timely troubleshooting. Once the target troubleshooting strategy is sent to the user terminal, it can be considered an automatically executed script that automatically troubleshoots the current fault of the user terminal.

[0070] In one embodiment, if Figure 5 As shown, step S150 includes:

[0071] S151. Acquire multiple troubleshooting sub-strategies included in the troubleshooting strategy, and a strategy label corresponding to each troubleshooting sub-strategy;

[0072] S152: Obtain similarity between the target classification result and the strategy label of each obstacle elimination sub-strategy, and determine the strategy label with the greatest similarity to the target classification result as the target strategy label;

[0073] S153: Obtain the troubleshooting sub-strategy corresponding to the target strategy tag as the target troubleshooting strategy.

[0074] In this embodiment, since the troubleshooting strategy has multiple troubleshooting sub-strategies preset, and a number of policy tags (such as application freeze, application crash, etc.) are set for each troubleshooting sub-strategy, after obtaining the target classification result, the similarity between it and the policy tag of each troubleshooting sub-strategy can be calculated (specifically, the cosine similarity between the word vector of the target classification result and the word vector of the policy tag is calculated), thereby determining the policy tag with the greatest similarity to the target classification result as the target policy tag. After determining the target tag, the troubleshooting sub-strategy corresponding to the target policy tag can be selected from the multiple troubleshooting sub-strategies included in the troubleshooting strategy as the target troubleshooting strategy, and then sent to the user terminal for automatic execution to troubleshoot the problem, so that the application can be used normally after the problem is troubleshooted.

[0075] In one embodiment, after step S150, the method further includes:

[0076] If a log periodic analysis instruction is detected, the stored historical log data set is obtained;

[0077] Obtaining a fault type set included in the historical log data set, obtaining a cumulative number of occurrences for each fault type in the fault type set, and arranging the cumulative number of occurrences of the fault type set according to a preset arrangement to generate a sorted fault type set;

[0078] Obtaining a matching result between the troubleshooting strategy and the sorted fault type set, so as to determine whether each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy;

[0079] If it is determined that the sorted fault type set does not successfully match the troubleshooting strategy, then obtaining the to-be-processed fault types for which no corresponding troubleshooting sub-strategy exists in the troubleshooting strategy to form a to-be-processed fault type set;

[0080] Generate a troubleshooting sub-strategy corresponding to each of the to-be-processed fault types in the to-be-processed fault type set based on a pre-trained generation model, and send the sub-strategy to a receiving terminal for review;

[0081] If an audit confirmation instruction is received from the receiving terminal and corresponds to the troubleshooting sub-strategy corresponding to each pending fault type in the set of pending fault types, the troubleshooting sub-strategy corresponding to each pending fault type in the set of pending fault types is added to the troubleshooting strategy to update the troubleshooting strategy.

[0082] In this embodiment, the background server in the server can also regularly update and maintain the troubleshooting strategy. Specifically, each time the background server detects a log periodic analysis instruction (such as the generation cycle of the log periodic analysis instruction is 1 day, 7 days, 15 days, 1 month, etc., which is customized based on user needs), it obtains the stored historical log data set, wherein the data arrangement order in the historical log data set can be arranged in the order of the log upload time. The background server can also obtain all the fault types included in the historical log data set based on the log classification model and form a fault type set. It can also arrange the cumulative number of occurrences of the fault type set according to a preset arrangement method to generate a sorted fault type set, such as the preset arrangement method is a descending arrangement method, wherein the greater the cumulative number of occurrences of the fault type in the fault type set, the higher its ranking. In the obtained sorted fault type set, the topK fault types (where K can be set to a positive integer value such as 3, 5, 10 according to actual needs) can be obtained, as well as all the remaining fault types.

