CN113781068A

CN113781068A - Online problem solving method and device, electronic equipment and storage medium

Info

Publication number: CN113781068A
Application number: CN202111059047.1A
Authority: CN
Inventors: 夏杰
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2021-09-09
Filing date: 2021-09-09
Publication date: 2021-12-10
Anticipated expiration: 2041-09-09
Also published as: CN113781068B

Abstract

The application relates to the technical field of artificial intelligence, and particularly discloses an online problem solving method, an online problem solving device, electronic equipment and a storage medium, wherein the online problem solving method comprises the following steps: acquiring first data according to error reporting information of online problems, wherein the first data is used for identifying data of operation of corresponding online personnel on a system after the system reports errors; according to the data type of the first data, performing feature extraction on the first data to acquire key feature information of the first data; matching a solution according to the key characteristic information; displaying the solution to a user and receiving feedback information of the user; matching at least one script combination in a preset script library to be a solution script according to the feedback information and the solution scheme; running the resolution script to resolve the online problem.

Description

Online problem solving method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to an online problem solving method, an online problem solving device, electronic equipment and a storage medium.

Background

At present, when a production problem occurs on line, the production problem is often reported to a corresponding responsible person after being discovered by an on-line service person, and then the responsible person further communicates with the service person, for example, the responsible person inquires about the reason of the occurrence of the problem of the on-line service person, the operation of the on-line service person after the occurrence of the problem, and the like. The question processing can be performed only after the relevant data is acquired through inquiry. In this processing mode, some problem data are difficult to obtain, for example, an online service person often cannot describe the condition of the system and the processing procedure of the online service person in detail and comprehensively, which results in low processing efficiency. Meanwhile, when a problem occurs, the report processing is only performed after the online service personnel actively find the problem, so that the processing timeliness is low. In addition, each time when a problem report is received, a responsible person is required to analyze from the beginning and reformulate a solution, so that the requirement on the responsible person is high, and the labor cost is high.

Disclosure of Invention

In order to solve the above problems in the prior art, the embodiments of the present application provide an online problem solving method, an online problem solving device, an electronic device, and a storage medium, which can report an online problem in time, and provide a corresponding solution, thereby improving timeliness and efficiency of online problem processing, reducing dependence on manpower, and further reducing labor cost.

In a first aspect, an embodiment of the present application provides an online problem solving method, including:

acquiring first data according to error reporting information of online problems, wherein the first data is used for identifying data of operation of corresponding online personnel on the system after the system reports errors;

according to the data type of the first data, performing feature extraction on the first data to acquire key feature information of the first data;

matching a solution according to the key characteristic information;

showing the solution to the user and receiving feedback information of the user;

matching at least one script combination in a preset script library to be a solution script according to the feedback information and the solution;

a resolution script is run to resolve the online problem.

In a second aspect, an embodiment of the present application provides an online problem solving apparatus, including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first data according to error reporting information of online problems, and the first data is used for identifying data of operation of corresponding online personnel on the system after the system reports errors;

the identification module is used for extracting the characteristics of the first data according to the data type of the first data to acquire key characteristic information of the first data;

the matching module is used for matching the solution according to the key characteristic information;

the display module is used for displaying the solution to the user and receiving feedback information of the user;

and the solution module is used for matching at least one script combination in a preset script library to be a solution script according to the feedback information and the solution scheme, and running the solution script to solve the online problem.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor coupled to the memory, the memory for storing a computer program, the processor for executing the computer program stored in the memory to cause the electronic device to perform the method of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, the computer program causing a computer to perform the method according to the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program, the computer operable to cause the computer to perform a method according to the first aspect.

The implementation of the embodiment of the application has the following beneficial effects:

in the embodiment of the application, when the online system has a problem, the corresponding online personnel after the error is reported by the identification system is automatically acquired to perform AI identification on the first data of the operation of the system, and then the corresponding key characteristic information is obtained. Then, similar solved problems are matched through comparison of the key characteristic information, and then the solved problems are used as the reference scheme of the error and displayed to the user. And finally, acquiring corresponding script combination to form a solution script by combining the feedback information of the user and the solution scheme, and running the solution script to solve the problem of the online system. Therefore, when the online system is in error, the operation information can be automatically reported, and the operation information of online personnel after the online system is in error is recorded. Meanwhile, the acquired data can be automatically analyzed, and then the corresponding solution automatically matched according to the analysis result is displayed to the system responsible person, so that the system responsible person only needs to confirm whether the solution is normal or not, if the solution is normal, the system responsible person can directly operate without compiling the solution from 0, the professional requirements on the responsible person are reduced, the labor cost is further reduced, and meanwhile, the automatic solution of the online problem is realized.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a schematic hardware structure diagram of an online problem solving apparatus according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of an online problem solving method provided in an embodiment of the present application;