[0083] Afterwards, it can also be determined whether each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy, thereby completing the matching operation between the troubleshooting strategy and the sorted fault type set, and obtaining a matching result. Only when each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy can the two be considered to be matched successfully; as long as there is at least one fault type in the sorted fault type set that does not have a corresponding troubleshooting sub-strategy in the troubleshooting strategy, it is considered that the two have failed to match. At this time, the fault types to be processed that do not have a corresponding troubleshooting sub-strategy in the troubleshooting strategy can be obtained to form a set of fault types to be processed. In the background server, a pre-trained generation model (such as a text generation model, more specifically a generative adversarial network, etc.) can also be pre-deployed to generate a troubleshooting sub-strategy corresponding to each fault type to be processed in the set of fault types to be processed, and send it to the receiving terminal for review. The receiving terminal user is a developer. After reviewing and approving the troubleshooting sub-strategies corresponding to each pending fault type in the set of pending fault types, the developer generates a corresponding review confirmation instruction and adds the corresponding troubleshooting sub-strategies to the troubleshooting strategy, thereby updating the troubleshooting strategy. This process enables regular and timely updates to the troubleshooting strategy, enabling it to support automatic troubleshooting of application usage faults on more user terminals.

[0084] It can be seen that the embodiment of implementing this method can timely obtain the terminal fault information of the user terminal and automatically generate log pulling configuration information, and after obtaining the corresponding log reporting data, quickly determine the fault type and corresponding troubleshooting strategy, and send the troubleshooting strategy to timely troubleshoot.

[0085] Figure 6 This is a schematic block diagram of an application log intelligent processing device provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above-mentioned application log intelligent processing method, the present invention further provides an application log intelligent processing device 100. The application log intelligent processing device 100 includes: a fault information acquisition unit 110, a log pull configuration information generation unit 120, a log reporting data receiving unit 130, a log classification unit 140, and a target troubleshooting strategy acquisition unit 150.

[0086] The fault information acquiring unit 110 is configured to respond to a program fault reporting instruction from a user terminal and acquire terminal fault information corresponding to the program fault reporting instruction.

[0087] In this embodiment, the technical solution is described with the server as the execution subject. The server can be regarded as a server cluster and at least includes a customer service server (whose main users are customer service personnel) and a backend server (whose main users are developers). When a user encounters a fault in an application on a user terminal, if the fault affects the subsequent normal use of the application and needs to be promptly troubleshooted, the user can manually click the troubleshooting virtual button on the user interaction interface to generate a program fault reporting instruction, or the user terminal can automatically generate a program fault reporting instruction when it detects that the application has a running fault in the background. When the program fault reporting instruction is sent to the customer service server in the server, a terminal fault information acquisition instruction is automatically generated by the customer service server and sent to the user terminal. After that, the user terminal automatically obtains the corresponding terminal fault information according to the terminal fault information acquisition instruction and feeds it back to the customer service server. After receiving the terminal fault information, the customer service server can perform timely analysis and processing to assist the user in quickly troubleshooting. Among them, the terminal fault information acquisition instruction is executed on the user terminal to obtain the device model, system version information, device UID information (the full name of UID is User Identification, which means user identity verification) and fault image / video information, and when the fault image / video information is obtained, a pop-up window can be displayed to prompt the user to record or take a screenshot of the fault interface of the application.

[0088] In one embodiment, the application log intelligent processing device 100 further includes:

[0089] A terminal fault information parsing unit is used to obtain the device model, system version information, device UID information and fault image / video information included in the terminal fault information;

[0090] A first verification unit is configured to determine that the terminal fault information passes integrity verification if it is determined that the device model, the system version information, the device UID information, and the fault image / video information are all non-null value information;

[0091] The second verification unit is used to obtain the information corresponding to the null value as abnormality verification information if it is determined that there is null value information in the device model, the system version information, the device UID information, and the fault image / video information, and send the abnormality verification information to the user terminal.

[0092] In this embodiment, to enhance the data security of terminal fault information, the terminal fault information in plaintext may be encrypted on the user terminal to update the terminal fault information, and then the ciphertext terminal fault information is sent to the customer service server. The customer service server decrypts the ciphertext terminal fault information to obtain the plaintext terminal fault information, parses the terminal fault information, obtains the device model, system version information, device UID information, and fault image / video information included therein, and then performs an integrity check on the information. If it is determined that the device model, system version information, device UID information, and fault image / video information are all non-null values, the terminal fault information is determined to have passed the integrity check, and the customer service server can then perform corresponding troubleshooting based on the terminal fault information. If it is determined that any of the device model, system version information, device UID information, and fault image / video information contain null values, the corresponding null value information is obtained as exception verification information and sent to the user terminal to prompt the user of the user terminal to manually fill in the missing information corresponding to the exception verification information in a timely manner, thereby completing the terminal fault information.