fig. 3 is a flowchart illustrating a method for acquiring first data according to error information of an online problem according to an embodiment of the present application;

fig. 4 is a schematic flowchart of a method for extracting features of first data according to a data type of the first data to obtain key feature information of the first data according to an embodiment of the present application;

fig. 5 is a schematic flowchart of a method for matching each screenshot with a historical error screenshot in a historical error reporting database according to error reporting information, and calculating a similarity between each screenshot and the corresponding historical error screenshot according to the error reporting information;

fig. 6 is a schematic flowchart of a method for calculating a similarity between a first screenshot and a corresponding first historical error screenshot according to an embodiment of the present disclosure;

fig. 7 is a schematic diagram of a numbered image obtained by numbering segmented images according to an embodiment of the present disclosure;

fig. 8 is a block diagram illustrating functional modules of an online problem solving apparatus according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application.

The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

First, referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of an online problem solving device according to an embodiment of the present application. The on-line problem solving apparatus 100 includes at least one processor 101, a communication line 102, a memory 103, and at least one communication interface 104.

In this embodiment, the processor 101 may be a general processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more ics for controlling the execution of programs according to the present disclosure.

The communication link 102, which may include a path, carries information between the aforementioned components.

The communication interface 104 may be any transceiver or other device (e.g., an antenna, etc.) for communicating with other devices or communication networks, such as an ethernet, RAN, Wireless Local Area Network (WLAN), etc.

The memory 103 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In this embodiment, the memory 103 may be independent and connected to the processor 101 through the communication line 102. The memory 103 may also be integrated with the processor 101. The memory 103 provided in the embodiments of the present application may generally have a nonvolatile property. The memory 103 is used for storing computer-executable instructions for executing the scheme of the application, and is controlled by the processor 101 to execute. The processor 101 is configured to execute computer-executable instructions stored in the memory 103, thereby implementing the methods provided in the embodiments of the present application described below.

In alternative embodiments, computer-executable instructions may also be referred to as application code, which is not specifically limited in this application.

In alternative embodiments, processor 101 may include one or more CPUs, such as CPU0 and CPU1 of FIG. 1.

In an alternative embodiment, the online problem solving apparatus 100 may include a plurality of processors, such as the processor 101 and the processor 107 in fig. 1. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

In an alternative embodiment, if the online problem solving apparatus 100 is a server, for example, the apparatus may be an independent server, or may be a cloud server that provides basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, web service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and artificial intelligence platform, and the like. The online problem solving apparatus 100 may further include an output device 105 and an input device 106. The output device 105 is in communication with the processor 101 and may display information in a variety of ways. For example, the output device 105 may be a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display device, a Cathode Ray Tube (CRT) display device, a projector (projector), or the like. The input device 106 is in communication with the processor 101 and may receive user input in a variety of ways. For example, the input device 106 may be a mouse, a keyboard, a touch screen device, or a sensing device, among others.

The online problem solving apparatus 100 may be a general-purpose device or a special-purpose device. The embodiment of the present application does not limit the type of the on-line problem solving apparatus 100.

Next, it should be noted that the embodiments disclosed in the present application may acquire and process related data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Hereinafter, the on-line problem solving method disclosed in the present application will be explained:

referring to fig. 2, fig. 2 is a schematic flow chart of a method for solving an online problem according to an embodiment of the present disclosure. The on-line problem solving method comprises the following steps:

201: and acquiring first data according to the error reporting information of the online problem.

Generally speaking, when the system reports an error, a corresponding representation form is generated. For example: and requesting a background url, and if the

return

404 and 302 or the return value contains Exception keywords such as Exception, indicating that the request is in error. Thus, in the present embodiment, the system can monitor whether the return information includes error abnormality information, and if the corresponding error abnormality information is monitored, the system can automatically generate error information to report an error.

In an alternative embodiment, the error reporting information may also be triggered by the form of an error reported by a system user. Specifically, some error systems may not be aware of it, and therefore, system error reporting cannot be achieved. For example: the page allows the user to fill in a form of information, such as an "email address". At this time, the user is filled with data in a mail format, but the page directly prompts the user for "mail format error". For such errors, the system cannot sense autonomously, and thus does not report errors proactively. At this time, the user can trigger the error reporting process through an active reporting form, and then generate the error reporting information. In particular, the system may maintain an error window, and when a user encounters a problem, the user may submit the encountered system problem by accessing the error window.