[0093] In one embodiment, the application log intelligent processing device 100 further includes:

[0094] The terminal fault additional information acquisition unit is used to acquire information filling prompt information and information filling associated question information corresponding to the abnormal verification information, and send them to the user terminal, so as to acquire the corresponding terminal fault additional information through the information filling prompt information and the information filling associated question information.

[0095] In this embodiment, considering that some users are unable to accurately fill in the missing information to be supplemented corresponding to the abnormal verification information due to professional issues, the customer service server can also automatically obtain information filling prompt information corresponding to the abnormal verification information (for example, including search prompt information on how to search for device model, system version information, and device UID information, and also including operation guidance information on how to take a screenshot of the application fault interface) and information filling related question information (for example, including related question information for obtaining information such as the time of occurrence of the fault and the duration of the fault), and the customer service server sends the information filling prompt information and information filling related question information to the user terminal.

[0096] After receiving and viewing the prompts and associated questions for filling in the information through the user terminal, the user can promptly edit and submit the corresponding additional information regarding the terminal fault and send it to the customer service server. Upon receiving the additional information, the customer service server can merge and update it with the terminal fault information previously uploaded by the user terminal to obtain complete terminal fault information.

[0097] The log pulling configuration information generating unit 120 is configured to generate log pulling configuration information based on the terminal fault information if it is determined that the terminal fault information passes the integrity verification, and send the log pulling configuration information to the user terminal.

[0098] In this embodiment, if it is determined in the customer service server of the server that the terminal fault information passes the integrity verification, log pulling configuration information is generated based on the terminal fault information. By sending the log pulling configuration information to the user terminal for automatic configuration and automatic execution, the log related to the terminal fault information can be automatically obtained, rather than pulling and uploading all the log information related to the application, thereby effectively reducing the amount of log data obtained and realizing the operational difficulty of log acquisition.

[0099] In one embodiment, the log pulling configuration information generating unit 120 is used to:

[0100] Performing image recognition on the fault image / video information in the terminal fault information based on a pre-trained image recognition model to obtain target fault description information corresponding to the terminal fault information;

[0101] Obtaining a target log pull type corresponding to the target fault description information;

[0102] The log pulling configuration information is generated according to the target log pulling type, the device model, the system version information, and the device UID information.

[0103] In this embodiment, if a pre-trained image recognition model (such as a convolutional neural network, a Yolo V3 target detection model, a Yolo V5 target detection model, etc.) is deployed in the customer service server to perform image recognition on the fault image / video information in the terminal fault information, the information therein is extracted as the target fault description information. Moreover, the target log pull type corresponding to the target fault description information can also be obtained in the customer service server. For example, when a first preset keyword (such as a freeze) exists in the target fault description information, the corresponding target log pull type is obtained. After determining the target log pull type, the device model, the system version information, and the device UID information, they are filled into a preset log pull configuration information template that includes a log automatic pull instruction, and the log pull configuration information can be quickly generated.

[0104] The log reporting data receiving unit 130 is configured to receive the log reporting data corresponding to the log pulling configuration information sent by the user terminal.

[0105] In this embodiment, after receiving the log reporting data sent by the user terminal and corresponding to the log pulling configuration information, the customer service server may further analyze the data to obtain troubleshooting related data.

[0106] The log classification unit 140 is configured to obtain a target classification result corresponding to the log reporting data based on a pre-trained log classification model.

[0107] In this embodiment, since a pre-trained log classification model can be deployed in the customer service server, the log classification model can be used to extract key information and classify the log reporting data uploaded by the user terminal, thereby performing specific classification to obtain the target classification result.

[0108] In one embodiment, the log classification unit 140 is configured to:

[0109] Extracting core data features of the log reporting data;

[0110] The core data features are input into the log classification model to obtain the target classification result.

[0111] In this embodiment, since the log reporting data can be uploaded in JSON format (which is JavaScript Object Notation and is also an open standard file format and data exchange format), which includes information such as network type, battery status, disk space, disk usage space, remaining memory space, whether it is debug mode, device screen size, device brand, original log, etc., the core data extraction model based on the log classification model (such as the convolution layer) can first extract the core data features of the log reporting data, and then input the core data features into the log classification model (specifically, such as inputting them into the pooling layer and the fully connected layer in sequence) to obtain the target classification results. The target classification results obtained can enable the customer service server to quickly determine the relevant troubleshooting strategies.