Meanwhile, in this embodiment, the first data is used to identify the operation of the system by the corresponding on-line personnel after the system error is reported. Illustratively, the first data may include: operational data, log data, and underlying data. The operation data can be a group of screenshots, and is a collection instruction triggered by error reporting information after an error is reported by the system, and collected operation behaviors of online personnel on the system after the error is reported by the system.

Specifically, in the present embodiment, a method for acquiring first data according to error information of an online problem is provided, as shown in fig. 3, the method specifically includes:

301: and determining at least one display device corresponding to the system with the error according to the error reporting information.

In this embodiment, the display device is used to display the operation status of the corresponding system to the online personnel, assist the online personnel to interact with the corresponding system, and display the feedback of the operation of the corresponding system to the online personnel.

302: and sending a collection instruction to each display device in the at least one display device so that each display device carries out screen capture operation according to the collection instruction to obtain at least one screenshot as operation data.

After the system reports an error, the online personnel can process the error-reported system at the first time. Typically, these processes will display feedback in the form of a visual interface via a corresponding display. Therefore, in the embodiment, whether error abnormal information occurs in the system can be monitored, if the error abnormal information occurs, the automatic screen capture operation of the corresponding display is immediately triggered, and the page information and the action information of the online personnel operating the system after the error is reported are recorded.

In an alternative embodiment, the operation data may be a video on a screen. Specifically, when error abnormal information occurs in the system, the automatic screen recording operation of the corresponding display is immediately triggered, and page information and action information of the system operated by an online person within a period of time after an error is reported are recorded.

303: and determining the occurrence time of the error reporting information, and acquiring a system log of the system with the error in the first time period according to the occurrence time as log data.

In this embodiment, the first period of time may be determined by the occurrence time. Illustratively, all system logs within 3 minutes before the time of error entry may be intercepted.

304: and analyzing the log data to obtain at least one keyword.

In this embodiment, a log type of log data may be first obtained, and then at least one candidate word included in the log data may be determined according to the log type. Specifically, different types of log data contain different information, such as: operating system logs are commonly used to record information about hardware, software and system problems in a system, while events occurring in the system can be monitored; application logs are often used to record the running information of corresponding applications in the system. Based on the above, for different log types, it can be determined in advance what information names corresponding to information possibly related to system errors exist in the log of the type, and the information corresponding to the information names is the key information in the log data. Therefore, when the log is analyzed, the corresponding information name can be quickly extracted as a candidate word according to the log type.

Then, a degree of association between each candidate word and the error information may be determined. At least one keyword is then determined among the at least one candidate word. Specifically, the relevance degree corresponding to each keyword in the screened at least one keyword is greater than a threshold value.

305: and capturing the bottom layer operation information of the system according to at least one keyword, and taking the captured operation information as bottom layer data.

In this embodiment, the keywords in the log data may be: table names, column names, etc. Based on the method, according to the database configuration corresponding to the service operated in the error reporting system, some operation information of the bottom layer can be automatically captured as bottom layer data through the acquired keywords. Illustratively, after the acquired log data is automatically analyzed, a table name, a primary key and the like related to error information can be obtained, then, an sql statement is automatically generated according to the primary key, and then, all column data of the table is inquired in a corresponding database.

Specifically, sql may be: select from table name where person id is the main bond. Therefore, the corresponding result can be found by executing the sql. In the above-mentioned sql, the data found in different tables are different, and simply speaking, several columns of data can be found if a table defines several columns.

For example: the t _ user table defines 5 columns of id, name, email, addr, and age, and the searched data includes the data in the 5 columns, which is as follows:

id:1001

name:zhangwuji

email:skycloud@aa.com

addr, Shanghai region

age:20

202: and according to the data type of the first data, performing feature extraction on the first data to acquire key feature information of the first data.

As can be seen from the above example, the first data includes three data types, namely, operational data, log data, and underlying data. In the present embodiment, the analysis method adopted for the first data of different data types is also different. Specifically, the present application provides a method for extracting features of first data according to a data type of the first data to obtain key feature information of the first data, as shown in fig. 4, the method includes:

401: and for each screenshot in the at least one screenshot, respectively carrying out character recognition on each screenshot to obtain at least one characteristic index as a first key characteristic index of the first data.

In this embodiment, the at least one characteristic indicator corresponds to at least one screenshot one to one. Specifically, for the text recognition result of each screenshot, the key information features contained therein may be extracted, for example: and (3) returning values such as '404', '302', Exception and the like of the request address in the http format as characteristic indexes corresponding to the screenshot.

402: and respectively matching each screenshot with the historical error screenshots in the historical error reporting database according to error reporting information, and calculating the similarity between each screenshot and the corresponding historical error screenshot to obtain at least one similarity serving as a second key characteristic index of the first data.