[0112] The target troubleshooting strategy acquisition unit 150 is used to acquire a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and send the target troubleshooting strategy to the user terminal so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

[0113] In this embodiment, a troubleshooting strategy is pre-deployed on the customer service server or backend server. Once the target classification results are obtained, the server can quickly extract the appropriate target troubleshooting strategy from the multiple troubleshooting sub-strategies included in the troubleshooting strategy and send it to the user terminal, allowing for timely troubleshooting. Once the target troubleshooting strategy is sent to the user terminal, it can be considered an automatically executed script that automatically troubleshoots the current fault of the user terminal.

[0114] In one embodiment, the target troubleshooting strategy acquisition unit 150 is configured to:

[0115] Obtain multiple troubleshooting sub-strategies included in the troubleshooting strategy, and a strategy label corresponding to each troubleshooting sub-strategy;

[0116] Obtaining the similarity between the target classification result and the strategy label of each obstacle elimination sub-strategy, and determining the strategy label with the greatest similarity to the target classification result as the target strategy label;

[0117] Obtain the troubleshooting sub-strategy corresponding to the target strategy tag as the target troubleshooting strategy.

[0118] In this embodiment, since the troubleshooting strategy has multiple troubleshooting sub-strategies preset, and a number of policy tags (such as application freeze, application crash, etc.) are set for each troubleshooting sub-strategy, after obtaining the target classification result, the similarity between it and the policy tag of each troubleshooting sub-strategy can be calculated (specifically, the cosine similarity between the word vector of the target classification result and the word vector of the policy tag is calculated), thereby determining the policy tag with the greatest similarity to the target classification result as the target policy tag. After determining the target tag, the troubleshooting sub-strategy corresponding to the target policy tag can be selected from the multiple troubleshooting sub-strategies included in the troubleshooting strategy as the target troubleshooting strategy, and then sent to the user terminal for automatic execution to troubleshoot the problem, so that the application can be used normally after the problem is troubleshooted.

[0119] In one embodiment, the application log intelligent processing device 100 further includes:

[0120] A log periodic analysis unit, configured to obtain a stored historical log data set if a log periodic analysis instruction is detected;

[0121] a fault type sorting unit, configured to obtain a fault type set included in the historical log data set, obtain a cumulative number of occurrences for each fault type in the fault type set, and arrange the cumulative number of occurrences of the fault type set in a preset arrangement to generate a sorted fault type set;

[0122] a troubleshooting type matching unit, configured to obtain a matching result between the troubleshooting strategy and the sorted fault type set, so as to determine whether each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy;

[0123] a fault type screening unit configured to, if it is determined that the sorted fault type set does not successfully match the troubleshooting strategy, obtain to-be-processed fault types for which no corresponding troubleshooting sub-strategy exists in the troubleshooting strategy, to form a to-be-processed fault type set;

[0124] a troubleshooting sub-strategy generating unit, configured to generate, based on a pre-trained generation model, a troubleshooting sub-strategy corresponding to each of the to-be-processed fault types in the to-be-processed fault type set, and send the generated sub-strategy to a receiving terminal for review;

[0125] An audit update unit is configured to add the troubleshooting sub-strategy corresponding to each of the pending fault types in the set of pending fault types to the troubleshooting strategy upon receiving an audit confirmation instruction sent by the receiving terminal and corresponding to the troubleshooting sub-strategy corresponding to each of the pending fault types in the set of pending fault types, so as to update the troubleshooting strategy.

[0126] In this embodiment, the background server in the server can also regularly update and maintain the troubleshooting strategy. Specifically, each time the background server detects a log periodic analysis instruction (such as the generation cycle of the log periodic analysis instruction is 1 day, 7 days, 15 days, 1 month, etc., which is customized based on user needs), it obtains the stored historical log data set, wherein the data arrangement order in the historical log data set can be arranged in the order of the log upload time. The background server can also obtain all the fault types included in the historical log data set based on the log classification model and form a fault type set. It can also arrange the cumulative number of occurrences of the fault type set according to a preset arrangement method to generate a sorted fault type set, such as the preset arrangement method is a descending arrangement method, wherein the greater the cumulative number of occurrences of the fault type in the fault type set, the higher its ranking. In the obtained sorted fault type set, the topK fault types (where K can be set to a positive integer value such as 3, 5, 10 according to actual needs) can be obtained, as well as all the remaining fault types.