In this embodiment, the at least one similarity corresponds to at least one screenshot one-to-one.

Meanwhile, the present application provides a method for matching each screenshot with a historical error screenshot in a historical error reporting database according to error reporting information, and calculating a similarity between each screenshot and the corresponding historical error screenshot, as shown in fig. 5, the method includes:

501: and screening out historical error reporting information which is the same as the error events of the error reporting information from a historical error reporting database according to the error reporting information.

Specifically, the error event corresponding to the historical error reporting information may be a historical error event that is historically the same as the error event reported by the current error reporting information.

502: and acquiring at least one historical error screenshot corresponding to the historical error reporting information.

In the present embodiment, since the error events are handled in the same manner, when an error event corresponding to the history error report information occurs, a corresponding capture instruction is triggered, and the same number of screen shots are captured at the same frequency. In short, the at least one historical error screenshot is the same number as the at least one screenshot.

503: and acquiring a first historical error screenshot corresponding to the first screenshot from at least one historical error screenshot.

In this embodiment, the first screenshot is any one of the at least one screenshot, and the order of the first historical error screenshot in the at least one historical error screenshot is the same as the order of the first screenshot in the at least one screenshot.

Specifically, the number of the at least one historical error screenshot is the same as the number of the at least one screenshot, and the at least one historical error screenshot and the at least one screenshot are arranged in the front-back order of time. Thus, the same sequence of shots will have the same time interval between the time of the shot and the time at which the error event occurred. In short, the same sequence of shots are taken after the same time interval after the time of the error event.

For example, in the process of screenshot, 200 pictures are shot by a rule of cutting every 2 s. The interception time of the subsequent 20 th screenshot is the 38 th time after the error event occurs. Therefore, the same order means that the screen shots are relatively uniform, and therefore, in the present embodiment, the screen shots having the same order are associated with each other. Specifically, the order 1 screenshot corresponds to the order 1 historical error screenshot, the order 2 screenshot corresponds to the order 2 historical error screenshot, and so on.

504: and calculating the similarity between the first screenshot and the corresponding first historical error screenshot.

In this embodiment, a method for calculating a similarity between a first screenshot and a corresponding first historical error screenshot is provided, and as shown in fig. 6, the method includes:

601: the first cut is partitioned into at least one sub-graph.

In this embodiment, the first slice can be uniformly divided into 16 sub-graphs by 4 × 4. Of course, other division methods may be applied to the present application, and the present application is not limited thereto.

602: and for each sub-image in the at least one sub-image, determining the weight of each sub-image according to the position of each sub-image in the first screenshot to obtain at least one weight.

In this embodiment, at least one weight corresponds to at least one subgraph. Following the example of 16 subgraphs described above, each subgraph may be assigned a weight based on the distance of the center of each subgraph from the center of the first screenshot. That is, the closer the distance, the higher the weight, and the further the distance, the lower the weight.

In an alternative embodiment, the center of each sub-image and the center of the first screenshot may be the center of gravity determined from the outline of the corresponding image.

603: and dividing the first historical error screenshot corresponding to the first screenshot into at least one historical error subgraph according to the dividing mode of the first screenshot.

604: and acquiring a first history error sub-graph corresponding to the first sub-graph from at least one history error sub-graph.

In this embodiment, the first sub-graph is any one of the at least one sub-graph, and a position of the first historical error sub-graph in the first historical error screenshot is the same as a position of the first sub-graph in the first screenshot. In short, following the example of 16 sub-graphs, the divided sub-graphs are numbered from left to right and from top to bottom, respectively, resulting in the numbered images shown in fig. 7.

In this way, the 16 history error subgraphs of the first history error screenshot divided in the same format are also numbered. That is, the numbers corresponding to the subgraphs at the same position are the same. Therefore, the subgraphs with the same number and the history error subgraphs can be corresponded.

605: and respectively calculating first similarity between each first sub-graph and the corresponding first historical error sub-graph to obtain at least one first similarity.

In this embodiment, at least one first similarity corresponds to at least one sub-graph one to one.

606: and carrying out weighted summation on at least one first similarity according to at least one weight to obtain the similarity between the first screenshot and the corresponding first historical error screenshot.

403: and performing feature extraction on the log data to obtain at least one log feature as a third key feature index of the first data.

In this embodiment, the method for extracting the features of the log data to obtain at least one log feature is similar to the method for extracting the keywords in the log data in step 304, and is not described herein again.

404: and querying the database according to the bottom data to obtain at least one query result as a fourth key characteristic index of the first data.

In this embodiment, the method for querying the database according to the bottom layer data to obtain at least one query result is similar to the method for capturing the bottom layer operation information of the system in step 305, and is not described herein again.

405: and taking the first key characteristic index, the second key characteristic index, the third key characteristic index and the fourth key characteristic index as key characteristic information of the first data.

203: and matching the solutions according to the key characteristic information.

In this embodiment, at least one historical error event of the same type as the error of the error information may be screened out from the historical error database according to the error information. Then, for each historical error reporting event in the at least one historical error reporting event, a first historical key feature index, a second historical key feature index, a third historical key feature index and a fourth historical key feature index of each historical error reporting event are respectively obtained.

Based on this, a second similarity between the first key feature index and the first historical key feature index, a third similarity between the second key feature index and the second historical key feature index, a fourth similarity between the third key feature index and the third historical key feature index, and a fifth similarity between the fourth key feature index and the fourth historical key feature index are calculated.

Therefore, the historical error reporting event of which the second similarity, the third similarity, the fourth similarity and the fifth similarity are all larger than the first threshold value is taken as the target alarm event, so as to obtain the historical solution corresponding to the target alarm event. And finally, taking the historical solution as a matching solution.

204: and displaying the solution to the user and receiving feedback information of the user.

In this embodiment, the solution may be sent to the terminal device of the user to present the solution to the user. The user referred to herein may be a decision-maker of the system.

205: and matching at least one script combination in a preset script library to be a solution script according to the feedback information and the solution.

In this embodiment, if the user feedback information is correct, that is, the user sees the solution presented to the user and analyzes and confirms that the solution can be executed, it indicates that the solution can be applied to the on-line problem that occurs currently without modification and adjustment. In this case, each script corresponding to the solution may be directly called in a preset script library, and the scripts are combined according to the sequence of steps in the solution to obtain the solution script. Specifically, in the present embodiment, a series of execution steps arranged in a sequential execution order are described in the solution, and each execution step corresponds to one execution script. Based on the method, scripts corresponding to each execution step can be matched in a preset script library through the corresponding relation, and the matched scripts are combined according to the arrangement sequence of the corresponding execution steps in the solution to obtain the solution script.

Illustratively, for solution a, the execution scheme is recorded as: step 1 is performed first, then step 2, then step 3, and finally step 4. Meanwhile, the solution a records that the script corresponding to step 1 is script 5, the script corresponding to step 2 is script 5, the script corresponding to step 3 is script 1, and the script corresponding to step 4 is script 20. Based on the corresponding relation, the script 1, the script 5, the script 9 and the script 20 are extracted in a preset script library, and the corresponding steps are sequenced in the solution, namely: combining the sequences of the step 1, the step 2, the step 3 and the step 4 to obtain a script execution sequence as follows: script 5, script 9, and a combination script of script 1 and script 20 are solution scripts.

Likewise, in the present embodiment, if the feedback information is modified, that is, the user is viewing the solution presented to the user, and analyzes and confirms that the solution is not enough to solve the current online problem and needs to be modified or adjusted accordingly. In this case, the feedback information further includes a modification suggestion, based on which, according to the modification suggestion in the feedback information, some steps in the solution can be replaced, and then a new solution is obtained. Specifically, the modification suggestion may include a location in the solution of the step that needs to be modified and a storage location of the script corresponding to the modified step.

Therefore, each corresponding script is called according to the new solution, and the scripts are combined according to the sequence of the steps in the new solution to obtain the solution script. The specific combination method is consistent with the combination method for which the feedback information is correct, and is not described herein again.

206: a resolution script is run to resolve the online problem.

In summary, in the online problem solving method provided by the present invention, when an online system has a problem, the first data of the operation of the online personnel on the system corresponding to the error reported by the identification system is automatically obtained to perform AI identification, and then the corresponding key feature information is obtained. Then, similar solved problems are matched through comparison of the key characteristic information, and then the solved problems are used as the reference scheme of the error and displayed to the user. And finally, acquiring corresponding script combination to form a solution script by combining the feedback information of the user and the solution scheme, and running the solution script to solve the problem of the online system. Therefore, when the online system is in error, the operation information can be automatically reported, and the operation information of online personnel after the online system is in error is recorded. Meanwhile, the acquired data can be automatically analyzed, and then the corresponding solution automatically matched according to the analysis result is displayed to the system responsible person, so that the system responsible person only needs to confirm whether the solution is normal or not, if the solution is normal, the system responsible person can directly operate without compiling the solution from 0, the professional requirements on the responsible person are reduced, the labor cost is further reduced, and meanwhile, the automatic solution of the online problem is realized.