[0127] Afterwards, it can also be determined whether each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy, thereby completing the matching operation between the troubleshooting strategy and the sorted fault type set, and obtaining a matching result. Only when each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy can the two be considered to be matched successfully; as long as there is at least one fault type in the sorted fault type set that does not have a corresponding troubleshooting sub-strategy in the troubleshooting strategy, it is considered that the two have failed to match. At this time, the fault types to be processed that do not have a corresponding troubleshooting sub-strategy in the troubleshooting strategy can be obtained to form a set of fault types to be processed. In the background server, a pre-trained generation model (such as a text generation model, more specifically a generative adversarial network, etc.) can also be pre-deployed to generate a troubleshooting sub-strategy corresponding to each fault type to be processed in the set of fault types to be processed, and send it to the receiving terminal for review. The receiving terminal user is a developer. After reviewing and approving the troubleshooting sub-strategies corresponding to each pending fault type in the set of pending fault types, the developer generates a corresponding review confirmation instruction and adds the corresponding troubleshooting sub-strategies to the troubleshooting strategy, thereby updating the troubleshooting strategy. This process enables regular and timely updates to the troubleshooting strategy, enabling it to support automatic troubleshooting of application usage faults on more user terminals.

[0128] It can be seen that the implementation of the embodiment of the device can timely obtain the terminal fault information of the user terminal and automatically generate log pulling configuration information, and after obtaining the corresponding log reporting data, quickly determine the fault type and corresponding troubleshooting strategy, and send the troubleshooting strategy to timely troubleshoot.

[0129] The above-mentioned application log intelligent processing device can be implemented in the form of a computer program. The computer program can be used in Figure 7 Runs on the computer equipment shown.

[0130] See also Figure 7 , Figure 7 This is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device integrates any application log intelligent processing device provided by an embodiment of the present invention.

[0131] See Figure 7 The computer device 400 includes a processor 402 , a memory, and a network interface 405 connected via a system bus 401 , wherein the memory may include a storage medium 403 and an internal memory 404 .

[0132] The storage medium 403 can store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions, which, when executed, can enable the processor 402 to execute the above-mentioned application log intelligent processing method.

[0133] The processor 402 is used to provide computing and control capabilities to support the operation of the entire computer device.

[0134] The internal memory 404 provides an environment for the operation of the computer program 4032 in the storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the above-mentioned application log intelligent processing method.

[0135] The network interface 405 is used to communicate with other devices through the network. Figure 7 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0136] The processor 402 is configured to run a computer program 4032 stored in a memory to implement the aforementioned application log intelligent processing method.

[0137] It should be understood that in the embodiment of the present invention, the processor 402 may be a central processing unit (CPU), and the processor 402 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0138] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0139] Therefore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the above-mentioned application log intelligent processing method.

[0140] The computer-readable storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0142] In the several embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the various units is merely a logical functional division, and actual implementation may employ other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0143] The steps in the methods of the embodiments of the present invention may be adjusted in order, combined, or deleted as needed. The units in the devices of the embodiments of the present invention may be combined, divided, or deleted as needed. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0144] If this integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention.

[0145] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for intelligent processing of application logs, characterized in that: include: In response to a program fault reporting instruction from a user terminal, obtaining terminal fault information corresponding to the program fault reporting instruction; If it is determined that the terminal fault information passes the integrity verification, generating log pulling configuration information based on the terminal fault information, and sending the log pulling configuration information to the user terminal; Receiving log reporting data corresponding to the log pulling configuration information sent by the user terminal; Based on a pre-trained log classification model, obtaining a target classification result corresponding to the log reporting data based on the log classification model; as well as A target troubleshooting strategy corresponding to the target classification result is obtained based on a preset troubleshooting strategy, and the target troubleshooting strategy is sent to the user terminal, so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

2. The method according to claim 1, characterized in that After obtaining terminal fault information corresponding to the program fault reporting instruction in response to the program fault reporting instruction of the user terminal, the method further includes: Obtain the device model, system version information, device UID information, and fault image / video information included in the terminal fault information; If it is determined that the device model, the system version information, the device UID information, and the fault image / video information are all non-null value information, then it is determined that the terminal fault information passes the integrity verification; If it is determined that there is null value information in the device model, the system version information, the device UID information, and the fault image / video information, the information corresponding to the null value is obtained as abnormality verification information, and the abnormality verification information is sent to the user terminal.