Referring to fig. 8, fig. 8 is a block diagram illustrating functional modules of an online problem solving apparatus according to an embodiment of the present disclosure. As shown in fig. 8, the online problem solving apparatus 800 includes:

the acquisition module 801 is configured to acquire first data according to error reporting information of an online problem, where the first data is used to identify data of an operation on the system by an online person corresponding to the error reporting of the system;

the identification module 802 is configured to perform feature extraction on the first data according to a data type of the first data, and acquire key feature information of the first data;

a matching module 803, configured to match solutions according to the key feature information;

a display module 804, configured to display the solution to the user and receive feedback information of the user;

and a solving module 805, configured to match at least one script combination as a solving script in a preset script library according to the feedback information and the solution, and run the solving script to solve the online problem.

In an embodiment of the present invention, the first data may include: operation data, log data and bottom layer data;

based on this, in terms of acquiring the first data according to the error reporting information of the online problem, the acquisition module 801 is specifically configured to:

determining at least one display device corresponding to the system with the error according to the error reporting information;

sending a collection instruction to each display device in at least one display device so that each display device carries out screen capture operation according to the collection instruction to obtain at least one screenshot as operation data;

determining the occurrence time of error reporting information, and acquiring a system log of an error-occurring system in a first time period according to the occurrence time as log data, wherein the first time period is determined by the occurrence time;

analyzing the log data to obtain at least one keyword;

and capturing the bottom layer operation information of the system according to at least one keyword, and taking the captured operation information as bottom layer data.

In an embodiment of the present invention, in analyzing log data to obtain at least one keyword, the collecting module 801 is specifically configured to:

acquiring a log type of log data;

determining at least one candidate word contained in the log data according to the log type, wherein each candidate word in the at least one candidate word is an information name corresponding to the key information in the log data;

determining the association degree between each candidate word and error information;

and determining at least one keyword in the at least one candidate word, wherein the association degree corresponding to each keyword in the at least one keyword is greater than a threshold value.

In an embodiment of the present invention, in terms of extracting features of first data according to a data type of the first data and acquiring key feature information of the first data, the identifying module 802 is specifically configured to:

for each screenshot in the at least one screenshot, respectively carrying out character recognition on each screenshot to obtain at least one characteristic index as a first key characteristic index of the first data, wherein the at least one characteristic index corresponds to the at least one screenshot one by one;

matching each screenshot with historical error screenshots in a historical error reporting database respectively according to error reporting information, and calculating the similarity between each screenshot and the corresponding historical error screenshot to obtain at least one similarity serving as a second key characteristic index of the first data, wherein the at least one similarity corresponds to the at least one screenshot one by one;

performing feature extraction on the log data to obtain at least one log feature as a third key feature index of the first data;

querying a database according to the bottom data to obtain at least one query result as a fourth key characteristic index of the first data;

and taking the first key characteristic index, the second key characteristic index, the third key characteristic index and the fourth key characteristic index as key characteristic information of the first data.

In an embodiment of the present invention, in terms of matching each screenshot with a historical error screenshot in a historical error reporting database according to error reporting information, and calculating a similarity between each screenshot and a corresponding historical error screenshot, the identifying module 802 is specifically configured to:

according to the error reporting information, screening out historical error reporting information which is the same as the error events of the error reporting information from a historical error reporting database;

acquiring at least one historical error screenshot corresponding to historical error reporting information, wherein the number of the at least one historical error screenshot is the same as that of the at least one screenshot;

acquiring a first historical error screenshot corresponding to a first screenshot from at least one historical error screenshot, wherein the first screenshot is any one of the at least one screenshot, and the order of the first historical error screenshot in the at least one historical error screenshot is the same as the order of the first screenshot in the at least one screenshot;

and calculating the similarity between the first screenshot and the corresponding first historical error screenshot.

In an embodiment of the present invention, in terms of calculating a similarity between a first screenshot and a corresponding first historical error screenshot, the identifying module 802 is specifically configured to:

dividing the first cut into at least one sub-graph;

for each sub-graph in the at least one sub-graph, determining the weight of each sub-graph according to the position of each sub-graph in the first screenshot to obtain at least one weight, wherein the at least one weight is in one-to-one correspondence with the at least one sub-graph;

dividing a first historical error screenshot corresponding to the first screenshot into at least one historical error subgraph according to the dividing mode of the first screenshot;

acquiring a first historical error sub-graph corresponding to a first sub-graph from at least one historical error sub-graph, wherein the first sub-graph is any one of the at least one sub-graph, and the position of the first historical error sub-graph in a first historical error screenshot is the same as the position of the first sub-graph in the first screenshot;

respectively calculating first similarity between each first subgraph and the corresponding first historical error subgraph index to obtain at least one first similarity, wherein the at least one first similarity is in one-to-one correspondence with the at least one subgraph;

and carrying out weighted summation on at least one first similarity according to at least one weight to obtain the similarity between the first screenshot and the corresponding first historical error screenshot.

In an embodiment of the present invention, in terms of matching solutions according to the key feature information, the solving module 805 is specifically configured to:

screening out at least one historical error reporting event with the same error type as the error reporting information from a historical error reporting database according to the error reporting information;

for each historical error reporting event in at least one historical error reporting event, respectively acquiring a first historical key characteristic index, a second historical key characteristic index, a third historical key characteristic index and a fourth historical key characteristic index of each historical error reporting event;

calculating a second similarity between the first key characteristic index and the first historical key characteristic index;

calculating a third similarity between the second key characteristic index and the second historical key characteristic index;

calculating a fourth similarity between the third key characteristic index and the third history key characteristic index;

calculating a fifth similarity between the fourth key feature index and the fourth historical key feature index;

determining a target alarm event in at least one historical error reporting event according to the second similarity, the third similarity, the fourth similarity and the fifth similarity, wherein the second similarity, the third similarity, the fourth similarity and the fifth similarity corresponding to the target alarm event are all larger than a first threshold;

acquiring a historical solution corresponding to the target alarm event;

and taking the historical solution as a matching solution.

Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 9, the electronic device 900 includes a transceiver 901, a processor 902, and a memory 903. Connected to each other by a bus 904. The memory 903 is used to store computer programs and data, and may transfer the data stored in the memory 903 to the processor 902.

The processor 902 is configured to read the computer program in the memory 903 to perform the following operations:

matching a solution according to the key characteristic information;

a resolution script is run to resolve the online problem.

based on this, in terms of acquiring the first data according to the error reporting information of the online problem, the processor 902 is specifically configured to perform the following operations:

analyzing the log data to obtain at least one keyword;

In an embodiment of the present invention, in analyzing the log data to obtain at least one keyword, the processor 902 is specifically configured to perform the following operations:

acquiring a log type of log data;

In an embodiment of the present invention, in terms of performing feature extraction on the first data according to a data type of the first data to obtain key feature information of the first data, the processor 902 is specifically configured to perform the following operations:

In an embodiment of the present invention, in terms of matching each screenshot with a historical error screenshot in a historical error reporting database according to error reporting information, and calculating a similarity between each screenshot and a corresponding historical error screenshot, the processor 902 is specifically configured to perform the following operations:

In an embodiment of the present invention, in terms of calculating a similarity between a first screenshot and a corresponding first historical error screenshot, the processor 902 is specifically configured to perform the following operations:

dividing the first cut into at least one sub-graph;

In an embodiment of the present invention, in terms of matching solutions according to the key feature information, the processor 902 is specifically configured to perform the following operations:

acquiring a historical solution corresponding to the target alarm event;

and taking the historical solution as a matching solution.

It should be understood that the online problem solving device in the present application may include a smart Phone (e.g., an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a Mobile Internet device MID (MID), a robot, a wearable device, etc. The above-mentioned on-line problem solving device is only an example, not an exhaustive list, and includes but is not limited to the above-mentioned on-line problem solving device. In practical applications, the above on-line problem solving apparatus may further include: intelligent vehicle-mounted terminal, computer equipment and the like.

Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented by combining software and a hardware platform. With this understanding in mind, all or part of the technical solutions of the present invention that contribute to the background can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments or some parts of the embodiments.

Accordingly, the present application also provides a computer readable storage medium storing a computer program, which is executed by a processor to implement part or all of the steps of any one of the online problem solving methods as described in the above method embodiments. For example, the storage medium may include a hard disk, a floppy disk, an optical disk, a magnetic tape, a magnetic disk, a flash memory, and the like.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the online problem solving methods as described in the above method embodiments.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are all alternative embodiments and that the acts and modules referred to are not necessarily required by the application.

In the above embodiments, the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference may be made to the description of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is merely a logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.

The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, and the memory may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the methods and their core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An on-line problem solving method, characterized in that the method comprises:

acquiring first data according to error reporting information of online problems, wherein the first data is used for identifying data of operation of corresponding online personnel on a system after the system reports errors;

matching a solution according to the key characteristic information;

displaying the solution to a user and receiving feedback information of the user;

matching at least one script combination in a preset script library to be a solution script according to the feedback information and the solution scheme;

running the resolution script to resolve the online problem.