3. The method according to claim 2, characterized in that The generating log pulling configuration information based on the terminal fault information includes: Performing image recognition on the fault image / video information in the terminal fault information based on a pre-trained image recognition model to obtain target fault description information corresponding to the terminal fault information; Obtaining a target log pull type corresponding to the target fault description information; The log pulling configuration information is generated according to the target log pulling type, the device model, the system version information, and the device UID information.

4. The method according to claim 2, characterized in that After the step of, if it is determined that null value information exists in the device model, the system version information, the device UID information, and the fault image / video information, obtaining information corresponding to the null value as abnormality verification information, and sending the abnormality verification information to the user terminal, the method further includes: Information filling prompt information and information filling associated question information corresponding to the abnormal verification information are obtained and sent to the user terminal, so as to obtain corresponding terminal fault additional information through the information filling prompt information and the information filling associated question information.

5. The method according to claim 1, wherein The obtaining, based on the log classification model, a target classification result corresponding to the log reporting data includes: Extracting core data features of the log reporting data; The core data features are input into the log classification model to obtain the target classification result.

6. The method according to claim 1, wherein The obtaining of a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy includes: Obtain multiple troubleshooting sub-strategies included in the troubleshooting strategy, and a strategy label corresponding to each troubleshooting sub-strategy; Obtaining the similarity between the target classification result and the strategy label of each obstacle elimination sub-strategy, and determining the strategy label with the greatest similarity to the target classification result as the target strategy label; Obtain the troubleshooting sub-strategy corresponding to the target strategy tag as the target troubleshooting strategy.

7. The method according to claim 1, characterized in that After the step of acquiring a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy and sending the target troubleshooting strategy to the user terminal, the method further includes: If a log periodic analysis instruction is detected, the stored historical log data set is obtained; Obtaining a fault type set included in the historical log data set, obtaining a cumulative number of occurrences for each fault type in the fault type set, and arranging the cumulative number of occurrences of the fault type set according to a preset arrangement to generate a sorted fault type set; Obtaining a matching result between the troubleshooting strategy and the sorted fault type set, so as to determine whether each fault type in the sorted fault type set has a corresponding troubleshooting sub-strategy in the troubleshooting strategy; If it is determined that the sorted fault type set does not successfully match the troubleshooting strategy, then obtaining the to-be-processed fault types for which no corresponding troubleshooting sub-strategy exists in the troubleshooting strategy to form a to-be-processed fault type set; Generate a troubleshooting sub-strategy corresponding to each of the to-be-processed fault types in the to-be-processed fault type set based on a pre-trained generation model, and send the sub-strategy to a receiving terminal for review; If an audit confirmation instruction is received from the receiving terminal and corresponds to the troubleshooting sub-strategy corresponding to each pending fault type in the set of pending fault types, the troubleshooting sub-strategy corresponding to each pending fault type in the set of pending fault types is added to the troubleshooting strategy to update the troubleshooting strategy.

8. An intelligent processing device for application logs, characterized in that: include: a fault information acquiring unit, configured to respond to a program fault reporting instruction from a user terminal and acquire terminal fault information corresponding to the program fault reporting instruction; a log pulling configuration information generating unit, configured to generate log pulling configuration information based on the terminal fault information if it is determined that the terminal fault information passes the integrity verification, and send the log pulling configuration information to the user terminal; A log reporting data receiving unit, configured to receive log reporting data corresponding to the log pulling configuration information sent by the user terminal; A log classification unit, configured to obtain, based on a pre-trained log classification model, a target classification result corresponding to the log reporting data; as well as A target troubleshooting strategy acquisition unit is used to acquire a target troubleshooting strategy corresponding to the target classification result based on a preset troubleshooting strategy, and send the target troubleshooting strategy to the user terminal so that the target troubleshooting strategy is automatically executed on the user terminal and the fault is automatically troubleshooted and eliminated.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the application log intelligent processing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions and can implement the application log intelligent processing method according to any one of claims 1 to 7 when executed by a processor.

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