2. The method of claim 1,

the first data includes: operation data, log data and bottom layer data;

the acquiring of the first data according to the error reporting information of the online problem includes:

sending a collection instruction to each display device in the at least one display device so that each display device performs screen capture operation according to the collection instruction to obtain at least one screenshot as the operation data;

determining the occurrence time of the error reporting information, and acquiring a system log of the error-occurring system in a first time period according to the occurrence time as the log data, wherein the first time period is determined by the occurrence time;

analyzing the log data to obtain at least one keyword;

and capturing the bottom layer operation information of the system according to the at least one keyword, and taking the captured operation information as the bottom layer data.

3. The method of claim 2, wherein analyzing the log data for at least one keyword comprises:

acquiring the log type of the log data;

determining at least one candidate word contained in the log data according to the log type, wherein each candidate word in the at least one candidate word is an information name corresponding to key information in the log data;

determining the association degree between each candidate word and the error information;

and determining the at least one keyword in the at least one candidate word, wherein the association degree corresponding to each keyword in the at least one keyword is greater than a threshold value.

4. The method according to claim 2, wherein the extracting features of the first data according to the data type of the first data to obtain key feature information of the first data comprises:

for each screenshot, respectively performing character recognition on the screenshot to obtain at least one characteristic index as a first key characteristic index of the first data, wherein the at least one characteristic index is in one-to-one correspondence with the at least one screenshot;

matching each screenshot with historical error screenshots in a historical error reporting database respectively according to the error reporting information, and calculating the similarity between each screenshot and the corresponding historical error screenshot to obtain at least one similarity serving as a second key feature index of the first data, wherein the at least one similarity is in one-to-one correspondence with the at least one screenshot;

5. The method of claim 4, wherein the matching each screenshot with historical error screenshots in the historical error reporting database according to the error reporting information and calculating the similarity between each screenshot and the corresponding historical error screenshot respectively comprises:

according to the error reporting information, screening out historical error reporting information which is the same as the error events of the error reporting information from the historical error reporting database;

acquiring at least one historical error screenshot corresponding to the historical error reporting information, wherein the number of the at least one historical error screenshot is the same as that of the at least one screenshot;

acquiring a first historical error screenshot corresponding to a first screenshot from the at least one historical error screenshot, wherein the first screenshot is any one of the at least one screenshot, and the order of the first historical error screenshot in the at least one historical error screenshot is the same as the order of the first screenshot in the at least one screenshot;

6. The method of claim 5, wherein calculating the similarity between the first screenshot and the corresponding first historical erroneous screenshot comprises:

dividing the first cut into at least one sub-graph;

dividing the first historical error screenshot corresponding to the first screenshot into at least one historical error subgraph according to the dividing mode of the first screenshot;

obtaining a first historical error sub-graph corresponding to a first sub-graph from the at least one historical error sub-graph, wherein the first sub-graph is any one of the at least one sub-graph, and the position of the first historical error sub-graph in the first historical error screenshot is the same as the position of the first sub-graph in the first screenshot;

respectively calculating first similarity between each first subgraph and the corresponding first historical wrong subgraph index to obtain at least one first similarity, wherein the at least one first similarity is in one-to-one correspondence with the at least one subgraph;

and carrying out weighted summation on the at least one first similarity according to the at least one weight to obtain the similarity between the first screenshot and the corresponding first historical error screenshot.

7. The method according to any one of claims 1-6, wherein the matching a solution according to the key feature information comprises:

screening out at least one historical error reporting event with the same error type as the error reporting information from the historical error reporting database according to the error reporting information;

for each historical error reporting event in the at least one historical error reporting event, respectively acquiring a first historical key characteristic index, a second historical key characteristic index, a third historical key characteristic index and a fourth historical key characteristic index of each historical error reporting event;

calculating a second similarity between the first key feature indicator and the first historical key feature indicator;

calculating a third similarity between the second key feature indicator and the second historical key feature indicator;

calculating a fourth similarity between the third key characteristic indicator and the third history key characteristic indicator;

calculating a fifth similarity between the fourth key feature indicator and the fourth historical key feature indicator;

determining the target alarm event in the at least one historical error reporting event according to the second similarity, the third similarity, the fourth similarity and the fifth similarity, wherein the second similarity, the third similarity, the fourth similarity and the fifth similarity corresponding to the target alarm event are all larger than a first threshold;

acquiring a historical solution corresponding to the target alarm event;

and taking the historical solution as the matched solution.

8. An inline problem solving apparatus, the apparatus comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first data according to error reporting information of online problems, and the first data is used for identifying data of operation of the system by corresponding online personnel after the error reporting of the system;

the matching module is used for matching a solution according to the key characteristic information;

the display module is used for displaying the solution to a user and receiving feedback information of the user;

9. An electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the one or more programs including instructions for performing the steps in the method of any of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method according to any one of claims 1-7.